Google Cloud

What’s new with Google Cloud

Fri, 10 Apr 2026 16:00:00 +0000

Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more.

Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

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Apr 6 - Apr 10

Community TechTalk: Powering Retail Agents with ADK, UCP & Apigee X
Move beyond basic chatbots to secure, transactional AI experiences. Join our Community TechTalk on April 16 to learn how Apigee X and Gemini build a "Trust Layer" for AI shopping assistants using UCP standards. We’ll demonstrate how to block prompt injections with Model Armor and implement cost governance via token limits to secure the path from discovery to purchase.

Register for the TechTalk
Implement multimodal capabilities in your AI agents
Explore three new reference architectures for building sophisticated multi-agent AI systems that can process and analyze multimodal data. To analyze disparate multimodal data and produce a high-confidence classification, see Classify multimodal data. To create a fluid conversational AI that processes audio and video streams in real time, see Enable live bidirectional multimodal streaming. To consolidate fragmented multimodal data into a searchable knowledge graph, see Multimodal GraphRAG resource orchestration.
Automate SecOps workflows with an agentic AI system
To accelerate incident response and reduce manual toil for your security team, you need a system that can automate remediation playbooks. Our new reference architecture helps you build an AI agent that orchestrates complex triage and investigation workflows across disparate security tools, such as SIEM, CSPM, and EDR, from a single interface. See the full guide to orchestrate security operations workflows.

Mar 30 - Apr 3

ASEAN Webinar | April 30: Mastering Agentic Governance at Scale with GCP
As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud experts Shilpi Puri & Wely Lau for a webinar on April 30th at 11:00 AM SGT to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud.

RSVP here.

Mar 23 - Mar 27

Turn your API sprawl into an agent-ready catalog
As organizations scale, APIs often become scattered across multiple gateways, creating "blind spots" that hinder AI adoption. To solve this, we’ve introduced two new capabilities for Apigee API hub: a new integration with API Gateway to automatically centralize API metadata into a single control plane, and a specification boost add-on (now in public preview). This add-on uses AI to enhance your API documentation with the precise examples and error codes that AI agents need to function reliably.

Read the full blog post to get started.
Webinar | April 16: AI Command & Control
As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud expert Satyam Maloo for a webinar on April 16th at 11:00 AM IST to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud.

RSVP here.
Modernizing and Decoupling Event Ingestion with Apigee
In modern cloud-native architectures, decoupling producers from consumers is critical for building resilient systems. While Google Cloud Pub/Sub provides a scalable backbone, exposing it directly to external clients can introduce security and management overhead. This new guide explores how to leverage Apigee as an intelligent HTTP ingestion point. Learn how to handle security, mediation, and traffic control before messages reach your internal bus using the PublishMessage policy or Pub/Sub API.

Read the full guide.

Mar 16 - Mar 20

Gemini-powered Assistant in BigQuery Studio Gets Context-Aware Upgrades
The Gemini-powered assistant in BigQuery Studio has been transformed into a fully context-aware analytics partner, supporting your entire data lifecycle. The new capabilities include intelligent resource discovery, which uses Dataplex Universal Catalog search to find resources across projects and deep dive into metadata using natural language. You can now automate tasks, such as scheduling production-grade queries directly through the chat interface, and instantly troubleshoot long-running or failed jobs with root cause analysis and cost control auditing.

Explore the full range of what the assistant can do.

Mar 9 - Mar 13

Want to use Gemini to develop code and don't know where to start?
This article includes a couple of examples of developing code with Gemini prompts; it identified changes that were needed to be made to get the code working. The article also refers to other examples that are available on github.

Mar 2 - Mar 6

Introducing Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model. Built for high-volume developer workloads at scale, 3.1 Flash-Lite delivers high quality for its price and model tier. Gemini 3.1 Flash-Lite can tackle tasks at scale, like high-volume translation and content moderation, where cost is a priority. And it can also handle more complex workloads where more in-depth reasoning is needed, like generating user interfaces and dashboards, creating simulations or following instructions.

Starting today, 3.1 Flash-Lite is rolling out in preview to enterprises via Vertex AI and developers via the Gemini API in Google AI Studio.
TechTalk: Implementing Device Authorization Grant (RFC 8628) for Apigee
Learn how to authorize "headless" devices like Smart TVs or AI agents that lack keyboards and browsers. Join our Community TechTalk on March 19 (5PM CET / 12PM EDT) to go under the hood of Apigee X/Hybrid. We’ll cover the real-world mechanics of state management, polling, and human-in-the-loop security patterns for devices and autonomous agents.

Register for the TechTalk

Feb 23 - Feb 27

Pro-level image generation gets faster and more accessible with Nano Banana 2
Nano Banana 2 is our state-of-the-art image generation and editing model. It delivers Pro-level image generation and editing at the speed you expect from Flash — making the quality, reasoning, and world knowledge you loved about Nano Banana Pro more accessible. Learn more about the model here.

The Intelligent Path to Compliance: Transforming Regulatory QC with Google Cloud
Reducing "Refuse to File" (RTF) risks and submission cycle times is critical for life sciences leaders. Google Cloud’s Regulatory Submission Semantic QC Auditor leverages Gemini and RAG architecture to transform Quality Control from a manual burden into an active, intelligent workflow.

By automating semantic cross-referencing, narrative coherence checks, and dynamic guidance-based auditing, this solution ensures rigorous accuracy and auditability. Operating within a secure GxP-ready environment, it empowers teams to detect subtle inconsistencies and generate remediation plans without sacrificing data privacy.

Learn more.
Stop typing, start interacting! The Gemini Live Agent Challenge is here. Build immersive agents that can help you see, hear, and speak using Gemini and Google Cloud. Compete for your share of $80,000+ in prizes and a trip to Google Cloud Next '26!

Submissions are open from February 16, 2026 to March 16, 2026. Learn more and register at geminiliveagentchallenge.devpost.com

Feb 9 - Feb 13

Introducing Gemini 3.1 Pro on Google Cloud.
3.1 Pro is a noticeably smarter, more capable baseline for complex problem-solving. We’re shipping 3.1 Pro at scale, building upon our goal to help you transform your business for the agentic future. Learn more about the model’s capabilities here. Gemini 3.1 Pro is available starting today in preview in Vertex AI and Gemini Enterprise. Developers can access the model in preview via the Gemini API in Google AI Studio, Android Studio, Google Antigravity, and Gemini CLI.
Automate Storage Compatibility with GKE Dynamic Default Storage Classes
Managing storage across mixed-generation VM clusters in GKE just got easier. With the new Dynamic Default Storage Class, Google Kubernetes Engine automatically selects between Persistent Disk (PD) and Hyperdisk based on a node's specific hardware compatibility. This abstraction eliminates the need for complex scheduling rules and manual pairing, ensuring your volumes "just work" regardless of the underlying infrastructure. By defining both variants in a single class, you reduce operational overhead while maintaining peak performance and cost-efficiency across your entire cluster.

Explore automated disk type selection
Community TechTalk: AI-Powered Apigee Development with strofa.io
Join the Apigee community on February 26 for a deep dive into strofa.io. Guest speaker Denis Kalitviansky will demonstrate how this new AI-powered tool automates and orchestrates Apigee development, from local emulators to large-scale hybrid environments. Discover how to scale your API management and streamline team collaboration using the latest in AI-driven automation.

Register now to reserve your spot.

Jan 26 - Jan 30

Simplify API Governance with Native OpenAPI v3 Support
Eliminate integration debt and accelerate deployment velocity with the General Availability of OpenAPI v3 (OASv3) support for API Gateway and Cloud Endpoints. You no longer need to downgrade modern specifications to OASv2. Instead, you can now define API contracts and enforce critical policies—including telemetry, quotas, and security—using native Google-specific extensions directly within your OASv3 files. This update ensures your APIs are secure by design while remaining fully compatible with the modern developer ecosystem and Google Cloud’s AI services.

Get started with OpenAPI v3 on API Gateway and Cloud Endpoints.

Accelerate API Testing with the New Open Source API Tester
Start validating your APIs with API Tester, a simple, YAML-based Test Driven Development (TDD) framework. Designed for the Apigee community, this tool allows you to write human-readable tests, run them instantly via a web client or CLI, and perform deep unit testing on Apigee proxies. With native support for JSONPath assertions and Apigee shared flows, you can verify everything from payload data to internal variables like proxy.basepath without leaving your terminal.

Explore the API Tester guide and start testing your proxies today.
Secure Sensitive Data with Kubernetes Secrets in Apigee hybrid
Enhance security in Apigee hybrid by accessing Kubernetes Secrets directly within your API proxies. This hybrid-exclusive feature keeps sensitive credentials within your cluster boundary and prevents replication to the management plane. It supports strict separation of duties: operators manage secrets via kubectl, while developers reference them as secure flow variables—ideal for high-compliance and GitOps workflows.

Implement Kubernetes Secrets in your hybrid proxies.
See the Console in a Whole New Light: Dark Mode is Now Generally Available in Google Cloud
Elevate your cloud management workflow with Dark Mode, now generally available in the Google Cloud console. We have delivered a modern, cohesive, and accessible experience reimagined for maximum comfort and productivity—especially during extended working hours and low-light environments. Dark Mode can be enabled automatically based on your operating system's preference, or manually through the Settings -> Appearance menu.

Switch to Dark Mode today to enjoy a modern, comfortable, and productive environment!
Apigee X Networking: PSC or VPC Peering?
Deciding how to connect Apigee X? Watch this video to compare Private Service Connect and VPC Peering. We break down northbound and southbound routing, IP consumption, and how to reach targets on-prem or in the cloud. Learn to simplify your architecture and avoid common networking "gotchas" for a smoother deployment.

Watch the video.

Jan 19 - Jan 23

Bridge the Gap: Excel-to-API Conversion in Apigee Portals
Give your customers more ways to connect! This new article by Tyler Ayers explores how to extend the Apigee Integrated Portal to support direct Excel file uploads. By leveraging SheetJS and custom portal scripts, you can enable users to upload spreadsheets, preview data, and submit it directly to your APIs, all without writing a single line of integration code themselves. It’s a powerful way to simplify onboarding for those who aren't yet API-ready.

Learn how to build it.
Elevate your applications with Firestore’s new advanced query engine
We have fundamentally reimagined Firestore with pipeline operations for Enterprise edition. Experience a powerful new engine featuring over a hundred new query features, index-less queries, new index types, and observability tooling to improve query performance. Seamlessly migrate using built-in tools and leverage Firestore’s existing differentiated serverless foundation, virtually unlimited scale, and industry-leading SLA. Join a community of 600K developers to craft expressive applications that maximize the benefits of rich queryability, real-time listen queries, robust offline caching, and cutting-edge AI-assistive coding integrations.

Learn more about Firestore pipeline operations.

What Google Cloud announced in AI this month

Tue, 31 Mar 2026 16:00:00 +0000

Editor’s note: Want to keep up with the latest from Google Cloud? Check back here for a monthly recap of our latest updates, announcements, resources, events, learning opportunities, and more.

March was a busy month for our AI teams. We launched Gemini Embedding 2, rolled out a highly cost-effective Veo 3.1 Lite model, and officially welcomed the Wiz team to Google Cloud to help redefine security in the AI era.

Alongside these launches, we created comprehensive guides to help you get the most out of these models, from prompting formulas for Nano Banana 2, to practical advice for optimizing your TPU training. Here’s a quick look at the latest news and resources to help your team build what’s next.

Top hits:

Gemini Embedding 2: Our first natively multimodal embedding model: Gemini Embedding 2 is our first natively multimodal embedding model that maps text, images, video, audio and documents into a single embedding space, enabling multimodal retrieval and classification across different types of media — and it’s available now in public preview.
Build with Veo 3.1 Lite, our most cost-effective video generation model: This model empowers developers to build high-volume video applications, at less than 50% of the cost of Veo 3.1 Fast, but with the same speed. This rounds out the Veo 3.1 model family, giving developers flexibility based on needs. For Cloud customers, it’s now available on Vertex AI.

Here’s a fun bonus: Check out our ultimate prompting guide for Veo 3.1 to get started.

Welcoming Wiz to Google Cloud: Redefining security for the AI era: Google has completed its acquisition of Wiz, a leading cloud and AI security platform. The Wiz team will join Google Cloud, and we will retain the Wiz brand. With the addition of Wiz, we will provide customers with a comprehensive platform to secure their cloud and hybrid environments, as well as accelerate threat prevention, detection, and response.
Gemini 3.1 Flash Live: Making audio AI more natural and reliable: We’ve improved 3.1 Flash Live’s overall quality, making it more reliable for developers and enterprises to build voice-first agents that can complete complex tasks at scale. On ComplexFuncBench Audio, a benchmark that captures multi-step function calling with various constraints, it leads with a score of 90.8% compared to our previous model.

News you can use:

The ultimate Nano Banana prompting guide: This is a must-read for anyone working with Nano Banana. We spent weeks testing Nano Banana 2 and Nano Banana Pro against every use case we could imagine to test its limits. We put together this guide to share exactly what we learned and how you can get the best results. Here’s an example formula: [Reference images] + [Relationship instruction] + [New scenario]

A developer’s guide to training with Ironwood TPUs: In this guide, we hear from Lillian Yu, CPA, CA , Product Strategy and Operation, and Liat Berry, Product Manager, on five strategies within the JAX and MaxText ecosystems designed to help developers refine training efficiency and hit peak performance on Ironwood hardware.
How to build production-ready AI agents with Google-managed MCP servers: In this guide, we anchor on a specific example. Cityscape is a demo agent built with Google's Application Development Kit (ADK) that turns a simple text prompt — like "Generate a cityscape for Kyoto" — into a unique, AI-generated city image. Check out the guide to learn more.

Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.

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February

In February, we’re giving developers more reasoning power with Gemini 3.1 Pro and Claude 4.6, and faster creative scaling with Nano Banana 2. We’re also opening up new training programs and step-by-step guides to help you tackle the hardest parts of the AI lifecycle, from capacity planning to mounting defenses against AI-powered attacks.

Here’s a rundown of our latest news, tools, and resources to help you build what’s next.

Top hits

Pro-level image generation gets faster and more accessible with Nano Banana 2: To build creative that stands out, you need models that naturally integrate into your workflows and scale with ease. Check out our blog to see how this comes to life (and how customers are putting the model to work).

Introducing Gemini 3.1 Pro on Google Cloud: Gemini 3.1 Pro is a clear step forward in reasoning, designed to solve tougher problems, giving you the reasoning depth your business needs. Gemini 3.1 Pro is available starting today in preview in Vertex AI and Gemini Enterprise. Developers can access the model in preview via the Gemini API in Google AI Studio, Android Studio, Google Antigravity, and Gemini CLI.
Announcing Claude Opus 4.6 and Claude Sonnet 4.6 on Vertex AI: Now generally available on Vertex AI, explore our sample notebook to get started and visit our documentation for comprehensive pricing and regional availability details.
New AI threats report: Distillation, experimentation, and integration: John Hultquist, chief analyst, Google Threat Intelligence Group, details what security leaders should know from our newest AI threat report on experimentation, integration, and distillation attacks.

News you can use

A developer's guide to production-ready AI agents: To help developers work through these challenges, we've published a collection of guides covering the full agent lifecycle. These resources first appeared during Kaggle’s 5 days of AI Agents Intensive, and they’ve proven so popular and useful, we wanted to make sure a wider audience had access, as well.
Gemini Enterprise Agent Ready (GEAR) program now available: We opened the Gemini Enterprise Agent Ready (GEAR) learning program to everyone. As a new specialized pathway within the Google Developer Program, GEAR empowers developers and pros to build and deploy enterprise-grade agents with Google AI.
Your guide to Provisioned Throughput (PT) on Vertex AI: Check out this deep-dive blog designed to show you the resources available to you today on Vertex AI, and how you can get started capacity planning.
How AI can boost defenders, from defense in depth to the cyber kill chain (Q&A): We know that defenders are also developing powerful AI tools, but what’s still unknown is what it could mean for enterprise software ownership if companies have to constantly mount AI-directed defenses at AI-powered attacks?

Janurary

We used to have to learn the language of computers. In 2026, they’re learning ours.

We kicked off the year by exploring the future of agentic commerce, where AI agents navigate the web to find and buy products for us. Our leaders call this the "invisible shelf" — a world where commerce isn't tied to a specific website. To make this reality scalable, we announced the Universal Commerce Protocol (UCP), a shared language that allows agents and retailers to understand each other.

We brought that same fluency to our creative and technical tools:

Updates to Veo 3.1 allow creators to use simple inputs — like reference images — to generate precise, mobile-ready video.
Natural language queries: With Comments to SQL in BigQuery, we’re removing the language barrier to data. Engineers can now write queries by describing their intent in natural language, prioritizing the question over the code.

Let’s dive in.

Top hits

1. Gemini Enterprise for Customer Experience (CX): Specifically built for agentic retail, this platform transforms fragmented search, commerce and service touch points into one seamless journey — whether you need a shopping assistant, a support bot, agentic search or help with merchandising.

2. We announced Universal Commerce Protocol (UCP): A new open standard for agentic commerce that works across the entire shopping journey — from discovery and buying to post-purchase support. UCP establishes a common language for agents and systems to operate together across consumer surfaces, businesses and payment providers. So instead of requiring unique connections for every individual agent, UCP enables all agents to interact easily. UCP is built to work across verticals and is compatible with existing industry protocols like Agent2Agent (A2A), Agent Payments Protocol (AP2) and Model Context Protocol (MCP).

3. We updated Veo 3.1, including improvements to Ingredients to Video and Portrait mode: Veo is getting more expressive, with improvements that help you create more fun, creative, high-quality videos based on ingredient images, built directly for the mobile format. This includes:

Improvements to Veo 3.1 Ingredients to Video, our capability that lets you create videos based on reference images.
Native vertical outputs for Ingredients to Video (portrait mode) to power mobile-first, short-form video creation.
State-of-the-art upscaling to 1080p and 4K resolution 1 for high-fidelity production workflows.

These updates are launching in the Gemini app, YouTube, Flow, Google Vids, the Gemini API and Vertex AI.

4. Vibe querying with comments-to-SQL: Crafting complex SQL queries can be challenging. Often, engineers simply want to express their data needs in plain English directly within their SQL workflow. That’s why we’re introducing Comments to SQL in BigQuery. This feature makes writing queries using natural language – ‘vibe querying’ – a reality. Learn more in the blog.

News you can use

Mastering Gemini CLI: Your complete guide from installation to advanced use-cases: We’ve teamed up with DeepLearning.ai and are excited to announce a free course – Gemini CLI: Code & Create with an Open-Source Agent. This course isn’t just for developers; we dive into practical use cases for various tasks such as data analysis, content creation, and personalized learning.
How Google SREs use Gemini CLI to solve real-world outages: In this article, we’ll delve into real scenarios that Google SREs are solving today using Gemini 3 (our latest foundation model) and Gemini CLI—the go-to tool for bringing agentic capabilities to the terminal.
Getting started with Gemini 3: Deploy your first Gemini 3 app to Google Cloud Run: In this blog, we will show you how to vibe code your first app—which leverages the Gemini 3 Flash Preview model and deploy it as a publicly accessible URL on Google Cloud Run. Google AI Studio lets you go from idea to app quickly by using natural language to generate fully functional apps using the power of Gemini 3.
Practical guidance: Building with the Secure AI Framework (SAIF) on Google Cloud: We know that security and data privacy are the top concern for executives when evaluating AI providers, and security is the top use case for AI agents in a majority of industries. To help you build AI boldly and responsibly, here’s our guide to developing AI with the Secure AI Framework (SAIF) on Google Cloud.
The truths about AI hacking that every CISO needs to know (Q&A): How will AI boost threat actors? And what can chief information security officers do about it? Google’s Heather Adkins, vice-president, Security Engineering, explores how securing the enterprise is about to change.

What Google Cloud announced in AI this month - 2025

Wed, 31 Dec 2025 17:00:00 +0000

2025 in review – your month-by-month Google Cloud AI highlight reel

From the early foundation of Gemini 2.0 to the breakthrough reasoning capabilities of Gemini 3 and Nano Banana Pro, this year was defined by velocity, creativity, and bringing AI to where your teams already work.

Whether you were building your first autonomous agent, generating cinematic video with Veo 3.1, or deploying on our newest Ironwood TPUs, our focus has been on giving you the tools to move faster. Here is your month-by-month guide to the innovations that defined 2025.

Keeping pace with AI innovation is a challenge for every organization. Developers and decision-makers this year faced specific hurdles: moving from prototype to production, managing the costs of inference, and solving the "agent orchestration" puzzle. The updates we released from January to December were answers to these friction points, designed to help you build applications that are faster, smarter, and more cost-effective.

Let’s take a look.

Setting the foundation with models and security

We kicked off the year by expanding the Gemini family and reinforcing the security required for enterprise adoption. In February, we addressed the gap between demos and production performance by launching the Vertex AI RAG Engine. This managed service helped developers reduce hallucinations and build trustworthy applications using their own enterprise data.

By March, the model landscape grew significantly with the introduction of Gemini 2.5 and Gemma 3, our most capable open model to date. To ensure these powerful tools could be deployed safely, we also launched AI Protection, a set of capabilities to safeguard AI workloads across clouds, proving that security is not an afterthought—it's the foundation.

Agents, I/O, and the developer experience

Spring brought a major shift toward agentic workflows. At Google Cloud Next '25 in April, we solved a major interoperability challenge with the launch of the Agent2Agent (A2A) protocol. This open standard allowed AI agents to discover and collaborate with one another, complementing Anthropic’s Model Context Protocol (MCP).

Creativity took center stage at Google I/O in May. We unveiled Veo 3 for video, Imagen 4 for images, and Lyria 2 for music, giving creators unprecedented control. We also introduced Jules, an autonomous AI coding agent designed to help developers with complex tasks like bug fixing. By June, we brought Gemini closer to where developers live—the terminal. The launch of the Gemini CLI allowed developers to generate code and debug issues without leaving their command line.

Creativity goes bananas, 1,000 real-world use cases, and payments

The second half of the year focused on proving the value of AI through ROI and enabling agents to take real-world actions. In July, security researchers gained a new ally with Big Sleep, an agent developed with Google DeepMind that successfully identified zero-day vulnerabilities in the wild.

In August, we launched Nano Banana, which quickly went viral overnight because it makes it easy to blend multiple images into a single image, maintain character consistency for rich storytelling, make targeted transformations using natural language, and use Gemini's world knowledge to generate and edit images.

Finally, to help businesses move from "what if" to "how to," we published 1,001 real-world gen AI use cases in August, complete with 101 technical blueprints. We then closed the loop on AI commerce in September with the Agent Payments Protocol (AP2). Developed with leading payments companies, AP2 gave agents the ability to securely initiate transactions, turning them from digital assistants into active business participants.

Gemini Enterprise, breakthrough reasoning, and heavy-duty infrastructure

We ended the year with massive leaps in model intelligence and the silicon required to power it. In October, we deepened our partnership with NVIDIA and expanded our Axion processors, enabling Google production services to run simultaneously on x86 and Arm-based machines for better efficiency.

We also announced Gemini Enterprise, the new front door for AI in the workplace. You gain instant access to Google's cutting-edge Gemini models. Use the latest multimodal breakthrough of Google's best AI to solve your most complex business problems.

Finally, the year culminated in November with the arrival of Gemini 3, our most intelligent model featuring a 1501 Elo score on the LMArena Leaderboard. To power these advanced workloads, we announced the general availability of Ironwood TPUs and the Antigravity agentic development platform.

2025 set a new pace for innovation, but 2026 will be about what you build with it. If you haven't yet experienced our most capable model, now is the time to start.

November

The fastest way to transform your business is here. This November, we took major jumps in both software and silicon. On the model front, we brought Gemini 3 to builders and businesses, securing a breakthrough 1501 Elo score on the LMArena Leaderboard, and redefining what’s possible with agentic workflows. Plus, we built Nano Banana Pro using Gemini 3, which will make it even easier for businesses to bring their creativity to life.

On the infrastructure side, we announced the general availability of Ironwood TPUs to handle the most demanding workloads imaginable. Whether you’re looking for the world’s best model or the heavy-duty hardware to run it at scale, we’ve got you covered.

Top hits

Bringing Gemini 3 to Enterprise. We brought Gemini 3, our most intelligent model, to every developer and enterprise team. It’s the best model in the world for multimodal understanding, and our most powerful agentic and vibe-coding model yet. Plus, Gemini 3 Pro tops the LMArena Leaderboard with a breakthrough score of 1501 Elo. You can learn more about the model capabilities here.
Announcing Nano Banana Pro for every builder and business. Nano Banana Pro (Gemini 3 Pro Image), our state-of-the art image generation and editing model, is available in Vertex AI and Google Workspace, and coming soon to Gemini Enterprise.

Build with Google Antigravity, our new agentic development platform: Development is lifting off. Google Antigravity is a new agentic development platform designed to help you operate at a higher, task-oriented level.
Announcing Ironwood TPUs General Availability and new Axion VMs to power the age of inference: Ironwood, our seventh generation TPU, will be generally available in the coming weeks. Ironwood is purpose-built for the most demanding workloads: from large-scale model training and complex reinforcement learning (RL) to high-volume, low-latency AI inference and model serving.
More ways to build, scale, and govern AI agents with Vertex AI Agent Builder. We shared new capabilities across the entire agent lifecycle to help you build, scale, and govern AI agents.
Expanding support for AI developers on Hugging Face. For those building with AI, most are in it to change the world — not twiddle their thumbs. So when inspiration strikes, the last thing anyone wants is to spend hours waiting for the latest AI models to download to their development environment. So, we announced a deeper partnership between Hugging Face and Google Cloud that reduces Hugging Face model download times through Vertex AI and Google Kubernetes Engine, offers native support for TPUs on all open models sourced through Hugging Face, and more.
Google Named a Leader in the Gartner® Magic Quadrant™ for AI Application Development Platforms. We are proud to announce that Google has been recognized as a Leader in the inaugural 2025 Gartner Magic Quadrant for AI Application Development Platforms for our Ability to Execute and Completeness of Vision.

Standout customer success

Mattel’s game changer: How AI is turning customer feedback into real-time product updates. Read about how Mattel is deploying generative AI across their business, including marketing, forecasting, customer service, design, and e-commerce. It's all aimed at reimagining the way Mattel makes toys, and dreams, come true.

News you can use

5 things to try with Gemini 3 Pro in Gemini CLI: We've integrated Gemini 3 Pro directly into Gemini CLI to unlock a new level of performance and productivity in the terminal. This powerful combination delivers state-of-the-art reasoning for executing better commands, enhances support for complex engineering work through agentic coding, and enables smarter, more tailored workflows via advanced tool use.
From silicon to softmax: Inside the Ironwood AI stack: This blog details the core components of Google's AI software stack that are woven into Ironwood, demonstrating how this deep co-design unlocks performance, efficiency, and scale. We cover the JAX and PyTorch ecosystems, the XLA compiler, and the high-level frameworks that make this power accessible.
Why GKE & Gemini CLI are better together. Dive into how the Gemini CLI and Google Kubernetes Engine (GKE) are coming together with the new open-sourcing of the GKE Gemini CLI extension. This extension brings GKE directly into the Gemini CLI ecosystem, and can also be used as an MCP server with any other MCP client.

October

From announcing the new front door for enterprise AI and supercharged infrastructure, to powerful new tools for creators, this month’s updates are focused on making generative AI more powerful and accessible.

We kicked off the month by announcing Gemini Enterprise, our new agentic platform designed to bring the best of Google AI to every workflow. We also expanded our AI infrastructure, deepening our NVIDIA partnership with new A4X Max instances, and advanced our multi-arch capabilities with Axion processors.

At the same time, we continue to see incredible creative momentum. Since launching our popular Nano Banana model, consumers have created 13 billion images and 230 million videos. Now, enterprises can harness that same power by combining Gemini 2.5 Pro with our generative media models—Lyria, Chirp, Imagen, and Veo.

Here’s a rundown of top announcements, and helpful how-to guides to help you get started.

Top hits

Introducing Gemini Enterprise. To help all of our customers succeed with AI, we’re introducing Gemini Enterprise. It’s our advanced agentic platform that brings the best of Google AI to every employee, for every workflow. Sign up for a 30-day free trial, or contact sales.
Expanding our NVIDIA partnership: Now shipping A4X Max, Vertex AI Training, and more. We deepened our partnership with NVIDIA with a suite of new capabilities that strengthens our platform for the entire AI lifecycle:

New A4X Max instances powered by NVIDIA’s GB300 NVL72, purpose-built for multimodal AI reasoning tasks
Google Kubernetes Engine (GKE), now supporting Dynamic Resource Allocation Kubernetes Network Driver (DRANET), boosting bandwidth in distributed AI/ML workloads
GKE Inference Gateway, now integrating with NVIDIA NeMo Guardrails
Vertex AI Model Garden to feature NVIDIA Nemotron models
Vertex AI Training recipes on top of the NVIDIA NeMo Framework and NeMo-RL

Announcing new capabilities in Vertex AI Training for large-scale training: Our new managed training features, aimed at developers training with hundreds to thousands of AI accelerators, builds on the best of Google Cloud’s AI infrastructure offerings, including Cluster Director for a fully managed and resilient Slurm environment, and adds sophisticated management tools. This includes pre-built data science tooling and optimized recipes integrated with frameworks like NVIDIA NeMo for specialized, massive-scale model building.
Google Skills: Your new home for Google AI learning and more. A new platform, Google Skills, that will bring together nearly 3,000 courses and labs in one place — including content from across Google Cloud, Google DeepMind, Grow with Google and Google for Education. Sign up here

Standout thought leadership

At Google, the future is multiarch; AI and automation are helping us get there: Parthasarathy Ranganathan, VP, Engineering Fellow and Wolff Dobson, Developer Relations Engineer, put Axion processors to the test: running Google production services. Now that our clusters contain both x86 and Axion Arm-based machines, Google's production services are able to run tasks simultaneously on multiple instruction-set architectures (ISAs). Today, this means most binaries that compile for x86 now need to compile to both x86 and Arm at the same time — no small thing when you consider that the Google environment includes over 100,000 applications!

News you can use

The ultimate prompting guide for Veo 3.1: This guide is a framework for directing Veo 3.1, our latest model that marks a shift from simple generation to creative control. Veo 3.1 builds on Veo 3, with stronger prompt adherence and improved audiovisual quality when turning images into videos.
5 ad agencies used Gemini 2.5 Pro and gen media models to create an "impossible ad”: To us, generative media is a canvas to explore ideas that were previously constrained by time, budget, or the limits of conventional production. To test this, we briefed several top agencies to use Google's AI to create an “impossible” ad — a campaign that pushes the boundaries of what’s creatively and technically feasible.
Building scalable AI agents: Design patterns with Agent Engine on Google Cloud: This post details how to build, scale, and manage enterprise-grade agentic systems using Google Cloud AI products to enable SI Partners to offer these transformative solutions to enterprise clients.
Use Gemini CLI to deploy cost-effective LLM workloads on GKE: With Inference Quickstart, you can replace months of manual trial-and-error with out-of-the-box manifests and data-driven insights. Inference Quickstart integrates with the Gemini CLI through native Model Context Protocol (MCP) support to offer tailored recommendations for your LLM workload cost and performance needs.

September

This month, we're buzzing with the news of AP2, the Agent Payments Protocol, an open protocol developed with 60+ leading payments and technology companies. AP2 is a blueprint for interoperable, AI-native commerce, designed to work seamlessly as an extension of the Agent2Agent (A2A) protocol and Model Context Protocol (MCP).

We’ve also seen “bananas” momentum with our generative media models, especially through nano-banana, or Gemini 2.5 Flash Image. Let’s dive in!

Top hits

Building next-gen visuals with Gemini 2.5 Flash Image (aka nano-banana) on Vertex AI: We announced native image generation and editing in Gemini 2.5 Flash (aka nano-banana) to deliver higher-quality images and more powerful creative control. Gemini 2.5 Flash Image is State-of-the-Art (SOTA) for both generation and image editing. For creative use cases, this means you can create richer, more dynamic visuals and edit images until they’re just right.

Powering AI commerce with the new Agent Payments Protocol (AP2): We announced the Agent Payments Protocol (AP2), an open protocol developed with leading payments and technology companies to securely initiate and transact agent-led payments across platforms. The protocol can be used as an extension of the Agent2Agent (A2A) protocol and Model Context Protocol (MCP). In concert with industry rules and standards, it establishes a payment-agnostic framework for users, merchants, and payments providers to transact with confidence across all types of payment methods.

The ROI of AI: How agents are delivering for business: The AI story is evolving. According to our 2025 ROI of AI Report, for the 52% of executives who report their organizations are now deploying AI agents in production, this represents a fundamental shift in how business gets done.
AI agent security: How to protect digital sidekicks (and your business): The promise of AI agents is immense. Powerful software that can plan, make decisions, and accurately act on your behalf at work? Yes please. But what if your digital assistant goes rogue? Without robust, flexible security in place, an AI agent could leak sensitive company data, cause a system outage, and even expose your business to security threats.
Scaling high-performance inference cost-effectively: GKE Inference Gateway is generally available, and we are launching new capabilities that deliver even more value. This underscores our commitment to helping companies deliver more intelligence, with increased performance and optimized costs for both training and serving. To learn more and get started, visit our AI Hypercomputer site.

News you can use

Agent Factory Recap: Keith Ballinger on AI, The Future of Development, and Vibe Coding: In Episode #6 of the Agent Factory podcast, Vlad Kolesnikov joined Keith Ballinger, VP and General Manager at Google Cloud, for a deep dive into the transformative future of software development with AI. We explore how AI agents are reshaping the developer's role and boosting team productivity.

Announcing the new Practical Guide to Data Science on Google Cloud: We've designed our new guide for practitioners looking to use Google Cloud’s capabilities across BigQuery, Vertex AI, and Google Cloud Serverless for Apache Spark. We’ll show you how to use an AI-first approach in your data science workflows, use previously untapped unstructured and multimodal data, and achieve new levels of efficiency.
Automate app deployment and security analysis with new Gemini CLI extensions: We’re closing the gap between your terminal and the cloud with a first look at the future of Gemini CLI, delivered through two new extensions: security extension and Cloud Run extension:

1. 1. /security:analyze performs a comprehensive scan right in your local repository, with support for GitHub pull requests coming soon. This makes security a natural part of your development cycle.
  2. /deploy deploys your application to Cloud Run, our fully managed serverless platform, in just a few minutes.

Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud, read our weekly updates, The Overwhelmed Person’s Guide to Google Cloud.

August

This August, we’re closing the gap between inspiration and implementation. We published 101 real-world gen AI use cases with technical blueprints, the technical counterpart to our popular 601 real-world gen AI use cases from the world's leading organizations. Each blueprint provides the Google Cloud tech stack you might need to bring these ideas to life. So take a look, play around, and share what you build.

If you’re not sure which Google AI developer tool to use to get started, we provided a helpful guide to show you which tool to use, and when.

Let’s dive in!

Top hits

Building next-gen visuals with Gemini 2.5 Flash Image on Vertex AI: We announced native image generation and editing in Gemini 2.5 Flash to deliver higher-quality images and more powerful creative control. Gemini 2.5 Flash Image is State of the Art (SOTA) for both generation and image editing.
- How to get started: Developers and enterprises can access Gemini 2.5 Flash Image in preview today on Vertex AI, with built-in SynthID watermarking for responsible and transparent use.

How much energy does Google AI use? We did the math: Did you know the estimated per-prompt energy impact is equivalent to watching TV for less than nine seconds? Take a look at the technical paper to learn more.

How Wells Fargo is using Google Cloud AI to empower its workforce with agentic tools: Wells Fargo, an early adopter of Google Agentspace, is transforming how individuals and teams work, collaborate, and serve customers.
At the Google Cloud Security Summit 2025, we shared details around new capabilities designed to help you secure your AI initiatives, and to help you use AI to make your organization more secure.

News you can use

Here’s which Google AI developer tool to use for each situation: From Jules to Firebase Studio, we mapped out our developer tool landscape to help you choose the right product for your project. This is a must read for any developer who wants to get started with Google AI developer tools.

Run OpenAI’s new gpt-oss model at scale with Google Kubernetes Engine: This guide walks you through deploying the new gpt-oss model from OpenAI on GKE for scalable and efficient performance.
Build a real-time voice agent with Gemini and Google ADK: Learn how to use the Agent Development Kit (ADK) and Gemini to create sophisticated, real-time voice agents for your applications.
The latest in AI Hypercomputer: Catch up on the latest updates and advancements in our AI Hypercomputer architecture, designed to handle the most demanding AI workloads.

July

Do you know since its preview launch on Vertex AI in June, enterprise customers have already generated over 6 million videos?

This July, we focused on helping you build what's next, and do it with confidence. We announced Veo 3 and Veo 3 Fast on Vertex AI to bring your ideas to life. This is exciting, especially for marketers, who want to rapidly iterate on high-quality creative. Plus, we’ve pulled together over 25+ of our best guides – from model deployment to building gen AI apps – to help you find ways to get started.

Top hits

Veo 3 and Veo 3 Fast are now available for everyone on Vertex AI: Veo 3 Fast is a faster way to turn text to video, from narrated product demos to short films.
- How to get started: Go here to learn more about Veo 3 and Veo 3 Fast on Vertex AI, and try it on Vertex AI Media Studio.

Our Big Sleep agent makes a big leap: Developed by Google DeepMind and Google Project Zero, Big Sleep can help security researchers find zero-day (previously unknown) software security vulnerabilities. We believe this is the first time an AI agent has been used to directly foil efforts to exploit a vulnerability in the wild.
Announcing a complete developer toolkit for scaling A2A agents on Google Cloud: We announced version 0.3 of the A2A protocol, which brings a more stable interface to build against and is critical to accelerating enterprise adoption. This version introduces several key capabilities, including gRPC support, the ability to sign security cards, and extended client side support in the Python SDK, which provide more flexible use, better security and easier integration.
The global endpoint offers improved availability for Anthropic’s Claude on Vertex AI: Anthropic's Claude models on Vertex AI now have improved overall availability with the global endpoint for Claude models. Now generally available, the global endpoint unlocks the ability to dynamically route your requests to any region with available capacity supported by the Claude model you're using.
Our customers are building cool things: For our latest edition, we explore Box’s AI agents extracting insights with cross-platform agent integration; Schroders uses multiple connected agents to build a complex investment research system; Hypros’ IoT device can monitor patient distress in hospitals without individual monitoring; a Formula E exhibition of whether regenerative braking can power an EV supercar for an entire lap.

News you can use

Bookmark our 25+ generative AI how-to guides: We gathered over 25 how-to guides for enterprise generative AI, covering everything from faster model deployment and building multi-agent systems to fine-tuning, evaluation, and RAG.
Build a conversational analytics agent with BigQuery: We released a new, first-party toolset for BigQuery that works with Google's Agent Development Kit (ADK) and the open-source MCP Toolbox for Databases. Learn how to build AI agents that can securely and intelligently interact with your enterprise data.
Take an open model from discovery to endpoint on Vertex AI: This guide walks you through the process of selecting, fine-tuning, evaluating, and deploying an open model on Vertex AI. It covers everything from the Model Garden to a production-ready endpoint.
Enable Secure Boot for your AI workloads: Learn how to enable Secure Boot for your GPU-accelerated AI workloads on Google Cloud. Our Secure Boot capability can be opted into at no additional charge, and now there’s a new, easier way to set it up for your GPU-accelerated machines.

June

From the command line to cinematic screens, June was about making AI practical and powerful. Developers can now access Gemini right from their terminal with our new command-line interface, Gemini CLI, and creators can produce stunning, narrative-driven video that combines audio and visuals with Veo 3. Plus, we published several helpful guides and resources for you to bookmark and use as you start building.

Top hits: Gemini comes to everyone – from your command line to Vertex AI

In your terminal: Introducing Gemini CLI, an open-source AI agent that brings the power of Gemini directly into your terminal. It provides lightweight access to Gemini, giving you the most direct path from your prompt to our model. Check it out on GitHub.

How to use it: To use Gemini CLI free-of-charge, simply login with a personal Google account to get a free Gemini Code Assist license. That free license gets you access to Gemini 2.5 Pro and its massive 1 million token context window. If you’re a professional developer who needs to run multiple agents simultaneously, or if you prefer to use specific models, you can use a Google AI Studio or Vertex AI key for usage-based billing or get a Gemini Code Assist Standard or Enterprise license.

In Vertex AI: Gemini 2.5 Flash and 2.5 Pro now stable and generally available. Our most intelligent models for speed and advanced reasoning are production-ready providing organizations with the stability, reliability and scalability needed to confidently deploy the most advanced AI capabilities into mission-critical applications. Learn more.

The story unfolds: You dream it, Veo creates it

Any creative class will tell you the same axiom of truth: A great story shows rather than tells. We gave our text-to-video model, Veo 3, a simple, evocative prompt to show how it can turn a creative spark into a cinematic scene. Veo 3 is now available for all Google Cloud customers and partners in public preview on Vertex AI.

Why this matters: Veo 3 not only brings stunning visual quality, but now adds sound from background sounds to dialogue. Learn more.

The prompt: "A medium shot frames an old sailor, his knitted blue sailor hat casting a shadow over his eyes, a thick grey beard obscuring his chin. He holds his pipe in one hand, gesturing with it towards the churning, grey sea beyond the ship's railing. 'This ocean, it's a force, a wild, untamed might. And she commands your awe, with every breaking light'"

The result:

AI agents – they’re taking autonomous action, but we need to keep them secure

As AI moves from answering questions to taking action, securing these autonomous systems is paramount. This month, we're not only highlighting how to build powerful agents but also how to implement a security-first approach to their deployment.

Exec perspective: Earlier this month, Anton Chuvakin, security advisor for Google Cloud’s Office of the CISO, discussed a new Google report on securing AI agents, and the new security paradigm they demand. Here’s a snippet: With great power comes a great responsibility for agent developers. To help mitigate the potential risks posed by rogue agent actions, we should invest in a new field of study focused specifically on securing agent systems.

Keeping the conversation going: What are the unique security threats posed by agentic AI, and what can leaders do to start securing their workloads? To help answer that question, we sat down with Archana Ramamoorthy, Senior Director, Google Cloud Security, to ask her how businesses should protect their AI workloads by making security more collaborative. Here’s a snippet:

“Just as leaders are getting a handle on the security risks of generative AI, the ground is shifting again. The emergence of AI agents — which can execute tasks on your company's behalf — is demanding a new security paradigm: agent security. This new layer of autonomy can magnify blind spots in an organization's existing security posture…The bottom line is that we need to be prepared, and the whole organization should invest in keeping systems secure.”

News you can use

Build smarter, multimodal AI applications

Build a RAG-capable app using Vertex AI services. This is a new architecture guide that helps understand the role of Vertex AI and Vector Search in a generative AI app. It includes diagrams, design considerations, and much more.
Check out this digestible tutorial to create multimodal agents for complex tasks like object detection by using a new tutorial featuring Gemini, LangChain, and LangGraph. Matthew and May show you which decisions you need to make to combine Gemini, LangChain and LangGraph to build multimodal agents that can identify objects.
Bookmark this quick guide to fine-tune video inputs on Vertex AI. If your work involves content moderation, video captioning, and detailed event localization, this guide is for you.

Deploy your AI efficiently and at scale

How good is your AI? In this explainer, we dive into the new features of the Gen AI Evaluation Service, designed to help you scale your evaluations, evaluate your autorater, customize your autorater with rubrics and evaluate your agents in production. Check out this helpful rubric:

New recipe! Learn how to access Llama4 and DeepSeek models today on AI Hypercomputer. A quick but essential read.

Ground your AI in your enterprise data

Read this guide on MCP integrations with Google Cloud Databases. Now with Toolbox, any MCP-compatible AI assistant (including Claude Code, Cursor, Windsurf, Cline, and many more) can help you write application code that queries your database, designs a schema for a new application, refactors code when the data model changes, generates data for integration testing, explores the data in your database, etc.

Stay tuned

May

This month has been one of our biggest yet – from over 100 announcements at I/O, to announcing critical new partnerships such as Anthropic’s Claude Opus 4 and Claude Sonnet 4 on Vertex AI, to launching a first-of-its kind generative AI certification for non-technical learners.

Today, we’re taking you through the biggest news this month, helpful guides to get started, and some inspiration for your next project with the May edition of our new series, Cool stuff Google Cloud customers built.

Top announcements

Google I/O brought a fresh wave of tools from Google Cloud, all designed to help businesses and developers build what's next. These updates bring new ways for organizations to work with AI, code more easily, create media, and manage intelligent agents. Here are the highlights:

We introduced new generative AI models for media, including Veo 3 for video, Imagen 4 for images, and Lyria 2 for music on Vertex AI. These models give you excellent ways to create visual and audio content from text prompts. Learn more in our blog here.
We've expanded Gemini 2.5 Flash and Pro model capabilities to help enterprises build more sophisticated and secure AI-driven applications and agents. With thought summaries, businesses get clarity and auditability of a model’s raw thoughts — including key details and tool usage. The new Deep Think mode uses research techniques that enable the model to consider multiple hypotheses before responding.
Gemini 2.5 is now powering all Gemini Code Assist editions! We also launched Jules, a new autonomous AI coding agent, now in public beta, designed to understand user intent and perform coding tasks like writing tests and fixing bugs
Firebase Studio is a cloud-based, AI workspace powered by Gemini 2.5 that lets you turn your ideas into a full-stack app in minutes. Now you can import Figma designs directly into Firebase Studio using the builder.io plugin, and then add features and functionality using Gemini in Firebase without having to write any code.
We're making AI application deployment significantly easier with Cloud Run, launching three key updates: first, you can now deploy applications built in Google AI Studio directly to Cloud Run with just a single button click; second, we enabled direct deployment of Gemma 3 models from AI Studio to Cloud Run, complete with GPU support for scalable, pay-per-use endpoints; and third, we've introduced a new Cloud Run MCP server, which empowers MCP-compatible AI agents (like AI assistants, IDE integrations, or SDKs) to programmatically deploy applications. Read more here.

The news didn’t stop with I/O – we announced several important announcements to help you deploy AI at scale:

Introducing the next generation of AI inference, powered by llm-d: We’re making inference even easier and more cost-effective, by making vLLM fully scalable with Kubernetes-native distributed and disaggregated inference. This new project is called llm-d. Google Cloud is a founding contributor alongside Red Hat, IBM Research, NVIDIA, and CoreWeave, joined by other industry leaders AMD, Cisco, Hugging Face, Intel, Lambda, and Mistral AI.
Mistral AI's Le Chat Enterprise and Mistral OCR 25.05 model are available on Google Cloud. Available today on Google Cloud Marketplace, Mistral AI's Le Chat Enterprise is a generative AI work assistant designed to connect tools and data in a unified platform for enhanced productivity.
Anthropic’s Claude Opus 4 and Claude Sonnet 4 on Vertex AI. Claude Opus 4 and Claude Sonnet 4 are generally available as a Model-as-a-Service (MaaS) offering on Vertex AI. For more information on the newest Claude models, visit Anthropic’s blog.

…and made strides in security:

What’s new with Google Cloud’s Risk Protection Program. We unveiled at Google Cloud Next major updates to our Risk Protection Program, an industry-first collaboration between Google and insurers that provides competitively priced cyber-insurance and broad coverage for Google Cloud customers. We’re now including Affirmative AI insurance coverage for your Google-related AI workloads. Here’s what’s new.
How Confidential Computing lays the foundation for trusted AI. Our latest Confidential Computing innovations highlight the creative ways our customers are using Confidential Computing to protect their most sensitive workloads — including AI.
How governments can use AI to improve threat detection and reduce cost. In the latest Cloud CISO Perspectives newsletter, our Office of the CISO’s Enrique Alvarez, public sector advisor, explains how government agencies can use AI to improve threat detection — and save money.

News you can use: Actionable ways to get started

Get fluent in generative AI:

62% of employers now expect candidates and employees to possess at least some familiarity with AI. That’s why we launched a first-of-its kind generative AI certification for non-technical learners—plus a new suite of no-cost training to help you prepare for that certification. That means you — and your company — can be among the first to take advantage of this opportunity to validate your strategic acumen in gen AI. Become a generative AI leader today.

Then, put generative AI to work

At I/O, we expanded generative AI media on Vertex AI. But how do you get started, today? To help you make the most of all the latest generative AI media announcements, we redesigned Vertex AI Studio. The developer-first experience will be your source for generative AI media models across all modalities. You’ll have access to Google’s powerful generative AI media models such as Veo, Imagen, Chirp and Lyria in the Vertex AI Media Studio.

Redesigned Vertex AI Studio

To help you turn your generative AI ideas into real web applications, we published this guide to create gen AI apps in less than 30 seconds with Vertex AI and Cloud Run. Any developer knows it’s a complex process to build shareable, interactive applications: you have to set up infrastructure, wire APIs, and build a front-end. It's usually a complex process. What if you could skip the heavy lifting and turn your generative AI concept into a working web app with just a few clicks?

New how-to series alert: Text-to-SQL agents

Recently, powerful large language models (LLMs) like Gemini, with their abilities to reason and synthesize, have driven remarkable advancements in the field of text-to-SQL. In this blog post, the first entry in a series, we explore the technical internals of Google Cloud's text-to-SQL agents.

Real-life demo: What if we turned Gemini into an AI basketball coach?

We rounded out this month with a deep-dive into a demo we showcased at Google Cloud Next and most recently, at I/O. In this article, we showed an AI experiment that turns Gemini 2.5 Pro into a jump shot coach. By combining a ring of Pixel cameras with Vertex AI, the coaching system connects AI motion capture, biomechanical analytics, and Gemini-powered coaching via text and voice.

“It’s like we always say: AI is only as good as the information you give it. For the AI basketball coach to be accurate, we knew we had to talk to actual, real-life professionals. So we talked to our partners at the Golden State Warriors and came up with essential criteria for helping you shoot like the pros.”

April

From Agent2Agent Protocol to Gemini 2.5 Pro, it’s been an exciting month. We hosted Google Cloud Next in Las Vegas on April 9th with over 30,000 people, announcing incredible innovations from Ironwood TPU to Agent2Agent Protocol. We also expanded Vertex AI in dizzying ways – now, it’s the only platform with generative media models across video, image, speech, and music and makes it possible for multiple agents to coexist in your enterprise.

If you missed the livestream, take a look at our Day 1 recap and summary of the developer keynote. It’s been incredible to see how customers have been applying AI to hundreds of use cases. In fact, we’ve counted more than 600 examples.

Top announcements

Agents

Our recently launched Agent2Agent (A2A) protocol is getting a lot of attention, and for good reason. This open interoperability protocol is designed to make it easy for AI agents to communicate with one another, no matter its foundation. It’s also a powerful complement to Anthropic’s Model Context Protocol (MCP). Together, these two open protocols are the foundation for complex multi-agent systems.

Since the launch, we’ve been excited to see all the discussions and reactions across social media and YouTube. The A2A GitHub repository (13k stars) has grown quickly, which underscores the industry's eagerness for a standardized agent communication protocol, especially these features:

Easy agent discovery: A key feature of A2A is the ability for agents to discover the capabilities of other agents through standardized "Agent Cards".
Complementary to MCP: As we say on our A2A ❤️ MCP topic page, A2A complements MCP. MCP. MCP equips an individual agent with the right context and tools to make it more capable A2A enables multiple, diverse agents to communicate and work with each other
Open and community driven: A2A is open source, inviting contributions from the broader community to refine and expand its functionality. To learn more, check out our GitHub here.

And, speaking of agents, we made a host of updates to Google Agentspace – starting with unified search. Now, Agentspace is integrated with Chrome Enterprise, which means you can search and access all of your enterprise’s resources — right from the search box in Chrome. You can also discover and adopt agents quickly and easily with Agent Gallery, and create agents with our new no-code Agent Designer.

Models

This month, we announced six new models (some in preview, some generally available) for our customers:

Gemini 2.5 Pro is available in preview on Vertex AI and the Gemini app.
Gemini 2.5 Flash — our workhorse model optimized specifically for low latency and cost efficiency — is coming soon to Vertex AI, AI Studio, and the Gemini app.
Imagen 3, our highest quality text-to-image model, now has improved image generation and inpainting capabilities for reconstructing missing or damaged portions of an image.
Chirp 3, our groundbreaking audio generation model, now includes a new way to create custom voices with just 10 seconds of audio input.
Lyria, the industry's first enterprise-ready, text-to-music model, can transform simple text prompts into 30-second music clips.
Veo 2, our industry-leading video generation model, is expanding with new features that help organizations create videos, edit them, and add visual effects.

Security

Hot on the heels of our AI Protection news, we introduced Google Unified Security at Next ‘25, which lays the foundation for superior security outcomes, followed by the RSA Conference in San Francisco. We showcased there:

Three new ways you can use AI as your security sidekick — complete with prompts to get you started.
Two new security agents for malware analysis and alert triage (plus two agents for customers who opt into the new SecOps Labs.)
Our singular vision for how agents can revolutionize security operations centers.
New open-source MCP servers for Google Security Operations, Google Threat Intelligence, and Security Command Center.

News you can use:

In just the last year alone, we’ve seen over 40x growth in Gemini use on Vertex AI, now with billions of API calls per month. So what’s new and better with Vertex AI?

To start:

Meta’s Llama 4 is generally available on Vertex AI.

Vertex AI Dashboards: These help you monitor usage, throughput, latency, and troubleshoot errors, providing you with greater visibility and control.
Vertex AI Model Optimizer: This capability uses Google's unique understanding of Gemini to automatically direct your query to the most performant model and tools, based on your quality, speed and cost preferences.
Live API: To enable truly conversational interactions, Live API offers streaming audio and video directly into Gemini.

Worth a second read:

Agent Development Kit (ADK) is a new open-source framework for designing agents built on the same framework that powers Google Agentspace and Google Customer Engagement Suite (CES) agents. Many powerful examples and extensible sample agents are readily available in Agent Garden.
Agent Engine is a fully managed runtime in Vertex AI that helps you deploy your custom agents to production with built-in testing, release, and reliability at a global, secure scale.

Next month, we’ll be releasing several guides and deep-dives into how to use all these features, so stay tuned!

Hear from our leaders:

As usual, we ended this month with our monthly column, The Prompt. In this installment, we hear from Logan Kilpatrick, AI product leader on Google DeepMind, on how multimodal capabilities – especially audio and vision – are bringing about a new UX paradigm. Here’s a snippet:

“There’s the age-old quote that a picture is worth a thousand words. This matters double in the world of multimodal. If I look at my computer right now and try to describe everything I see, it would take 45 minutes. Or, I could just take a picture. Use cases for vision could be something from as simple as object tracking to image detection. For example, a factory watching an assembly line to make sure there's no impurities in the product they’re creating. Or analyzing dozens of pictures of your farm and trying to understand crop yields. There's huge breadth and opportunity by blending these modalities together.”

March

In March, we made big strides in our models and accessibility – from announcing Gemini 2.5, our most intelligent AI model, to Gemma 3, our most capable and advanced version of the Gemma open-model family. We also introduced Gemini Code Assist for individuals, a free version of our AI coding assistant.

In the industry, we shone light on telecom and gaming. We went to Mobile World Congress, where we displayed key agent use cases where AI is becoming an imperative in telecom. At the same time, we talked to leaders in the games industry, who told us how they’re using Google Cloud AI to drive unprecedented advancements in game development, including smarter, faster, and more immersive gaming experiences.

Top announcements

The Gemini family is growing – we introduced Gemini 2.5, a thinking model designed to tackle increasingly complex problems. Gemini 2.5 Pro Experimental leads common benchmarks by meaningful margins and showcases strong reasoning and code capabilities. Learn all about it here.

We also introduced Gemma 3, the most capable model you can run on a TPU or GPU. To help you get started, we shared guides on how to deploy your AI workloads on Cloud Run and Vertex AI.

In the world of open-source, we announced Claude 3.7 Sonnet, Anthropic’s most intelligent model to date and the first hybrid reasoning model on the market, is available in preview on Vertex AI Model Garden. Claude 3.7 Sonnet can produce quick responses or extended, step-by-step thinking that is made visible to the user. Explore our sample notebook and documentation to start building.

Finally, we took a step forward in security. We introduced AI Protection, a set of capabilities designed to safeguard AI workloads and data across clouds and models — irrespective of the platforms you choose to use. Our Office of the CISO suggests these five do-it-today, actionable tips on how leaders can help their organizations adopt AI securely.

News you can use

Wondering how to make the most of your AI? When it comes to Gemini 2.0, we created a helpful guide that teaches you how to optimize one of enterprises’ most time-intensive tasks: document extraction.

From an infrastructure perspective, we broke down the four top use cases for AI Hypercomputer and ways to get started. In this guide, you’ll learn everything from affordable inference to reducing delivery bottlenecks. Take Moloco, for example. Using the AI Hypercomputer architecture they achieved 10x faster model training times and reduced costs by 2-4x.

And, speaking of cost – do you know the true cost of enterprise AI? Enterprises need ways to optimize large AI workloads because these resources can still be quite expensive. Learn how to calculate your AI cost and dig into these five tips to optimize your workflow on Google Cloud Platform.

Hear from our leaders

We closed off this month with our monthly column, The Prompt. In this installment, we hear from Suraj Poozhiyil, AI product leader, on how AI agents depend on “enterprise truth” – your enterprise’s unique context – to be successful. Here’s a snippet:

We’ve heard it before – AI is only as good as the data you put into it. When it comes to agents, AI agents are only as good as the context you give them. Enterprise truth is the answer to questions like, "What is our company’s policy for creating a purchase order?" and "What is the specific workflow for approvals and compliance?"

February

2025 is off to a racing start. From announcing strides in the new Gemini 2.0 model family to retailers accelerating with Cloud AI, we spent January investing in our partner ecosystem, open-source, and ways to make AI more useful. We’ve heard from people everywhere, from developers to CMOs, about the pressure to adapt the latest in AI with efficiency and speed – and the delicate balance of being both conservative and forward-thinking. We’re here to help. Each month, we’ll post a retrospective that recaps Google Cloud’s latest announcements in AI – and importantly, how to make the most of these innovations.

Top announcements: Bringing AI to you

This month, we announced agent evaluation in Vertex AI. A surprise to nobody, AI agents are top of mind for many industries looking to deploy their AI and boost productivity. But closing the gap between impressive model demos and real-world performance is crucial for successfully deploying generative AI. That’s why we announced Vertex AI’s RAG Engine, a fully managed service that helps you build and deploy RAG implementations with your data and methods. Together, these new innovations can help you build reliable, trustworthy models.

From an infrastructure perspective, we announced new updates to AI Hypercomputer. We wanted to make it easier for you to run large multi-node workloads on GPUs by launching A3 Ultra VMs and Hypercompute Cluster, our new highly scalable clustering system. This builds on multiple advancements in AI infrastructure, including Trillium, our sixth-generation TPU.

What’s new in partners and open-source

This month, we invested in our relationship with our partners. We shared how Gemini-powered content creation in Partner Marketing Studio will help partners co-market faster. These features are designed to streamline marketing efforts across our entire ecosystem, empowering our partners to unlock new levels of success, efficiency, and impact.

At the same time, we shared several important announcements in the world of open-source. We announced Mistral AI’s Mistral Large 24.11 and Codestral 25.01 models on Vertex AI. These models will help developers write code and build faster – from high-complexity tasks to reasoning tasks, like creative writing. To help you get started, we provided sample code and documentation.

And, most recently, we announced the public beta of Gen AI Toolbox for Databases in partnership with LangChain, the leading orchestration framework for developers building LLM applications. Toolbox is an open-source server that empowers application developers to connect production-grade, agent-based generative AI applications to databases. You can get started here.

Industry news: Google Cloud at the National Retail Federation (NRF)

The National Retail Federation kicked off the year with their annual NRF conference, where Google Cloud showed how AI agents and AI-powered search are already helping retailers operate more efficiently, create personalized shopping experiences, and use AI to get the latest products and experiences to their customers. Check our new AI tools to help retailers build gen AI search and agents.

As an example, Google Cloud worked with NVIDIA to empower retailers to boost their customer engagements in exciting new ways, deliver more hyper-personalized recommendations, and build their own AI applications and agents. Now with NVIDIA's AI Enterprise software available on Google Cloud, retailers can handle more data and more complex AI tasks without their systems getting bogged down.

News you can use

This month, we shared several ways to better implement fast-moving AI, from a comprehensive guide on Supervised Fine Tuning (SFT), to how developers can help their LLMs deliver more accurate, relevant, and contextually aware responses, minimizing hallucinations and building trust in AI applications by optimizing their RAG retrieval.

We also published new documentation to use open models in Vertex AI Studio. Model selection isn’t limited to Google’s Gemini anymore. Now, choose models from Anthropic, Meta, and more when writing or comparing prompts.

Hear from our leaders

We closed out the month with The Prompt, our monthly column that brings observations from the field of AI. This month, we heard from Warren Barkley, AI product leader, who shares some best practices and essential guidance to help organizations successfully move AI pilots to production. Here’s a snippet:

More than 60% of enterprises are now actively using gen AI in production, helping to boost productivity and business growth, bolster security, and improve user experiences. In the last year alone, we witnessed a staggering 36x increase in Gemini API usage and a nearly 5x increase of Imagen API usage on Vertex AI — clear evidence that our customers are making the move towards bringing gen AI to their real-world applications.

What’s new with Google Cloud - 2025

Fri, 26 Dec 2025 17:00:00 +0000

Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more.

Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

Dec 22 - Dec 26

To design production-ready AI agents, you must choose the right tools for memory, reasoning, and orchestration. To simplify this process, Choose your agentic AI architecture components provides an iterative framework to help you select products and tools that best match your workload characteristics. To build and deploy secure and reliable single-agent AI systems on a scalable serverless platform, see our Single-agent AI system using ADK and Cloud Run architecture guide.

Dec 15 - Dec 19

Announcing Advanced Governance Capabilities to Vertex AI Agent Builder: Today, with the integration of the Cloud API Registry, we’re excited to bring enhanced tool governance capabilities to Vertex AI Agent Builder. With this latest update, administrators can now manage available tools for developers across your organization directly in Vertex AI Agent Builder Console, and developers can leverage tools managed by the registry with a new ApiRegistry.

Following last month's expansion of our Agent Builder platform, we are also introducing new capabilities across the entire agent lifecycle to help developers build faster using new ADK capabilities and visual tools, and scale with high performance through the expansion of Agent Engine services, including the general availability of support for sessions and memory. Read more below.

Vertex AI Agent Builder provides the unified platform to manage the entire agent lifecycle, helping you close the gap from prototype to a production-ready agent. To explore these new features, visit the updated Agent Builder documentation and release notes.
Single-tenant Cloud HSM is now Generally Available: We’re thrilled to announce the General Availability (GA) of Single-tenant Cloud HSM - a standards compliant, highly available, and scalable HSM cluster that provides you complete control over your cryptographic keys for highly sensitive workloads in the cloud for general purpose applications.

Customers have complete control over their cryptographic keys and the ability to manage their own admin credentials through our gcloud APIs, which establish a cryptographically isolated cluster of dedicated HSM partitions for each customer.

Single-tenant Cloud HSM is integrated with Cloud KMS, allowing its use with Customer-Managed Encryption Keys (CMEK). Single-tenant Cloud HSM is available in the following regions: us-central1, us-east4, europe-west1, and europe-west4.
Advanced AI, data, and compliance security capabilities are now available to Security Command Center (SCC) Premium pay-as-you-go (PayGo) customers. Previously exclusive to Enterprise and Premium subscriptions, we now offer to PayGo customers the AI Security Dashboard, Data Security Posture Management (DSPM), Compliance Manager, and Security Graph, including Graph Search and Correlated Threats. This update can help you address novel risks from generative AI and autonomous agents by offering integrated, automated protection for both traditional and AI workloads in Google Cloud. Customers can start a 30-day free trial to access the full SCC Premium experience.

Dec 8 - Dec 12

Application Design Center is now Generally Available
We're excited to announce the General Availability (GA) of Application Design Center, enabling platform teams and developers to streamline cloud application infrastructure design, deployment, and evolution, ensuring security and best practices.

This GA launch includes powerful new capabilities such as enterprise-grade governance with public APIs and gcloud CLI support; bring your own Terraform, full compatibility with VPC Service Controls; and simplified onboarding with app-managed project support.

To learn more, read the Application Design Center GA launch blog.
Apigee Feature Templater Simplifies API Proxy Development for Everyone
The new open-source Apigee Feature Templater (AFT) streamlines API proxy authoring by turning complex policies into reusable building blocks called "features." Non-experts can quickly assemble robust proxies—including AI Gateways, security, and data masking—using simple CLI or REST commands. AFT accelerates time-to-market by enabling expert developers to delegate feature creation and empowering a broader team to compose APIs. Read the full release details.
Navigating the Industry Shift in Client Authentication for Apigee mTLS
An industry policy change is phasing out the Client Authentication Extended Key Usage (EKU) in public certificates, directly impacting server-to-server mTLS for Apigee. This shift forces organizations away from Public CAs to manage their own Private PKI to maintain service continuity by mid-2026. This article presents the two paths: implementing a Private Certificate Authority (Private CA), ideally using Google Cloud Certificate Authority Service (CAS) for immediate mTLS continuity; or modernizing long-term with Demonstrating Proof of Possession (DPoP) for maximum operational efficiency. Read about the two paths to mTLS continuity.
Learn how to implement Kubernetes Secrets in Apigee hybrid
Apigee hybrid introduces direct, read-only access to custom Kubernetes Secrets within API proxies. This exclusive feature offers a superior way to handle highly sensitive credentials (like private keys and backend passwords) compared to KVMs. Secrets never leave the cluster boundary, ensuring enhanced compliance and security. It enables a clean separation of duties, allowing cluster operators to manage credentials via GitOps workflows while API developers securely reference them using flow variables, without ever viewing the raw sensitive data. Read the full article.
Don't let your AI program fail at the final hurdle. Our new guide, Successful Chatbot: 5 Steps from ROI to Rollout, outlines the essential practices for rigorous customer testing and strategic deployment. Learn how to align testing with business goals, define clear evaluation criteria, and drive actionable insights. The post emphasizes that delivering a successful AI program requires more than just domain expertise, highlighting the importance of clear scoping, strategic staffing, and disciplined financial planning. This is crucial for maximizing confidence in your AI's long-term impact, especially in regulated industries like healthcare.

Useful Product links - Google Cloud's Vertex AI & Agentspace, Google Cloud's Healthcare API, and, Google Cloud's Natural Language API
Apigee Now Supports Model Context Protocol (MCP) for Secure Agentic Tools
Google has expanded support for Model Context Protocol (MCP) with the release of fully-managed, remote MCP servers, giving developers worldwide consistent and enterprise-ready access to Google and Google Cloud services. This includes support for MCP in Apigee, which makes it possible for agents to use your secure, governed APIs and custom workflows cataloged in Apigee API hub as tools to complete tasks for end users.

With Apigee’s support for MCP, you don’t need to make any changes to your existing APIs, write any code, or deploy and manage any local or remote MCP servers. Apigee uses your existing API specifications and manages the underlying infrastructure and transcoding, so that you can focus on the business logic for your agents. Read the full announcement.
Introducing Fully-Managed MCP Servers to Power Agentic AI
Google Cloud is announcing fully-managed, remote Model Context Protocol (MCP) servers, enhancing Google’s API infrastructure to provide a unified, enterprise-ready layer for AI agents. This eliminates the burden on developers to install and manage individual MCP servers. Now, AI agents can reliably use trusted Google services like Google Maps, BigQuery, Compute Engine, and GKE as tools to perform complex tasks. This unified approach, managed via Cloud IAM and Apigee API Hub, ensures rigorous governance, security, and observability for all agentic interactions. Read the full announcement.
Marketplace Customer Credit Program now available for Marketplace Channel Private Offers
Google Cloud's Marketplace Customer Credit Program offers up to 3% in Google Cloud credits when customers purchase an eligible cloud marketplace solution for the first time, whether directly through an ISV or via a chosen channel partner. Learn more.
Two-step control plane minor upgrades with rollback safety in Public preview
Upgrading a production Kubernetes cluster can be a stressful, high-stakes event. GKE's new two-step control plane minor upgrade with rollback safety gives you a safe window to validate a new minor version before committing, with a simple rollback option if you find any issues. By decoupling control plane binary changes from new API and feature changes, you can easily revert to the previous minor version if issues arise during the validation period. Learn more about this new, safer upgrade process.

Google named a Leader in the 2025 IDC MarketScape for Worldwide Hyperscaler Marketplaces
IDC Marketscape has positioned Google as a Leader in the 2025 IDC MarketScape for Worldwide Hyperscaler Marketplaces. We believe this recognition underscores our commitment to deliver a cloud marketplace experience that fuels the AI agent economy and accelerates innovation. This achievement reflects our dedication to creating an open and interoperable agentic AI ecosystem for our customers and partners. Learn more.

Dec 1 - Dec 5

Unlock the full potential of your data with Object Contexts in Google Cloud Storage
This new feature provides a foundation for semantic storage and actions, allowing you to integrate Gemini with GCS objects and enrich your objects in a more intelligent and meaningful way. Learn how to get started with Object Contexts and revolutionize your data workflows. Learn more.

Nov 24 - Nov 28

Boost API Security: IP Allowlisting and ML Enhancements for Apigee Abuse Detection
To keep your applications safe, it’s critical to detect and block attacks on your APIs as quickly as possible. In the past few months, we’ve made some changes to Advanced API Security’s Abuse Detection feature to make it easier and faster to identify legitimate attacks and take action. Get all the details on Apigee's new IP allowlisting.
Apigee AI Gateway Deep Dive on December 11
Join the final Apigee Community Tech Talk of the year for a deep dive into the Apigee AI Gateway. This session provides practical details on integrating, proxying, and converting complex MCP protocol services with traditional REST backends. Learn specific techniques for securing, monitoring, and gaining technical control over MCP backends to meet enterprise-grade governance requirements. Register now

Nov 10 - Nov 14

Deploy n8n to Cloud Run
With just a few commands, you can deploy n8n to Cloud Run and have it up and running, ready to supercharge your business with AI workflows that can manage spreadsheets, read and draft emails, and more. n8n and Cloud Run are both easy to use and powerful tools that empower developers to do more with AI. Learn more here.
GKE Node Memory Swap in Public preview
You can now configure swap space on your GKE Standard nodes to provide a crucial buffer against Out-of-Memory (OOM) errors for memory-intensive applications, especially during unexpected usage spikes. Enabling swap can improve workload resilience, reduce pod evictions due to memory pressure, and enhance overall application stability and cost-effectiveness. This feature is currently available in a public preview.

Nov 3 - Nov 7

Announcing the Data Engineering Agent
Data teams can now automate complex SQL pipeline tasks with the new Data Engineering Agent for BigQuery, available in Preview. This agent simplifies development, maintenance, and troubleshooting, allowing engineers to focus on strategic initiatives. It supports natural language pipeline creation, intelligent modification, and seamless migration from legacy tools.

Transform your data engineering workflows today!
From Threat Model to TTX: Bringing a New Design Partner to the Table
Gain an overview of threat modeling, how threat models can be performed rapidly, and why threat model scenarios make excellent tabletop scenarios - especially for products that are still in development.

To get more information about threat modeling or tabletop exercises, check out The Defender’s Advantage or reach out to a Mandiant cybersecurity expert for specialized assistance.
Application Monitoring now includes a Topology.
Application Monitoring now includes a graphical representation of runtime dependencies (i.e Topology) for your App Hub defined application. This now allows you to quickly understand your app architecture, spot anomalous runtime interactions and resolve issues flagged from alerts quicker. Runtime dependencies are extracted from the OpenTelemetry traces you send to Cloud Trace from your App Hub registered workload.

Follow the outline here to register your app and unlock the benefits of Application Monitoring and its newly launched Topology
Supercharge AI Agents: Apply Enterprise Governance to GenAI Workflows with Apigee
As Generative AI agents move to production, you need control over cost, reliability, and security. A powerful new pattern introduces Apigee as the unified AI Agent Gateway for Large Language Model (LLM) calls. Route agent traffic through Apigee to gain immediate enterprise-grade governance, including dynamic circuit breaking, token consumption quotas, and sensitive data masking. A new Apigee wrapper for the Agent Development Kit (ADK) simplifies implementation. Turn your agents into manageable, secure AI products.

Read the full article and explore the new pattern.

Oct 20 - Oct 24

Dataframe visualization in Colab Enterprise. Use visualization cells to create custom, stylized visualizations of your DataFrames: no coding required! Choose your fields, chart type, aggregation, and color scheme, then see a visualization of your data without leaving your notebook. Check out the tutorial and get started with data visualization today.

Oct 13 - Oct 17

Build Serverless AI in the Cloud Run Hackathon
Ready to go from idea to global scale in minutes? The Cloud Run Hackathon is here! Build serverless AI apps with AI Studio, orchestrate intelligent agents, or harness the power of GPUs. Compete for a share of $50,000+ in prizes!
- Submissions are open from Oct 6, 2025 to Nov 10, 2025.
- Learn more and register: run.devpost.com

Oct 6 - Oct 10

Multi-agent AI systems help you optimize complex and dynamic processes by segmenting them into discrete tasks that multiple specialized AI agents collaboratively execute. To get started with building secure and reliable multi-agent AI systems, see this reference architecture guide: Design a multi-agent AI system in Google cloud. The example architecture in this guide showcases a couple of agent patterns: sequential, and loop. For a comprehensive review of all the possible agent design patterns and for help with choosing patterns that are appropriate for your use cases, see this design guide: Choose a design pattern for your agentic AI system.

Sept 29 - Oct 3

Announcing Koog Supports for Agent2Agent protocol (A2A)
The future of interconnected AI is here. We're thrilled to announce that Koog now supports A2A, a protocol that lets agents talk directly, securely, and seamlessly across companies and clouds. For Kotlin developers, this unlocks a new era of powerful, enterprise-grade AI. Build sophisticated agents that automatically discover and collaborate with other services, all while calling on Google Cloud's state-of-the-art models like Gemini directly from your workflows. Stop building bridges and start creating truly intelligent, interconnected systems today. Learn more about building with Koog, A2A, and Google Cloud.

Sept 15 - 19

Your AI is Now a Local Expert: Grounding with Google Maps is GA
We are excited to announce the General Availability (GA) of Grounding with Google Maps in Vertex AI. This feature lets developers build generative AI applications that are connected to real-world, up-to-date information from Google Maps, using its data on over 250 million places worldwide.

To learn more and get started, visit our documentation and check out our demo.
Production-ready YOLO model training serving workflow on Vertex AI
This guide walks you through a complete, automated workflow for training a custom YOLO model on Vertex AI. You'll learn how to use a custom training job, package the model in a custom prediction container, and register it in the Vertex AI Model Registry, making it ready for easy deployment. Best of all, this approach is designed to work directly with existing Vertex AI managed datasets for object detection, meaning you can reuse the same data you're already using for AutoML models.

Checkout details on developer forums

Sept 8 - 12

Scaling Inference To Billions of Users And AI Agents: Discover the architecture required to serve AI models at a planetary scale. This article details how Google Cloud’s ecosystem—from the GKE Inference Gateway for smart load balancing to the power of custom TPUs and open-source engines like vLLM—provides a production-ready path. Move beyond the hype and learn how to build for the next wave of AI. Explore the technical deep-dive.
We're celebrating the one-year anniversary of bringing Confidential Computing with Intel TDX to Google Cloud. We've been shipping new capabilities to help you protect your most sensitive data while it's in use. Now Generally Available:
- Confidential GKE Nodes with Intel TDX: Secure entire Kubernetes clusters, node pools, and workloads.
- Confidential Space with Intel TDX: Build secure data clean rooms for collaboration on sensitive information.
- Confidential GPUs: Protect cutting-edge AI workloads with Confidential NVIDIA H100s GPUs on GCE and GKE.
  
  We've also expanded Intel TDX to more regions! Read the blog

Aug 25 - 29

Applied AI for Modern Manufacturers: New original growth series, hosted by Jake Hall, The Manufacturing Millennial, that dives into leading trends, best practices, and what companies are doing right now with AI in manufacturing. Hear from industry thought leaders - Rick Bullotta, Jonathan Wise, Walker Reynolds and Berardino Baratta - and Google Cloud experts - Praveen Rao, Eric Lam, Dave Nguyen Ph.D., Geoffrey Hirschheim, and Jim Anderson. Watch Modules 1 and 2 now, where we delve into the AI Innovation and trends and AI Costs and ROI in the Era of Digital Manufacturing. Next module kicks off Tuesday, Sep 2. Join now
Firestore with MongoDB compatibility is now generally available (GA): Developers can now build cost-effective, scalable, and highly reliable apps on Firestore's serverless database using a familiar MongoDB-compatible API. With the general availability of Firestore with MongoDB compatibility, the 600,000 active developers within the Firestore community can now use existing MongoDB application code, drivers, and tools, as well as the open-source MongoDB ecosystem, with Firestore's serverless service. Firestore offers benefits like multi-region replication, virtually unlimited scalability, up to 99.999% SLA, single-digit millisecond read performance, integrated Google Cloud governance, and pay-as-you-go pricing. Register now for the webinar on September 9th for a deep dive into Firestore with MongoDB compatibility.

Aug 18 - 22

Earth Engine in BigQuery is now Generally Available, bringing advanced geospatial analytics directly to your BigQuery workflows. Unlock insights with satellite data!

Aug 11 - Aug 15

New HPC VM and Slurm-gcp Images: A new HPC VM Image (under the project cloud-hpc-image-public) is now available, featuring a Rocky Linux 8-based image, IntelMPI v2021.16, and RDMA drivers. In partnership with SchedMD, new Slurm images (Slurm 25.05) have also been released. These are based on the latest HPC VM Image and are available for Ubuntu 22.04/24.04 Accelerator Images (ARM/AMD64) and Debian 12. These releases allow for the deployment of Slurm-ready clusters on GCP, providing the advantages of an HPC-optimized and performance-tested foundation. Read more.
Scaling our Gemini Embedding model in Vertex AI. Following increased popularity from its General Availability launch in May, we've recently increased quota and input size limits for customers of Vertex AI's most powerful text embedding model, gemini-embedding-001.
- Customers can now send up to 250 input texts per request (generating 250 embeddings) instead of only a single piece of text, bringing improved throughput and decreased round-trip network latency to large-scale embedding applications.
- We've increased quota limits for this model by 10x for most users, allowing hassle-free scaling of embedding applications to millions of tokens per minute and beyond.
  
  Get started with Gemini Embeddings today!

Aug 4 - Aug 8

GKE Node Memory Swap in private preview: You can now configure swap space on your GKE Standard nodes to provide a crucial buffer against Out-of-Memory (OOM) errors for memory-intensive applications, especially during unexpected usage spikes. Enabling swap can improve workload resilience, reduce pod evictions due to memory pressure, and enhance overall application stability and cost-effectiveness. This feature is currently available in a private preview.
- Contact your Google Cloud account team for more information and to request access.
- If you'd like to see more configurations, please contact your account team or make a feature request on our issue tracker!
Unlock Peak Performance: GKE Topology Manager is Now Generally Available: For customers running performance-sensitive workloads like AI/ML and HPC, GKE Topology Manager is now GA and ready to optimize your performance through NUMA alignment. By ensuring CPU, memory, and GPU resources are allocated on the same NUMA node, the Topology Manager minimizes cross-socket latency and maximizes throughput for your most demanding applications. Configure your alignment policies via the NodeConfig API to achieve significant performance gains.
- Achieve these performance gains by configuring your alignment policies via the NodeConfig API.
- If you'd like to see more expansion of Topology manager, please contact your account team or make a feature request on our issue tracker!
Fine-Tune at Scale: A Massive GKE NodeConfig Expansion for All Workloads: GKE has massively expanded node customization capabilities, adding nearly 130 new Sysctl and Kubelet configurations. This gives you finer-grained control for any workload needing node customization, performance requirements, or application-specific tuning. By replacing complex DaemonSets with native controls, you can benefit from enhanced security, high flexibility, faster node startup times, and less operational management.
- Check out our public documentation to learn how to consume these new NodeConfig options.
- If you'd like to see more configurations, please contact your account team or make a feature request on our issue tracker!
New capability for managing licenses in Compute Engine: We are announcing a new capability in Compute Engine which allows users to easily change the OS licenses on their VMs. Users can now append, remove, or replace OS licenses, enabling seamless transitions between license types—such as converting Red Hat Enterprise Linux from pay-as-you-go (PAYG) to bring-your-own subscription (BYOS), or upgrading from Ubuntu to Ubuntu Pro—without needing to redeploy instances. This feature empowers customers to meet their evolving licensing with speed and flexibility. To learn more, read about managing licenses on Compute Engine.
GKE Turns 10 Hackathon: Calling all developers! Google Kubernetes Engine (GKE) is turning 10, and we're celebrating with a hackathon! Join us to build powerful AI agents that interact with microservice applications using Google Kubernetes Engine and Google AI models. Compete for over $50,000 in prizes and demonstrate the power of building agentic AI on GKE.
- Submissions are open from Aug 18, 2025 to Sept, 22 2025
- Learn more and register: gketurns10.devpost.com

Jul 28 - Aug 1

Now GA: C4 VMs with Local SSD, bare metal, and larger shapes, on Intel Xeon 6: C4's expanded shapes are now GA! This expansion introduces C4 shapes with Google’s next-gen Titanium Local SSD, C4 bare metal instances, and new extra-large shapes, all powered by the latest Intel Xeon 6 processors, Granite Rapids. We’re excited to be the first leading hyperscaler to bring Xeon 6 to customers, delivering performance gains of up to 30% for general compute and up to 60% for ML recommendation workloads, and up to 35% lower access latency on Titanium Local SSD shapes. Learn more here!

Jul 14 - 18

DMS SQL Server to PostgreSQL migrations are now generally available! Accelerate your SQL Server modernization to Cloud SQL for PostgreSQL or AlloyDB for PostgreSQL with:
- Automatic database schema and code conversion
- Gemini augmented code conversion
- Gemini assisted PostgreSQL training and code improvements
- Low-downtime, CDC based data movement

Learn more and start your migration journey today!

Jul 7 - 11

Level up your AI Agent game with "The Agent Factory," a new video podcast for developers! We're going beyond the buzz to explore practical design, build, deploy, & management strategies for production-ready AI agents using Google Cloud. Expect code snippets, architecture deep dives, and integrations with open-source frameworks. Subscribe now!

Jun 23 - 27

Announcing partnership between Maxim AI and Google Cloud's Vertex AI to evaluate agentic applications — Maxim AI offers a comprehensive platform to help teams build, evaluate, and observe their AI agents with greater speed and confidence, covering the entire AI lifecycle from prompt engineering to production monitoring. This new partnership deeply integrates Vertex AI's Gen AI evaluation service directly within the Maxim AI environment, allowing users to leverage Gemini to power assistant responses and evaluate them using Vertex AI's comprehensive suite of evaluators. This provides access to metrics such as helpfulness, relevance, safety, and trajectory. The setup allows users to simulate, evaluate, and trace complex multi-turn interactions on Maxim, helping teams bring reliable AI products to market faster through a seamless developer experience. To learn more, check out this blog from Maxim AI
Run non-request workloads at scale with Cloud Run Worker Pools, now in Public Preview — Looking for the ease-of-use and scalability of serverless, without being limited to HTTP request-driven workloads? Cloud Run Worker Pools provide the same elasticity and high-quality developer experience as Cloud Run Services, but are designed for non-request workloads. Worker Pools are ideal for pull-based use cases like processing messages from Pub/Sub or Kafka, and other backend processing. Check out the public documentation to learn more about how to choose between Services, Jobs, and Worker Pools. Then give Worker Pools a try by deploying a sample Worker Pool.
Building a Multi-Agent Research Assistant for Financial Analysis with Schroders & Google Cloud — Financial analysts spend hours grappling with ever-increasing volumes of market and company data to extract key signals, combine diverse data sources, and produce company research. To maximise its edge as an active manager, Schroders wants to enable its analysts to shift from data collection to the higher-value strategic thinking that is critical for business scalability and client investment performance. To achieve this, Schroders and Google Cloud collaborated to build a multi-agent research assistant prototype using Vertex AI Agent Builder. Find out more here.

Jun 16 - 20

Simplify Your Multi-Cloud Strategy with Cloud Location Finder, now in Public Preview: As cloud environments expand beyond traditional architectures to include multiple clouds, managing your infrastructure effectively becomes more complex. Imagine effortlessly accessing consistent and up-to-date location information across different cloud providers, so your multi-cloud applications are designed and optimized with performance, security, and regulatory compliance in mind. Today, we are making this a reality with Cloud Location Finder, a new Google Cloud service which provides up-to-date location data across Google Cloud, Amazon Web Services (AWS), Azure, and Oracle Cloud Infrastructure (OCI). Now, you can strategically deploy workloads across different cloud providers with confidence and control. Cloud Location Finder is accessible via REST APIs and gcloud CLI, explore the Cloud Location Finder documentation and blog to learn more.
SOTA Gemini Text Embedding is Now Generally Available in Vertex AI: We recently launched a new Gemini Embedding text model (gemini-embedding-001) through the Vertex AI GenAI API. This groundbreaking model, leveraging Gemini's core language understanding, sets a new benchmark for text embeddings. It's the first unified model to excel across English, multilingual text, and code, outperforming previous models (text-embedding-005, text-multilingual-embedding-002) and achieving top ranking on the MTEB Multilingual leaderboard (100+ tasks). Our internal benchmarks demonstrate substantial performance improvements across various industry verticals, including retail, news, finance, healthcare, legal, and code. Detailed results are available in our technical report.
Backup vaults now support disk backups and multi-regions: We’ve added exciting new features to Google Cloud Backup and Disaster Recovery service! You can now secure your Persistent Disk and Hyperdisk backups in backup vaults, protecting them from cyber attacks and accidental data loss. In addition, backup vaults can now be created in multi-region storage locations, maximizing your data resilience and supporting compliance with business continuity requirements. Check out the blog to learn more!
DeepSeek R1, a powerful 671B parameters model, is now available as a fully managed API on Vertex AI in Preview, making advanced AI capabilities more accessible to developers. This Model as a Service (MaaS) offering eliminates the need for extensive GPU resources and infrastructure management, allowing developers to focus on building applications. DeepSeek R1 on Vertex AI provides a simple, scalable API with features like transparent "chain-of-thought" reasoning and enterprise-ready security. It's currently available at no additional cost during the preview, and can be accessed via UI, REST API, or the OpenAI Python API Client Library. Learn more.

Jun 9 - 13

Serverless Spark Now GA in BigQuery: Unified Analytics, Accelerated: Google Cloud Serverless for Apache Spark is now generally available in BigQuery, offering a unified developer experience in BigQuery Studio. Run Spark and SQL side-by-side on the same data, powered by the Lightning Engine for up to 3.6x faster performance and enhanced with Gemini productivity. Simplify your data pipelines and accelerate insights with this deeply integrated, zero-ops solution.
Cloud Pub/Sub introduced Pub/Sub Single Message Transforms (SMTs) to make it easy to perform simple data transformations right within Pub/Sub: An overarching goal of Pub/Sub is to simplify streaming architectures. We already greatly simplified data movement with Import Topics and Export Subscriptions, which removed the need to use additional services for ingesting raw streaming data through Pub/Sub into destinations like BigQuery. Pub/Sub Single Message Transforms (SMTs), designed to be a suite of features making it easy to validate, filter, enrich, and alter individual messages as they move in real time. The first SMT is available now: JavaScript User-Defined Functions (UDFs), which allows you to perform simple, lightweight modifications to message attributes and/or the data directly within Pub/Sub via snippets of JavaScript code. JavaScript UDFs as the first Single Message Transform is generally available starting today for all users. You'll find the new "Add Transform" option in the Google Cloud console when you create a topic or subscription in your Google Cloud project. You can also use gcloud CLI to start using JavaScript Single Message Transforms today.
This analysis evaluates the efficiency of fine-tuning a Llama 3-8B model on Vertex AI using both a single A100 GPU and a distributed four-A100 setup with Axolotl. While both methods achieved similar model convergence, the results underscore the power of distributed training. The process, which took 1 day and 20 hours on a single device, was completed in just 11 hours in the distributed environment—a dramatic acceleration. This speed was achieved with consistently high GPU utilization (94%), though at the cost of higher system and GPU memory overhead. For a detailed breakdown of the methodology, resource utilization metrics, and performance curves, you can review the complete work here.

May 26 - 30

Cloud Run GPUs are now GA: NVIDIA GPU support for Cloud Run is now generally available, offering a powerful runtime for a variety of use cases that’s also remarkably cost-efficient. Developers can now get on-demand access to GPUs with our serverless runtime, Cloud Run. Follow the footsteps of customers like MidJourney, vivo, and Wayfair. Read blog.
Datastream now supports MongoDB as a source! Seamlessly ingest data from MongoDB (Replica Sets, Sharded Clusters, self-hosted, AtlasDB) into BigQuery/Cloud Storage. Enjoy scalable, fully-managed data streaming with backfill and CDC, enabling real-time insights and data-driven decisions. Link

May 19 - May 23

Beyond cuts and fades: Understanding narrative flow with Gemini for accurate scene transition detection — Google Cloud's Gemini models are revolutionizing video understanding by accurately detecting narrative scene transitions, moving beyond simple cuts and fades. This breakthrough technology understands the holistic context of videos by analyzing visual, audio, and textual elements simultaneously. Media companies can now convert passive video assets into structured data, enabling intelligent content discovery, strategic ad placement, and personalized viewing experiences. The result? Up to 38% increased viewer engagement and 27% reduced abandonment rates.
- Read more on the medium blog.
- Learn more and access the code repository: View Code Repo
Announced at I/O: Deploy AI apps to Cloud Run from AI Studio and MCP — We are making AI deployments easier and more accessible by introducing new ways to deploy your apps to Cloud Run.
- You can deploy applications developed in AI Studio with a click of a button to Cloud Run, including Gemma 3.
- Model Context Protocol(MCP) is becoming a popular open protocol standardizing how AI agents interact with other tools. Now with Cloud Run MCP server, you can deploy apps from compatible AI agents like from Claude or VS Code Copilot.
- Read blog to learn more.

May 12 - May 16

Google for Startups Accelerator: AI For Energy now accepting applications!
Applications are now open for startups headquartered in Europe and Israel, working on solutions for utilities, grid operators and energy developers; solutions for residential and commercial end-use customers focused on demand flexibility and solutions for industrial customers. This equity-free program offers 10 weeks of intensive mentorship and technical project support to startups integrating AI into their core energy services or products. Selected startups will collaborate with a cohort of peer founders and engage with leaders across Google and the energy sector. The curriculum will provide founders with access to AI tools and include workshops on tech and infrastructure, UX and product, growth, sales, leadership and more. Learn more and apply before June 30th, 2025.
Extending Google Cloud Workstations containers to run any GUI based programAre you having difficulty customizing Google Cloud Workstations to run a GUI program outside of the supported configurations of IDE’s? If so, you’re not alone. In this article we discuss how to use the base Workstations Docker image and build it to run a terminal and Google Chrome.
Google Cloud Marketplace simplifies deals and improves economics. Announcing three initiatives that build upon Google Cloud Marketplace as a growth engine for customers and partners:

1. Improving partner deal economics to help partners retain more earnings by moving to a variable revenue share model
2. Simplifying commit drawdown for purchases through channel partners
3. Unlocking new workloads with the Marketplace Customer Credit Program incentive
  
  Learn more

2025 Google Cloud DORA Awards are now open for submission!Has your team achieved remarkable success through DORA principles? It's time to shine. We're thrilled to announce the launch of the 2025 Google Cloud DORA Awards, celebrating outstanding achievements in technology delivery and operational performance. Submit your story today!

May 5 - May 9

AI assisted development with MCP Toolbox for Databases
We are excited to announce new updates to MCP Toolbox for Databases. Developers can now use Toolbox from their preferred IDE, such as Cursor, Windsurf, Claude Desktop, more and leverage our new pre-built tools such as execute_sql and list_tables for AI-assisted development with Cloud SQL for PostgreSQL, AlloyDB and self-managed PostgreSQL.
- Get Started with MCP Toolbox for Databases

Apr 28 - May 2

Itching to build AI agents? Join the Agent Development Kit Hackathon with Google Cloud! Use ADK to build multi-agent systems to solve challenges around complex processes, customer engagement, content creation, and more. Compete for over $50,000 in prizes and demonstrate the power of multi-agent systems with ADK and Google Cloud.
- Submissions are open from May 12, 2025 to June 23, 2025.
- Learn more and register here.

Apr 21 - Apr 25

Iceland’s Magic: Reliving Solo Adventure through Gemini
Embark on a journey through Iceland's stunning landscapes, as experienced on Gauti's Icelandic solo trip. From majestic waterfalls to the enchanting Northern Lights, Gautami then takes these cherished memories a step further, using Google's multi-modal AI, specifically Veo2, to bring static photos to life. Discover how technology can enhance and dynamically relive travel experiences, turning precious moments into immersive short videos. This innovative approach showcases the power of AI in preserving and enriching our memories from Gauti's unforgettable Icelandic travels. Read more.
Introducing ETLC - A Context-First Approach to Data Processing in the Generative AI Era: As organizations adopt generative AI, data pipelines often lack the dynamic context needed. This paper introduces ETLC (Extract, Transform, Load, Contextualize), adding semantic, relational, operational, environmental, and behavioral context. ETLC enables Dynamic Context Engines for context-aware RAG, AI co-pilots, and agentic systems. It works with standards like the Model Context Protocol (MCP) for effective context delivery, ensuring business-specific AI outputs. Read the full paper.

Apr 14 - Apr 18

What’s new in Database Center
With general availability, Database Center now provides enhanced performance and health monitoring for all Google Cloud databases, including Cloud SQL, AlloyDB, Spanner, Bigtable, Memorystore, and Firestore. It delivers richer metrics and actionable recommendations, helps you to optimize database performance and reliability, and customize your experience. Database Center also leverages Gemini to deliver assistive performance troubleshooting experience. Finally, you can track the weekly progress of your database inventory and health issues.

Get started with Database Center today
- Access Database Center in Google Cloud console
- Review the documentation to learn more

Apr 7 - Apr 11

This week, at Google Cloud Next, we announced an expansion of Bigtable's SQL capabilities and introduced continuous materialized views. Bigtable SQL and continuous materialized views empower users to build fully-managed, real-time application backends using familiar SQL syntax, including specialized features that preserve Bigtable's flexible schema — a vital aspect of real-time applications. Read more in this blog.
DORA Report Goes Global: Now Available in 9 Languages!
Unlock the power of DevOps insights with the DORA report, now available in 9 languages, including Chinese, French, Japanese, Korean, Portuguese, and Spanish. Global teams can now optimize their practices, benchmark performance, and gain localized insights to accelerate software delivery. The report highlights the significant impact of AI on software development, explores platform engineering’s promises and challenges, and emphasizes user-centricity and stable priorities for organizational success. Download the DORA Report Now
New Google Cloud State of AI Infrastructure Report Released
Is your infrastructure ready for AI? The 2025 State of AI Infrastructure Report is here, packed with insights from 500+ global tech leaders. Discover the strategies and challenges shaping the future of AI and learn how to build a robust, secure, and cost-effective AI-ready cloud. Download the report and enhance your AI investments today. Download the 2025 AI infrastructure report now
Google Cloud and Oracle Accelerate Enterprise Modernization with New Regions, Expanded Capabilities
Announcing major Oracle Database@Google Cloud enhancements! We're launching the flexible Oracle Base Database Service and powerful new Exadata X11M machines. We're rapidly expanding to 20 global locations, adding new Partner Cross-Cloud Interconnect options, and introducing Cross-Region Disaster Recovery for Autonomous Database. Benefit from enhanced Google Cloud Monitoring, integrated Backup & DR, plus expanded support for enterprise applications like SAP. Customers can run critical Oracle workloads with more power, resilience, and seamless Google Cloud integration. Get started right away from your Google Cloud Console or learn more here.

Mar 17 - Mar 21

Cloud CISO Perspectives: 5 tips for secure AI success - To coincide with new AI Protection capabilities in Security Command Center, we’re offering 5 tips to set up your organization for secure AI success.
Our 4-6-3 rule for strengthening security ties to business: The desire to quickly transform a business can push leaders to neglect security and resilience, but prioritizing security can unlock value. Follow these 4 principles, 6 steps, and 3 metrics to use a security-first mindset to drive business results.
The new Data Protection Tab in Compute Engine ensures your resources are protected: Not only have we co-located your backup options, but we also have introduced smart default data protection for any Compute Engine instance created via Cloud Console. Here’s how it works.
DORA report - Impact of Generative AI in Software Development
This report builds on and extends DORA's research into AI. We review the current landscape of AI adoption, look into its impact on developers and organizations, and outline a framework and practical guidance for successful integration, measurement, and continuous improvement. Download the report!

Mar 10 - Mar 14

Protecting your APIs from OWASP’s top 10 security threats: We compare OWASP’s top 10 API security threats list to the security capabilities of Apigee. Here’s how we hold up.
Project Shield makes it easier to sign up, set up, automate DDoS protection: It’s now easier than ever for vulnerable organizations to apply to Project Shield, set up protection, and automate their defenses. Here’s how.
How Google Does It: Red teaming at Google scale - The best red teams are creative sparring partners for defenders, probing for weaknesses. Here’s how we do red teaming at Google scale.
AI Hypercomputer is a fully integrated supercomputing architecture for AI workloads – and it’s easier to use than you think. Check out this blog, where we break down four common use cases, including reference architectures and tutorials, representing just a few of the many ways you can use AI Hypercomputer today.
Transform Business Operations with Gemini-Powered SMS-iT CRM on Google Cloud: SMS-iT CRM on Google Cloud unifies SMS, MMS, email, voice, and 22+ social channels into one Smart Inbox. Enjoy real-time voice interactions, AI chatbots, immersive video conferencing, AI tutors, AI operator, and unlimited AI agents for lead management. Benefit from revenue-driven automation, intelligent appointment scheduling with secure payments, dynamic marketing tools, robust analytics, and an integrated ERP suite that streamlines operations from project management to commerce. This comprehensive solution is designed to eliminate inefficiencies and drive exponential growth for your business. Experience the Future Today.
Join us for a new webinar, Smarter CX, Bigger Impact: Transforming Customer Experiences with Google AI, where we'll explore how Google AI can help you deliver exceptional customer experiences and drive business growth. You'll learn how to:

Transform Customer Experiences: With conversational AI agents that provide personalized customer engagements.
Improve Employee Productivity & Experience: With AI that monitors customers sentiment in real-time, and assists customer service representatives to raise customer satisfaction scores.
Deliver Value Faster: With 30+ data connectors and 70+ action connectors to the most commonly used CRMs and information systems.

Register here

Mar 3 - Mar 7

Hej Sverige! Google Cloud launches new region in Sweden - More than just another region, it represents a significant investment in Sweden's future and Google’s ongoing commitment to empowering businesses and individuals with the power of the cloud. This new region, our 42nd globally and 13th in Europe, opens doors to opportunities for innovation, sustainability, and growth — within Sweden and across the globe. We're excited about the potential it holds for your digital transformations and AI aspirations.
[March 11th webinar] Building infrastructure for the Generative AI era: insights from the 2025 State of AI Infra report: Staying at the forefront of AI requires an infrastructure built for AI. Generative AI is revolutionizing industries, but it demands a new approach to infrastructure. In this webinar, we'll unveil insights from Google Cloud's latest research report and equip tech leaders with a practical roadmap for building and managing gen AI workloads, including: the top gen AI use cases driving the greatest return on investment, current infrastructure approaches and preferences for Generative AI workloads, the impact of performance benchmarks, scalability, and security on cloud provider selection. Register today.
Cloud CISO Perspectives: Why PQC is the next Y2K, and what you can do about it: Much like Y2K 25 years ago, post-quantum cryptography may seem like the future’s problem — but it will soon be ours if IT doesn’t move faster, explains Google Cloud’s Christiane Peters. Here's how business leaders can get going on PQC prep.
How Google Does It: Using threat intelligence to uncover and track cybercrime — How does Google use threat intelligence to uncover and track cybercrime? Google Threat Intelligence Group’s Kimberly Goody takes you behind the scenes.
5 key cybersecurity strategies for manufacturing executives — Here are five key governance strategies that can help manufacturing executives build a robust cybersecurity posture and better mitigate the evolving risks they face.
Datastream now offers Salesforce source in Preview. Instantly connect, capture changes, and deliver data to BigQuery, Cloud Storage, etc. Power real-time insights with flexible authentication and robust backfill/CDC. Unlock Salesforce data for Google Cloud analytics, reporting, and generative AI. Read the documentation to learn more.
Find out how much you can save with Spanner - According to a recent Forrester Total Economic Impact™ study, by migrating to Spanner from a traditional database, a $1 billion per year B2C organization could get a 132% return on investment (ROI) with a 9-month payback period, and realize $7.74M in total benefits over the three years. To see how, check out the blog or download the report.
GenAI Observability for Developers series: The Google Cloud DevRel team hosted a four-part webinar series, "Gen AI Observability for Developers," demonstrating observability best practices in four programming languages. Participants learned to instrument a sample application deployed on Cloud Run for auditing Vertex AI usage, writing structured logs, tracking performance metrics, and utilizing OpenTelemetry for tracing. The series covered Go, Java, NodeJS, and Python, using common logging and web frameworks. Missed it? Recordings and hands-on codelabs are available to guide you at:
- Gen AI O11y for Go Developers
- Gen AI O11y for Java Developers
- Gen AI O11y for NodeJS Developers
- Gen AI O11y for Python Developers
  
  Stay tuned for future events at cloudonair.withgoogle.com.

Feb 24 - Feb 28

Rethinking 5G: Ericsson and Google Cloud are collaborating to redefine 5G mobile core networks with a focus on autonomous operations. By leveraging AI and cloud infrastructure, we aim to enhance efficiency, security, and innovation in the telecommunications industry. This partnership addresses the increasing demands of 5G and connected devices, paving the way for a more dynamic and intelligent network future, and setting the stage for next-generation technologies like 6G. Learn more here.
Adopt a principles-centered well-architected framework to design, build, deploy, and manage Google Cloud workloads that are secure, resilient, efficient, cost-efficient, and high-performing. Also get industry and technology-focused well-architected framework guidance, like for AI and ML workloads.

Feb 17 - Feb 21

Easier Default Backup Configuration for Compute Engine Instances - The Create a Compute Instance page in the Google Cloud console now includes enhanced data protection options to streamline backup and replication configurations. By default, an option to back up data is pre-selected, ensuring recoverability in case of unforeseen events. Learn more here.

Feb 10 - Feb 14

[Webinar] Generative AI for Software Delivery: Strategies for IT Leaders: Generative AI is transforming the way organizations build and deploy software. Join Google Cloud experts on February 26th to learn how organizations can leverage AI to streamline their software delivery, including: the role of gen AI in software development, how to use gen AI for migration and modernization, best practices for integrating gen AI into your existing workflows, and real-world applications of gen AI in software modernization and migration through live demos. Register here.

Feb 3 - Feb 7

SQL is great but not perfect. We’d like to invite you to reimagine how you write SQL with Google’s newest invention: pipe syntax (public available to all BigQuery and Cloud Logging users). This new extension to GoogleSQL brings a modern, streamlined approach to data analysis. Now you can write simpler, shorter and more flexible queries for faster insights. Check out this video to learn more.

Jan 13 - Jan 17

C4A virtual machines with Titanium SSD—the first Axion-based, general-purpose instance with Titanium SSD, are now generally available. C4A virtual machines with Titanium SSDs are custom designed by Google for cloud workloads that require real-time data processing, with low-latency and high-throughput storage performance. Titanium SSDs enhance storage security and performance while offloading local storage processing to free up CPU resources. Learn more here.

Jan 6 - Jan 10

A look back on a year of Earth Engine advancements: 2024 was a landmark year for Google Earth Engine, marked by significant advancements in platform management, cloud integration, and core functionality and increased interoperability between Google Cloud tools and services. Here’s a round up of 2024’s top Earth Engine launches.
Get early access to our new Solar API data and features: We're excited to announce that we are working on 2 significant expansions to the Solar API from Google Maps Platform and are looking for trusted testers to help us bring them to market. These include improved and expanded buildings coverage and greater insights for existing solar installations with Detected Arrays. Learn more.
Google for Startups Accelerator: Women Founders applications are now open for women-led startups headquartered in Europe and Israel. Discover why this program could be the perfect fit for your startup and apply before January 24th, 2025.
Best of N: Generating High-Quality Grounded Answers with Multiple Drafts - We are excited to announce that Check Grounding API has released a new helpfulness score feature. Building on top of our existing groundedness score, we now enable users to implement Best of N to improve RAG response quality without requiring extensive model retraining. Learn more about Best of N and how it can help you here.

The Year in Google Cloud — 2025

Fri, 19 Dec 2025 17:00:00 +0000

In the AI era, when one year can feel like 10, you’re forgiven for forgetting what happened last month, much less what happened all the way back in January. To jog your memory, we pulled the readership data for top product and company news of 2025. And because we publish a lot of great thought leadership and customer stories, we pulled that data too. Long story short: the most popular stories largely mapped to our biggest announcements. But not always — there were more than a few sleeper hits on this year’s list. Read on to relive this huge year, and perhaps discover a few gems that you may have missed.

January

2025 started strong with important new virtual machine offerings, foundational AI tooling, and tools for both Kubernetes and data professionals. We also launched our "How Google Does It" series, looking at the internal systems and engineering principles behind how we run a modern threat-detection pipeline. We showed developers how to get started with JAX and made AI predictions for the year ahead. Readers were excited to learn about how L’Oréal built its MLOps platform and Deutsche Börse’s pioneering work on cloud-native financial trading.

Product news

Thought leadership:

Customer stories

February

There are AI products, and then there are products enhanced by AI. This month’s top launch, Gen AI Toolbox for Databases, falls into the latter category. This was also the month readers got serious about learning, with blogs about upskilling, resources, and certifications topping the charts. The fruits of our partnership with Anthropic made an appearance in our best-read list, and engineering leaders detailed Google’s extensive efforts to optimize AI system energy consumption. Execs ate up an opinion piece about how agents will unlock insights into unstructured data (which makes up 90% of enterprises’ information assets), and digested a sobering report on AI and cybercrime. During the Mobile World Congress event, we saw considerable interest in our work with telco leaders like Vodafone Italy and Amdocs.

Product and company news:

Thought leadership

Customer stories

March

Back when we announced it, our intent to purchase cybersecurity startup Wiz was Google’s largest deal ever, and the biggest tech deal of the year. We built on that security momentum with the launch of AI Protection. We also spread our wings to the Nordics with a new region, and announced the Gemma 3 open model on Vertex AI. Meanwhile, we explained the threat that North Korean IT workers pose to employers, gave readers a peek under the hood of the Colossus file system, and reminisced about what we’ve learned over 25 years of building data centers. Readers were interested in Levi’s approach to data and weaving it into future AI efforts, and in honor of the GDC Festival of Gaming, our AI partners shared some new perspectives on “living games.”

Product and company news

Thought leadership

Customer stories

April

With April came Google Cloud Next, our flagship annual conference. From Firebase Studio, Ironwood TPUs, and Google Agentspace, to Vertex AI, Cloud WAN, and Gemini 2.5, it’s hard to limit ourselves to just a few stories, there were so many bangers (for the whole list, there’s always the event recap). Meanwhile, our systems team discussed innovations to keep data center infrastructure’s thermal envelope in check. And at the RSA Conference, we unveiled our vision for the agentic security operations center of the future. On the customer front, we highlighted the startups who played a starring role at Next, and took a peek behind the curtain of The Wizard of Oz at Sphere.

Product and company news

Thought leadership

Customer stories

May

School was almost out, but readers got back into learning mode to get certified as generative AI leaders. You were also excited about new gen AI media models in Vertex AI, the availability of Anthropic’s Claude Opus 4 and Claude Sonnet 4. We also learned that you’re very excited to use AI to generate SQL code, and about using Cloud Run as a destination for your AI apps. We outlined the steps for building a well-defined data strategy, and showed governments how AI can actually improve their security posture. And on the customer front, we launched our “Cool Stuff Customers Built” round-ups, and ran stories from Formula E and MLB.

Thought leadership

Customer stories

June

Up until this point, the promise of generative AI was largely around text and code. The launch of Veo 3 changed all that. Developers writing and deploying AI apps saw the availability of GPUs on Cloud Run as a big win, and we continued our steady drumbeat of Gemini innovation with 2.5 Flash and Flash-Lite. We also shared our thoughts on securing AI agents. And to learn how to actually build these agents, readers turned to stories about Box, the British real estate firm Schroders, and French luxury conglomerate LVMH (home of Louis Vuitton, Channel, Sephora and more).

Thought leadership

Customer stories

July

Readers took a break from reading about AI to read about network infrastructure — the new Sol transatlantic cable, to be precise. Then it was back to AI: new video generation models in Vertex; a crucial component for building stateful, context-aware agents; and a new toolset for connecting BigQuery data to Agent Development Kit (ADK) and Multi-Cloud Protocol (MCP) environments. Developers cheered the integration between Cloud Run and Docker Compose, and executive audiences enjoyed a listicle on actionable, real-world uses for AI agents.

On the security front, we took a back-to-basics approach this month, exploring the persistence of some cloud security problems. And then, back to AI again, with our Big Sleep agent. Readers were also interested in how AI is alleviating record-keeping for nurses at HCA Healthcare, Ulta Beauty’s data warehousing and mobile record keeping initiatives, and how SmarterX migrated from Snowflake to BigQuery.

Thought leadership

Customer stories

August

AI is compute- and energy-intensive; in a new technical paper, we released concrete numbers about our AI infrastructure’s power consumption. Then people went [nano] bananas for Gemini 2.5 Flash Image on Vertex AI, and developers got a jump on their AI projects with a wealth of technical blueprints to work from. The summer doldrums didn’t stop our security experts from tackling the serious challenge of cyber-enabled fraud. We also took a closer look at the specific agentic tools empowering workers at Wells Fargo, and how Keeta processes 11 million blockchain transactions per second with Spanner.

Thought leadership

Customer stories

September

AI is cool tech, but how do you monetize it? One answer is the Agent Payment Protocol, or AP2. Developers and data scientists preparing for AI flocked to blogs about new Data Cloud offerings, the 2025 DORA Report, and new trainings. Executives took in our thoughts on building an agentic data strategy, and took notes on the best prompts with which to kickstart their AI usage. And because everybody is impacted by the AI era, including business leaders, we explained what it means to be “bilingual” in AI and security. Then, at Google's AI Builders Forum, startups described how Google’s AI, infrastructure, and services are supporting their growth. Not to be left out, enterprises like Target and Mr. Cooper also showed off their AI chops.

Thought leadership

Customer stories

October

Welcome to the Gemini Enterprise era, which brings enhanced security, data control, and advanced agent capabilities to large organizations. To help you prepare, we relaunched a variety of enhancements to our learning platform, and added new commerce and security programs. And while developers versed themselves on the finer points of Veo prompts, we discussed securing the AI supply chain, building AI agents for cybersecurity and defense, and a new vision on economic threat modeling. We partnered with PayPal to enable commerce in AI chats, Germany’s Planck Institute showed how AI can help share deep scientific expertise, and DZ Bank pioneered ways to make blockchain-based finance more reliable.

Thought leadership

Customer stories

November

Whether it was Gemini 3, Nana Banana Pro, or our seventh-generation Ironwood TPUs, this was the month that we gave enterprise customers access to all our latest and greatest AI tech. We also did a deep dive on how we built the largest-ever Kubernetes cluster, clocking in at a massive 130,000 nodes, and we announced a new collaboration with AWS to improve connectivity between clouds.

Meanwhile, we updated our findings on the adversarial misuse of AI by threat actors and on the ROI of AI for security, and executives vibed out on our piece about vibe coding. Then, just in time for the holidays, we took a look at how Mattel is using AI tools to revamp its toys, and Waze showed how it uses Memorystore to keep the holiday traffic flowing.

Thought leadership

Customer stories

December

The year is winding down, but we still have lots to say. Early returns show that you were interested in how to mitigate the React2Shell vulnerability, support for MCP across Google services, and the early access launch of AlphaEvolve. And let’s not forget Gemini 3 Flash, which is turning heads with its high-level reasoning, plus amazing speed and a flexible cost profile.

What does this all mean for you and your future? It’s important to contextualize these technology developments, especially AI. For example, the DORA team put together a guide on how high-performing platform teams can integrate AI capabilities into their workflows, we discussed what it looks like to have an AI-ready workforce, and our Office of the CISO colleagues put out their 2026 cybersecurity predictions. More to the point (guard), you could do like Golden State Warrior Stephen Curry and turn to Gemini to analyze your game, to prepare for the year ahead. We’ll be watching on Christmas Day to see how Steph is faring with Gemini’s advice.

Thought leadership

Customer stories

What Stephen Curry learned about his game from a custom Gemini agent

And that’s a wrap on 2025! Thanks for reading, and see you next year!

Supporting Viksit Bharat: Announcing our newest AI investments in India

Tue, 11 Nov 2025 03:30:00 +0000

Editor's note: This blog has been translated into Bengali, Hindi, Marathi, Tamil, and Telugu.

India’s developer community, vibrant startup ecosystem, and leading enterprises are embracing AI with incredible speed. To meet this moment for India, we are investing in powerful, locally-available tools in India that can help foster a diverse ecosystem, and ensure our platform delivers the controls you need for compliance and AI sovereignty.

Today, we’re announcing a significant expansion of our local AI hardware capacity for customers in India. This increase in local compute, powered by Google's AI Hypercomputer architecture with the latest Trillium TPUs, will help more businesses and public sector organizations train and serve their most advanced Gemini models in India.

By unblocking new opportunities for high-performance, low-latency AI applications we can help customers meet India’s data residency and sovereignty requirements.

Enabling models and control: AI tools built for India's context

While infrastructure is the foundation for digital sovereignty, it also requires control over the data and the models built on it. We’re committed to bringing our latest AI advancements to India faster than ever, with the controls you need.

Our new services would enable you to build, tune, and deploy models that understand India's unique business logic and rich cultural context.

Next-generation models, here in India: Earlier this year, Google Cloud made Gemini available to regulated Indian customers by deploying Gemini 2.5 Flash with local machine-learning processing support. Now, we’re opening early testing for our latest and most advanced Gemini models to Indian customers. We’re also committing to launching the most powerful Gemini models in India with full data residency support. This is a first for Google Cloud, and a direct response to help meet the needs of our Indian customers.
More AI capabilities, available locally: We’re providing additional consumption models and pre-built AI-powered applications tailored for local context by launching a suite of new capabilities with data residency support in India:

Batch support for Gemini 2.5 Flash: Now generally available, this allows organizations to run high-volume, non-real-time AI tasks at a lower cost, all in India.
Document AI: Now in preview, we’re providing local support to help Indian businesses automate document processing.

More local context in your AI: Grounding on Google Maps is a new capability to ground model responses in real time from Google Maps, ensuring AI applications can provide accurate, location-aware answers.

A sovereign AI ecosystem: Building for India, with India

The most durable and decisive factor for long-term digital sovereignty lies in cultivating the "human element" — the skilled talent and innovation ecosystem. A sovereign AI future depends on building a strong local ecosystem.

Our strategy is to support India’s ecosystem-led approach by investing in the researchers, developers, and startups who are building for India's specific needs.

Collaboration with IIT Madras: Google Cloud and Google DeepMind are thrilled to collaborate with IIT Madras to support the launch of Indic Arena. Run independently by the renowned AI4Bharat center at IIT Madras, this platform will allow users from all over India to anonymously evaluate and rank AI models on tasks unique to India's rich multilingual landscape. To support this initiative, we are providing cloud credits to power this critical, community-driven resource.

"At AI4Bharat, our mission is to build AI for India's specific needs. A critical part of this is having a neutral, standardized benchmark to understand how models are performing across our many languages,” said Mitesh Khapra, associate professor, IIT Madras. “Indic Arena will be that platform. We are delighted to have Google Cloud's support to provide the initial compute power to bring this independent, public-facing project to life for the entire Indian AI community."

We encourage all developers, researchers, and organizations in India to explore the Indic Arena platform and contribute to building a more inclusive AI future.

We invite the entire Indian ecosystem, from startups and universities to government bodies and enterprises, to take advantage of this new, dedicated capacity for Gemini in Vertex AI and our sovereign-ready infrastructure to build the next generation of AI that is built by Indians, for Indians.

How scientists can leverage AI agents using Gemini Enterprise, Gemini Code Assist, and Gemini CLI

Mon, 03 Nov 2025 17:00:00 +0000

Scientific inquiry has always been a journey of curiosity, meticulous effort, and groundbreaking discoveries. Today, that journey is being redefined, fueled by the incredible capabilities of AI. It’s moving beyond simply processing data to actively participating in every stage of discovery, and Google Cloud is at the forefront of this transformation, building the tools and platforms that make it possible.

The sheer volume of data generated by modern research is immense, often too vast for human analysis alone. This is where AI steps in, not just as a tool, but as a collaborative force. We’re seeing powerful new models and AI agents assist with everything from identifying relevant literature and generating novel hypotheses to designing experiments, running simulations, and making sense of complex results. This collaboration doesn’t replace human intellect; it amplifies it, allowing researchers to explore more avenues, more quickly, and with greater precision.

At Google Cloud, we’re bringing together high-performance computing (HPC) and advanced AI on a single, integrated platform. This means you can seamlessly move from running massive-scale simulations to applying sophisticated machine learning models, all in one environment.

So, how can you leverage these capabilities to get to insights faster? The journey begins at the foundation of scientific inquiry: the hypothesis.

AI-enhanced scientific inquiry

Every great discovery starts with a powerful hypothesis. With millions of research papers published annually, identifying novel opportunities is a monumental task. To overcome this information overload, scientists can now turn to AI as a powerful research partner.

Our Deep Research agent tackles the first step: performing a comprehensive analysis of published literature to produce detailed reports on a given topic that would otherwise take months to compile. Building on that foundation, our Idea Generation agent then deploys an ensemble of AI collaborators to brainstorm, evaluate, propose, debate, and rank novel hypotheses. This powerful combination, available in Gemini Enterprise, transforms the initial phase of scientific inquiry, empowering researchers to augment their expertise and find connections they might otherwise miss.

Go from hypothesis to results, faster

Once a hypothesis is formed, the work of translating it into executable code begins. This is where AI coding assistants, such as Gemini Code Assist, excel. They automate the tedious tasks of writing analysis scripts and simulation models by generating code from natural language and providing real-time suggestions, dramatically speeding up the core development process.

But modern research is more than just a single script; it’s a complete workflow of data, environments, and results managed from the command line. For this, Gemini CLI brings that same conversational power directly to your terminal. It acts as the ultimate workflow accelerator, allowing you to instantly synthesize research and generate hypotheses with simple commands, then seamlessly transition to experimentation by generating sophisticated analysis scripts, and debugging errors on the fly, all without ever breaking your focus. Gemini CLI can further accelerate your path to impact by transforming raw results into publication-ready text, generating the code for figures and tables, and refining your work for submission.

This capability extends to automating the entire research environment. Beyond single commands, Gemini CLI can manage complex, multi-step processes like cloning a scientific application, installing its dependencies, and then building and testing it—all with a simple prompt, maximizing your productivity.

The new era of discovery: Your expertise, AI agents, and Google Cloud

The new era of scientific discovery is here. By embedding AI into every stage of the scientific process - from sparking the initial idea to accelerating the final analysis - Google Cloud provides a single, unified platform for discovery. This new era of AI-enhanced scientific inquiry is built on a robust, intelligent infrastructure that combines the strengths of HPC simulation and AI. This includes purpose-built solutions like our H4D VMs optimized for scientific simulations, alongside the latest A4 and A4X VMs, powered by the latest NVIDIA GPUs, and Google Cloud Managed Lustre, a parallel file system that eliminates storage bottlenecks and allows your HPC and AI workloads to create and analyze massive datasets simultaneously. We provide the power to streamline the entire process so you can focus on scientific creativity - and changing the world!

Join the Google Cloud Advanced Computing Community to connect with other researchers, share best practices, and stay up to date on the latest advancements in AI for scientific and technical computing, or contact sales to get started today.

Top 25 blogs of 2025… so far

Wed, 30 Jul 2025 16:00:00 +0000

Six months into 2025, we’ve already published hundreds of posts here on the Google Cloud blog. We asked ourselves, why wait until the busy end of the year to review your favorites? With everything from new AI models, product launches, emerging cyber threats, company news, certifications and customer stories, here is a mid-year recap that will get you up to speed on the latest from Google Cloud and the rapidly emerging cloud and AI landscape.

25. How Google Does It: Making threat detection high-quality, scalable, and modern

Published January 7, 2025

Google and Alphabet run the largest Linux fleet in the world, with nearly every flavor of operating system available, and see a steady stream of malicious system and network activity. Learn how our threat detection and response team detects, analyzes, and responds to threats on a vast scale.

Read the blog.

24. Cloud Run GPUs are now generally available

Published June 2, 2025

More and more organizations are turning to Cloud Run, Google Cloud’s serverless runtime, for its simplicity, flexibility, and scalability. And now, with the general availability of NVIDIA GPUs on the platform, developers can choose Cloud Run for applications that require powerful graphics processing, like machine learning models.

Read the blog.

23. BigQuery emerges as autonomous data-to-AI platform

Published April 10, 2025

This is not your grandfather’s data warehouse. BigQuery is now an AI-native, multimodal, and agentic data-to-AI platform. The blog post provides an overview of the many new features and capabilities that went into this new designation, including new data preparation, data analysis, code generation and management and troubleshooting capabilities.

Read the blog.

22. Announcing Gen AI Toolbox for Databases. Get started today

Published February 6, 2025

Tired of building custom plumbing to connect your AI apps to your databases? This article announces the public beta of the Gen AI Toolbox for Databases, an open-source server built with LangChain that provides a secure, scalable, and manageable way to connect your generative AI applications to your data.

Read the blog.

21. Ghost in the router: China-nexus espionage actor UNC3886 targets Juniper Networks

Published March 11, 2025

After discovering in 2024 that threat actors deployed custom backdoors to Juniper Networks’ Junos OS routers, Mandiant worked with Juniper to investigate this activity and observed that the affected routers were running end-of-life hardware and software. Learn more about the threat and how to remediate it in your environment.

Read the blog.

20. What's new with AI Hypercomputer?

Published April 9, 2025

It’s a platform, it’s a system, it’s AI Hypercomputer, Google Cloud’s fully managed supercomputing system for running AI and HPC workloads. As discussed at Google Cloud Next 2025, AI Hypercomputer supports all the latest and greatest compute, networking and storage infrastructure, and its software layer helps AI practitioners and engineers move faster with open and popular ML frameworks. Finally, there’s a full suite of workload management and observability tools to help you manage the thing.

Read the blog.

19. Ipsos research shows why cloud certification matters — get certified with Google Cloud

Published February 25, 2025

Google Cloud partnered with Ipsos, the global research firm, to study the impact of cloud certifications on career advancement and achievement. For example, 8 out of 10 survey respondents said earning a recognized certificate helped them land a job faster and 75% believe they secured a higher salary through their certification.

Read the blog.

18. Connect globally with Cloud WAN for the AI Era

Published April 9, 2025

With 202 points of presence (PoPs), powered by over 2 million miles of fiber, 33 subsea cables, and backed by a 99.99% reliability SLA, Google's backbone network is, how do we put it? Vast. And with Cloud WAN, enterprises can now use it for their own wide area network (WAN) architectures.

Read the blog.

17. Expanding generative media for enterprise on Vertex AI

Published April 9, 2025

At Google Cloud Next 25, we announced powerful new creative controls for our generative media models on Vertex AI. Now you can edit video with in-painting and out-painting, use camera controls for dynamic shots, and even create custom voices for AI-powered narration with as little as 10 seconds of audio.

Read the blog.

16. Suspected China-nexus threat actor actively exploiting critical Ivanti Connect Secure vulnerability

Published April 3, 2025

Threat actors continue to target edge devices globally, leveraging deep device knowledge and using both zero-day and now n-day flaws. This activity aligns with the broader strategy that the Google Threat Intelligence Group has observed among suspected China-nexus espionage groups, who invest significantly in exploits and custom malware for critical edge infrastructure.

Read the blog.

15. Defending against UNC3944: Cybercrime hardening guidance from the frontlines

Published May 6, 2025

Who is UNC3944? A financially-motivated threat actor characterized by its persistent use of social engineering and brazen communications with victims. Mandiant provides guidance and strategies for hardening systems and defenses against the cybercrime group, offering practical steps to protect against their specific attack methods.

Read the blog.

14. MCP Toolbox for Databases (formerly Gen AI Toolbox for Databases)

Published April 22, 2025

Ready to build AI agents that can actually use your data? This article announces that our MCP Toolbox for Databases now supports the Model Context Protocol (MCP), making it easier than ever to connect your generative AI agents to enterprise data. With new support for the Agent Development Kit (ADK) and LangGraph, you can build powerful, stateful agents with intuitive code and connect them to your databases securely.

Read the blog.

13. Formula E's AI equation: A new Driver Agent for the next era of racing

Published March 25, 2025

As motorsport has grown in popularity, the ability of fans from diverse backgrounds to enter the cockpit has not always kept up. Formula E sought to level the course for aspiring drivers by creating an AI-powered Driver Agent; connected to a Formula E simulator, the agent provides drivers and coaches with real-time feedback on technique and tactics, help them improve faster than a flying lap.

Read the blog.

12. Google Agentspace enables the agent-driven enterprise

Published April 9, 2025

Do you want to search all your company’s information in a few clicks, or generate ideas with built-in agents that already know your company’s style? Google Agentspace now includes a no-code agent designer, a gallery for discovering agents, and two new expert agents for deep research and idea generation, all integrated directly into Chrome.

Read the blog.

11. Announcing Veo 3, Imagen 4, and Lyria 2 on Vertex AI

Published May 20, 2025

The next generation of creating for enterprise is here. We expanded Vertex AI to include our most powerful generative AI media models: Imagen 4 for stunningly realistic images with crisp text, Veo 3 for breathtaking video with synchronized audio, and Lyria 2 for composing high-fidelity, original music.

Read the blog.

10. Adversarial misuse of generative AI

Published January 19, 2025

In the security realm, large language models (LLMs) open a world of new possibilities, from sifting through complex telemetry to secure coding, vulnerability discovery, and streamlining operations. However, some of these same AI capabilities are also available to attackers, leading to understandable anxieties about the potential for AI to be misused for malicious purposes.

Read the blog.

9. Ivanti Connect Secure VPN targeted in new zero-day exploitation

Published January 8, 2025

Ivanti kicked off the year by disclosing two new vulnerabilities impacting its Ivanti Connect Secure (ICS) VPN appliances. Mandiant identified UNC5221, a suspected China-nexus espionage actor that previously exploited two other Ivanti vulnerabilities as early as December 2023, as the threat actor targeting the new zero-days. Successfully exploiting one of the vulnerabilities could result in downstream compromise of a victim network.

Read the blog.

8. Google announces agreement to acquire Wiz

Published March 18, 2025

Google Cloud shares a vision with Wiz to improve security by making it easier and faster for organizations of all types and sizes to protect themselves, end-to-end, across all major clouds, and this post announces Google's agreement to acquire the cloud security startup.

Read the blog.

7. Veo 3 available for everyone in preview on Vertex AI

Published June 26, 2025

You dream it, Veo creates it. This post announces Veo 3, our most powerful text-to-video model yet, is now open for everyone to try in public preview on Vertex AI. Create stunning, near-cinematic videos with synchronized sound, and join the next wave of creative storytelling, now available to Google Cloud customers and partners.

Read the blog.

6. Vertex AI offers new ways to build and manage multi-agent systems

Published April 9, 2025

This article announces ways to build multi-agentic systems, an evolution of traditional AI agents. To get there, we launched a new suite of tools in Vertex AI to help developers build and deploy them, including an open-source Agent Development Kit (ADK) and a managed Agent Engine. We also introduce the Agent2Agent (A2A) protocol, a new open standard to allow agents built by different companies to communicate and collaborate.

Read the blog.

5. Techniques for improving text-to-SQL

Published May 16, 2025

Even though it’s been around for a long time, not all developers speak fluent SQL. English, on the other hand, is pretty well-known. In this technical deep dive for developers working with natural language processing and databases, get the insights and techniques you need to enhance the accuracy and performance of your text-to-SQL conversions.

Read the blog.

4. Firebase Studio lets you build full-stack AI apps with Gemini

Published April 9, 2025

For over a decade, developers the world over have relied on Firebase’s backend cloud computing services and application development platforms to power their web applications. And with the new Firebase Studio, they can now use it to develop full-stack AI applications, integrating with the Gemini AI model.

Read the blog.

3. Multiple Russia-aligned threat actors targeting Signal Messenger

Published February 19, 2025

As part of the ongoing Russian-Ukrainian conflict, Signal Messenger accounts are of great interest to Russia's intelligence services for their potential to deliver sensitive government and military communications. Google Threat Intelligence Group has observed increasing efforts from several Russia state-aligned threat actors to compromise Signal Messenger accounts used by individuals of interest to Russia's intelligence services.

Read the blog.

2. New Google Cloud certification in generative AI

One of the top questions we hear is “how do I get ahead”? This isn't just another certification in a sea of technical qualifications. The Generative AI Leader certification is specifically focused on generative AI, and designed for visionary professionals like you — the managers, administrators, strategic leaders and more who understand that AI’s impact stretches far beyond code.

Read the blog.

1. 601 real-world gen AI use cases from the world's leading organizations

Published April 9, 2025

Since Next 2024, we’ve been gathering examples of how our customers are putting generative AI to use everyday across their operations and offerings. We nearly doubled the number of entries for Next 2025, and clearly they’re still resonating, as this has been our most popular story of the year. What use cases are most exciting you? Pop over to our LinkedIn page and let us know.

Read the blog.

Thank you for being a part of the Google Cloud blog community! We look forward to bringing you lots more blogs for you to devour in the second half of the year.

The overwhelmed person’s guide to Google Cloud: week of January 16

Tue, 21 Jan 2025 17:00:00 +0000

The content in this blog post was originally published last week as a members-only email to the Google Cloud Innovators community. To get this content directly in your inbox (not to mention lots of other benefits), sign up to be an Innovator today.

New and shiny

Three new things to know this week

Trillium TPUs are generally available. These powerful processors trained Gemini 2.0, and represent a breakthrough in price performance. Whether doing model training or inference, you’ll see some fantastic benefits. And TPU Trillium-based machines are now available for use in GKE Standard or Autopilot clusters.
AlloyDB Omni updated with key new features. I’ve got a few AlloyDB references in this newsletter edition, and won’t apologize for it. Run this powerful PostgreSQL database anywhere with AlloyDB Omni. It now features support for PostgreSQL 15.7, columnar engine on ARM, new caching capabilities, along with an updated Kubernetes operator.
Preview of new Parameter Manager service is now available. Where are you stashing configuration settings, environment-specific settings, and secrets used by your apps? We just added an extension to Secret Manager named Parameter Manager. This gives you a centralized store for app parameters, while still getting the encryption, versioning, and regional support from Secret Manager. Check it out!

Watch this

Learn about function calling with LLMs. How do you bring real-time data and activities into your LLMs? Aja and Jason tell you all about function calling. Don’t miss their other video on preparing data for LLMs!

Community cuts

Every week I round up some of my favorite links from builders around the Google Cloud-iverse. Want to see your blog or video in the next issue? Drop Richard a line!

Going from prototype to production with AI analysis. I like seeing posts from you all where you start with some experiments—here, in Vertex AI Studio—before turning your work into a deployable artifact. Robert does that here with some LLM prompting that turns into code deployed to Cloud Run.
Use Firebase Data Connect for easier relational database interactions. Have you seen this in action yet? It’s pretty rad, and makes storing and retrieving data simpler. Check out this post which gives you a thorough look at how it works.
Maybe AlloyDB is all you need. By all means, use BigQuery. But Danilo points out that AlloyDB is a viable choice for transactional and analytical workloads.

Learn and grow

Three ways to build your cloud muscles this week

There’s still time to register for the Google Cloud x MLB™ Hackathon! Major League Baseball is calling all developers to join the ultimate challenge: build groundbreaking fan experiences on Google Cloud. Win a chance to demo your creation during the Next developer keynote and even score a trip to the 2025 MLB® All-Star Game® presented by Mastercard. Step up to the plate and register today to compete.
Try out AlloyDB Omni’s columnar engine for analytical queries. This powerful feature of AlloyDB is also included in the run-anywhere AlloyDB Omni. Check out this codelab walks you through installing AlloyDB Omni on a Compute Engine VM and then testing out the columnar engine.
Build a RAG-capable app using Vertex AI services. This is a new architecture guide that I found useful for understanding the role of Vertex AI and Vector Search in a generative AI app. It includes diagrams, design considerations, and much more.
Choose between Managed Service for Apache Kafka and Pub/Sub. Both are great options for event-driven architectures. But this new guide helps you distinguish more between the two, and the ideal use cases.
Learn how to do supervised fine-tuning of an LLM. You might know when to do SFT, but do you know how? Erwin and Bethany have a detailed but digestible post that explains the process.

One more thing

https://x.com/OfficialLoganK/status/1876378894036783614

You can now “install” Google AI Studio as an app on desktop or mobile. Logan shares that Google AI Studio is now a progressive web app. I’ve got it as a dedicated app on my local machine now.

Become an Innovator to stay up-to-date on the latest news, product updates, events, and learning opportunities with Google Cloud.

What’s new with Google Cloud - 2024

Tue, 31 Dec 2024 17:00:00 +0000

Week of Dec 16 - Dec 20

Windows Server 2025 is now available on Google Compute Engine. We are excited to announce the general availability of Windows Server 2025 on Google Compute Engine. You can now run Windows Server 2025 Data Center, and Windows Server 2025 Data Center Core editions as well as Windows SQL Server 2022 on Windows Server 2025 with pay-as-you-go licenses. Customers that don’t already have a Microsoft Enterprise Agreement can use the Google Compute Engine’s provided image to take advantage of Google Cloud’s relationships with Microsoft for pay-as-you-go licenses that scale with your workload and offers premium support.
Google Agentspace is here: unlock enterprise expertise for employees with agents that bring together Gemini’s advanced reasoning, Google-quality search, and enterprise data, regardless of where it’s hosted. Google Agentspace makes your employees highly productive by helping them accomplish complex tasks that require planning, research, content generation, and actions – all with a single prompt.

Week of Dec 9 - Dec 13

Best of N: Generating High-Quality Grounded Answers with Multiple Drafts. We are excited to announce that Check Grounding API has released a new helpfulness score feature. Building on top of our existing groundedness score, we now enable users to implement Best of N to improve RAG response quality without requiring extensive model retraining!
A3 Ultra VMs powered by NVIDIA H200 Tensor Core GPUs and Hypercompute Clusters are in preview. A3 Ultra VMs offer a significant leap in performance over previous generations. Coupled with Hypercompute Cluster, which the infrastructure and workload provisioning, and ongoing operations of AI supercomputers up to tens of thousands of accelerators is seamless. This delivers a easy to manage, secure and high-performance cloud experience for AI workloads. Learn more about the offering!

Week of Nov 25 - Nov 29

The Cyber Threat Intelligence Program Design Playbook, Now Available. Mandiant Academy published a new on-demand course, Cyber Threat Intelligence (CTI) Program Design Playbook. Comprised of three 2-hour courses, this learning track explains Mandiant’s approach to design and build, efficiently operate, and enhance a CTI program. This is the inaugural track from the Academy’s updated approach to on-demand learning with succinct, operations-oriented lessons designed to provide the world’s cybersecurity professionals the answers and resources they need to succeed.

Week of Nov 11 - Nov 15

Subsea cable connectivity is coming to Tuvalu for the first time with the addition of the Tuvalu Vaka cable. Building on the Bulikula subsea cable system announced last year, this new network infrastructure is a collaboration among several partners including Australia, Japan, New Zealand, Taiwan, Tuvalu, Tuvalu Telecommunications Corporation and the United States, and will help reduce the digital divide in the Pacific.

Week of Nov 4-8

We are excited to announce the reCAPTCHA Password Leak Detection Container App, a new tool that makes it easier than ever to protect your users from account takeovers. This container app simplifies the integration of reCAPTCHA's powerful password leak detection, allowing you to instantly detect compromised credentials and proactively prompt users to change their password before their account is compromised. With pre-built libraries and a streamlined process, you can significantly reduce integration time and enhance your website's security with ease.

Week of Oct 21-25

We're excited to announce GA support for scanning: Rocky Linux, Alma, SUSE (SLES), Red Hat (UBI), Chainguard, Wolfi & Google Distroless. These operating systems are now supported in both Artifact Registry scanning, as well as On Demand Scanning.
When the Container Scanning API is enabled, any container with these new operating systems or distroless images will automatically be scanned for vulnerabilities when pushed to Artifact Registry. We've also upgraded our On Demand scan to include; NPM, Python, Ruby, Rust, .Net & PHP language packages. See all supported package types.
Term Extension Now Available for Compute Engine Committed Use Discounts: You can now extend the term length of your Compute Engine resource-based Committed Use Discounts (“CUDs”) beyond the preset 1-year and 3-year options. CUDs offer significant cost savings for predictable workloads. You can now choose a CUDs term length beyond the original commitment end date that perfectly aligns with your workload needs, from one year and one day up to 6 years. Learn more

Week of Oct 14-18

Announcing Google Cloud Marketplace private offer enhancements that enable additional payment flexibility for enterprises, including when transacting generative AI models.

Week of Oct 7-11

We are excited to announce the launch of new Google Cloud Cortex Framework data integration and analytics solution content for BigQuery and Looker with Oracle EBS data. To learn more read our announcement blog.
Google Cloud is partnering with leading AI and cybersecurity startups to accelerate their growth and innovation, through the ISV Startup Springboard program, announced this week at the Google Cloud Startup Summit. Learn more and register interest.
Privileged Access Manager (PAM) is now Generally Available. The GA release offers new capabilities in addition to recently released public preview and includes features such as Pub/Sub integration for custom alerting and monitoring, alerts on IAM grant modifications outside of PAM, and integration with VPC Service Controls to tackle data exfiltration. Learn more.

Week of Sept 23-27

We are excited to announce that registration is open for the App Dev & Infrastructure Summit on October 30 (AMER) and October 31 (EMEA). Google Technology Fellows - our luminary technical leaders - and industry experts will share strategies and learnings on how to improve efficiency, reduce costs, and speed up AI innovation for your cloud and application infrastructure at this global digital event. Register here.

Week of Sept 16-20

Starting this week, Google Cloud customers with eligible support plans can access assistance for the Cluster Toolkit through the Cloud Console. Cluster Toolkit, formerly known as Cloud HPC Toolkit, is open-source software offered by Google Cloud which simplifies the process for you to deploy HPC, AI and ML workloads on Google Cloud. The Cloud Support team will handle filed cases, ensuring that users receive timely and effective support for their Cluster Toolkit implementations. Select 'Cluster Toolkit' as the sub-category under 'Compute Engine' when creating a support ticket in your Cloud Console to get in touch about any Cluster Toolkit issues.
Backup and DR service is excited to announce the public preview of backup vaults and simplified VM backup offering. Backup vaults provide secure backups for cyber resilience through immutable and indelible backups for VMs and databases, delivering security against accidental or malicious data deletion. Simplified Compute Engine VM backup with a fully-managed experience, directly integrated into the cloud console makes backing of VMs as easy as 1-2-3. The solution also enables backup admins to empower application developers to self-protect their VMs while retaining centralized governance and oversight. Read the full blog to learn more and try out the new features.

Week of Sept 2-6

We’re excited to share that Topaz will be extended to Taiwan. Announced in 2022, the transpacific subsea cable system was the first to connect Canada and Japan. Now, with the extension of Topaz to Taiwan, we’ll provide the region with increased reliability and resilience for network operators, for Google, and for users.

Week of Aug 26-30

We are excited to announce the general availability of Instant snapshots for Google Compute Engine Persistent Disks, which provide near-instantaneous, high-frequency, point-in-time checkpoints of a disk that can be rapidly restored as needed. Read the full blog to try it out.
In response to customer and partner requests for pollen data in Japan, we are excited to announce that data for Japanese Cedar and Cypress trees-the 2 main sources of pollen allergens in Japan-have been added to our Pollen API from Google Maps Platform.

Week of Aug 19-23

We are excited to announce we’re adding support for NVIDIA L4 GPUs to Cloud Run, in preview. Developers love Cloud Run for its simplicity, fast autoscaling, scale-to-zero capabilities, and pay-per-use pricing. Those same benefits come into play for real-time inference apps serving open gen AI models. Check out this launch blog. Also watch demos from this launch event webinar Run AI on Cloud run.
We are excited to announce that Google Cloud Functions is now Cloud Run functions — event-driven programming in one unified serverless platform. This goes beyond a simple name change. We’ve unified the Cloud Functions infrastructure with Cloud Run, and developers of Cloud Functions (2nd gen) get immediate access to all new Cloud Run features, including NVIDIA GPUs. Read the launch blog and watch demos from this launch event webinar Run AI on Cloud run.

Week of Aug 5-9

Google’s Workforce Identity federation now enables Microsoft Entra ID users to access Google BigQuery from Microsoft Power BI with Single-Sign-On. No users or groups need to be provisioned in Google Cloud as Workforce identity Federation leverages a syncless federation capability using attribute based access control to authorize access to Google BigQuery using Microsoft Entra user attributes such as user group membership. You can refer to our documentation to learn more.
We are excited to announce the preview of SQL support in Bigtable to bring Google’s pioneering NoSQL database to a broader developer audience. Bigtable leverages GoogleSQL ─the same SQL dialect used by BigQuery─ making it easier to use Bigtable as low-latency analytics serving layer in combination with BigQuery’s newly announced continuous queries but does so with extensions to support its signature data model so you can use SQL without giving up on all the flexibility that comes with a NoSQL database. It also simplifies migrations from open source databases such as Apache Cassandra. With over 100 new functions from JSON processing capabilities, kNN for GenAI and HLL for real-time analytics, SQL opens the door to many new possibilities with Bigtable. Learn more in our detailed blog post.
We are excited to announce the public preview of BigQuery continuous queries, a groundbreaking new feature that empowers users to run continuously processing SQL statements that can process, analyze, and transform data as new events arrive in BigQuery, ensuring insights are always up to date. Native integration with the Google Cloud ecosystem unlocks the ability of Vertex AI and Gemini to perform machine learning inference on incoming data in real time. As well as streaming replication of continuous query results to Pub/Sub topics, Bigtable instances, or other BigQuery tables. Read the full blog and try it out!
AlloyDB’s AutoPilot capabilities- Automatic memory management, Adaptive AutoVacuum, Automatic storage tiering ,Automatic data columnarization and query rewrite- makes management super efficient and easy. AlloyDB eliminates the drudgery of maintaining a PostgreSQL database by using, behind the scenes , advanced self-tuning machine learning algorithm. In this blog we will look into a real world example of AlloyDB Adaptive AutoVacuum in work and how AlloyDB Cluster Storage Space is Released
Google Cloud Identity Platform, our consumer identity solution, now supports Passkeys. With Passkeys, developers can authenticate their app's end users securely, protecting them from account takeover attacks like phishing and leaked credentials. To join the private preview, contact your Google account team.

Week of July 15-19

Google Cloud is excited to launch the Modern SecOps Masterclass, now available on Coursera. This course equips security professionals with cutting-edge skills to modernize their Security Operations Centers (SOCs) using our Autonomic Security Operations framework and Continuous Detection, Continuous Response (CD/CR) methodology. Read the full blog and enroll now.
Learn how to potentially achieve a strong consistency in Cloud Bigtable for your next big data solution. Bigtable offers high throughput at low latency. It is ideal for storing large amounts of data in a key-value store while supporting high read and write throughput at low latency for fast access. Bigtable provides eventual consistency as well as strong consistency. This blog talks about achieving strong data consistency in a multi-cluster Bigtable instance. Read the full blog.

Week of June 24-28

Introducing Google Cloud Marketplace Channel Private Offers, enabling customers, ISV partners, and channel partners to efficiently transact private offers via reseller-initiated sales of third-party solutions listed on the Google Cloud Marketplace. This differentiated program also empowers channel partners to manage the customer relationship from billing, collections to revenue recognition. Read the full blog.
A blog on benchmark study (collaborated with Yahoo) by comparing the cost and performance of Apache Flink and Google Cloud Dataflow for two specific streaming data processing use cases. The goal of the study was to determine the most cost-effective platform for these use cases by establishing a fair comparison methodology and controlling variables such as throughput and workload. The results indicate that, with some optimization on Dataflow can perform on-par with Apache Flink. Read the full blog.
A Blog on Secure Gateways: Mutual TLS for Ingress Gateway Secure Gateways: Mutual TLS for Ingress Gateway," discusses the implementation of mutual TLS (mTLS) for enhanced security in ingress gateways. It explains how mTLS ensures both client and server authentication through certificates, going beyond the traditional server-only verification. The article explores the setup process and the benefits of using mTLS, emphasizing its role in establishing secure communication channels in modern cloud architectures. Read the full blog.
A Blog on Wildcard certificates with Ingress Gateway "Wildcard certificates with Ingress Gateway" provides a guide on how to use wildcard certificates to secure multiple services behind a single Istio Ingress Gateway. This simplifies certificate management and improves the user experience by allowing seamless connections across different services within the same domain. The article demonstrates the configuration process step-by-step and explains how wildcard certificates are matched to incoming requests. Read the full blog

Week of June 17-21

Learn how to leverage BigQuery vector search to analyze your logs and asset metadata stored in BigQuery. Using vector search, you can find semantically similar logs which can be helpful in several use cases such as outlier detection, triage and investigation. This how-to blog walks you through the setup from processing logs, generating vector embeddings, to analyzing vector search results. It includes sample SQL queries which can be adapted for your own logs and use case. Read the full blog.
Nuvem, first announced last year, is a transatlantic subsea cable system that will connect Portugal, Bermuda, and the United States. We are now working with the Regional Government of Azores to enable extending the system to the Azores as well. Named after the Portuguese word for “cloud,” Nuvem will improve network resiliency across the Atlantic, helping meet growing demand for digital services and further establishing its landing locations as digital hubs.

Week of June 10-14

General Availability of A3 Mega, a new instance type in the A3 VM family. A3 Mega is powered by the NVIDIA H100 Tensor Core GPU, delivers a 2.4x improvement in large scale training performance over multiple A3 instances.
- 2x the GPU-to-GPU networking bandwidth over A3 Instances.
- Enhanced GPUDirect-TCPXO networking offloads GPUDirect memory access from the CPU, providing direct access through through the NIC (Network Interface Card) to GPU memory, based on Titanium TOPs, which improves performance of multi-node distributed training workloads.
Simplify your Network: The Cloud Networking Product Management and Engineering team will be traveling across US cities in June/July and Sept. Learn how Cross-Cloud Network can transform your infrastructure. The workshop will address Cross-Cloud Networking for hybrid and multicloud enterprises with distributed applications, internet-facing content and applications, security, and AI-assisted network operations with Gemini Cloud Assist. Join us at one of the following Google office locations and meet the experts who will share the latest innovations, use cases, and demos. Register here.
Learn how you can leverage the cloud deployment archetypes (zonal, regional, multi-regional, global, hybrid, & multicloud) to architect cloud topologies that meet your workload’s requirements for reliability, cost, performance, & operational simplicity. Read the full blog.

Week of May 20-24

Maximize performance and optimize spend with Compute Engine’s latest General Purpose VMs, N4 and C4. N4's flexible configurations and price-performance gains help optimize costs, while C4 provides top-tier performance for demanding applications. With N4 and C4, you get tailored solutions for all your general-purpose workloads, so you can lower the total cost of running your business without compromising on performance or workload-specific requirements. Learn more here.

Week of Apr 22 - April 26

Simplify your connectivity to Google by using a Verified Peering Provider to connect to Google, instead of using Direct Peering. Verified Peering Providers handle all of the complex connectivity allowing you to focus on your core business. Learn more here.

Week of Apr 15- Apr 19

New training in AI, data analytics and cybersecurity, designed to expand onramps to tech careers through colleges and employers. Learn more.

Week of Apr 1- Apr 5

Security Command Center (SCC) Enterprise is now generally available (GA). It is the industry’s first cloud risk management solution that converges cloud security and enterprise security operations into a single platform, supercharged by Mandiant expertise and AI. Learn more in our announcement blog.
Identify common container runtime attacks, analyzes suspicious code, and use natural language processing to pinpoint malicious scripts with GKE threat detection, powered by Security Command Center. Now in public preview.
Get a fully managed compliance service that automatically delivers end-to-end coverage for GKE, scanning for issues against the most important benchmarks with GKE compliance, now in public preview. Near-real-time insights are available in a centralized dashboard, with compliance reports automatically produced for you.
Streamline your GCE backup strategy! With tag-based backups in Google Backup and DR, protection is automated – new VMs with the right tags are protected immediately, saving you time and increasing reliability. Read more on the blog here. Differential privacy enforcement with privacy budgeting is now available in BigQuery data clean rooms so organizations can prevent data from being reidentified when it is shared.

Week of Mar 18- Mar 22

Google Kubernetes Engine (GKE) and NVIDIA NeMo framework are used to train large language models (LLMs). Due to the increasing demand for efficient and scalable training of LLMs, the need for GPUs at a large scale with high speed networking is rapidly growing. GKE offers a comprehensive set of features that make it suitable for enterprise-level training and inference. This blog post shows how generative AI models can be adapted to your use cases by demonstrating how to train models on Google Kubernetes Engine (GKE) using the NVIDIA NeMo framework.
Cloud Run now supports volume mounts! Mount a Cloud Storage bucket or NFS file share as a volume to easily serve static assets, access app configuration data, or access an AI/ML model. Learn more in our blog post.

Week of Mar 11- Mar 15

Datastream adds support for SQL Server sources, now in preview. With existing support for MySQL, PostgreSQL, and Oracle, support for SQL Server sources extends the reach of Datastream and empowers you to replicate data from a range of relational sources to several Google Cloud services, such as BigQuery, Cloud Storage, AlloyDB, and Spanner. Read more in the blog here.

Week of Feb 5- Feb 9

Check out this new blog and learn more about the Integrated Commerce Network (ICN) delivered by Kin + Carta and built on Google Cloud. The ICN features 3 of our premier digital commerce partners for an integrated end-to-end solution including Bloomreach, commercetools and Quantum Metric.

Week of Jan 29- Feb 2

IDC finds 318% ROI from migrating to Google Cloud IaaS: Check out the latest IDC research study to learn how organizations worldwide are benefitting by adopting Google Cloud Infrastructure as a Service.

Week of Jan 15-19

Check out the latest generative AI training available from Google Cloud : Take a look at our top ten trainings in Duet AI to help boost your productivity in 2024.

Week of Jan 1-5

The year in Google Cloud: Top news of 2023: A look back at the biggest stories of 2023 from Google Cloud, covering generative AI, DevOps, containers, data and databases, security, and more.

The Year in Google Cloud - 2024

Fri, 20 Dec 2024 17:00:00 +0000

If you’re a regular reader of this blog, you know that 2024 was a busy year for Google Cloud. From AI to Zero Trust, and everything in between, here’s a chronological recap of our top blogs of 2024, according to readership. You’ll probably see some favorites on this list, and discover a few that you missed the first time around.

January

We started the new year strong, removing data transfer fees for anyone moving data off of our platform. Translation: Anyone doing cool things on Google Cloud (like using generative AI to analyze their microservices deployment) is doing it because they want to, not because they have to. And in business news, we shared how to make the most out of your data and AI in the coming year.

February

From local GPUs, to model libraries, to distributed system design, the second month of 2024 was the first of many to come where AI topics dominated the charts. Our Transform team explored gen AI’s impact on various industries.

March

If it wasn’t already obvious, this month’s top-read blogs showed that our core audience is developers pushing the boundaries of innovation. Business leaders, meanwhile, read about best practices for securely deploying AI on Google Cloud.

April

Watch out, here comes Google Cloud Next, where we made a record 218 announcements; the top three are listed here. Readers were also keen to hear about how Citadel Securities built out a research platform on Google Cloud.

May

We don’t always get it right, but when there’s a problem, we’re committed to providing you with accurate, timely information with which to make your own assessments. We’re also committed to making you, and customers like McLaren Racing, go really, really fast when developing new AI-based applications.

June

Whether you wanted to modernize your databases, deliver higher system reliability, create really cool AI-powered apps, or learn how legendary companies tackle data management, the Google Cloud blog was your go-to source midway through the year.

July

We talk a lot about “meeting customers where they are.” Sometimes that means a disaster zone, a remote research station, or a truck cruising down the highway. Over on Transform, you read about the history of our custom Axion and TPU chips.

August

Just when you thought you knew how to run AI inference, your available graph database options, or the name of Google Cloud’s event-driven programming service, we went and changed things up. We like to keep you on your toes ;) And business readers got a first look at AI agents — more to come on this.

September

You’ve been generating (and storing) business data for years. Now, we’re making it easier for you to make sense of, and actually use, that data. Speaking of using data, the Transform team compiled a jaw-dropping list of the real-world ways customers are using gen AI in their organizations.

October

According to this month’s most-read blog, 75% of you rely on AI for at least one daily professional responsibility, including code writing, information summarization, and code explanation, and experience “moderate” to “extreme” productivity gains. So it was no big surprise that business leaders wanted to read about how to develop an AI strategy.

November

Not content to hold the existing record for most nodes in a Kubernetes cluster (15,000), we went ahead and more than quadrupled it, to the delight of AI unicorns. But whether you work for an AI unicorn, or just a plain old zebra, all Google Cloud users needs to start using multi-factor authentication next year, as well as learn how to avoid comman AI pitfalls.

December

We’re closing out the year on an AI highnote, with the availability of amazing new image and video generation models, as well as the new Trillium TPU, which Google used to train Gemini 2.0, our most capable AI model… yet. Be on the lookout for how these technologies — and many others — will reshape your industry and how you work in the coming year.

To all our loyal readers and customers, thanks for an amazing 2024, and here’s to an even more exciting and productive 2025!

AI Playground: Where learning and innovation converge in the heart of London

Mon, 16 Dec 2024 18:00:00 +0000

AI is rapidly transforming industries and redefining the future of work. However, many organizations face a significant hurdle: bridging the knowledge gap and acquiring the necessary skills to effectively harness the power of AI.

Recognizing this challenge, Google Cloud is set to launch the AI Playground in Shoreditch, Central London, in the first quarter of 2025. This innovative space will serve as a dynamic hub for businesses and individuals to demystify AI, explore its potential, and develop practical expertise.

More than a showcase — dive deep into AI

Google’s powerful Gemini model family — now in its supercharged second generation — takes center stage at the AI Playground, featuring several interactive demos that put its multimodal and agentic capabilities on display. Among the capabilities guests can experience and experiment with first hand are Gemini’s ability to analyze complex data, generate creative formats, and power innovative solutions.

The AI Playground is much more than a technology showcase.

We’ve built the space to serve as an immersive learning environment where visitors can actively engage with AI, participate in hands-on workshops and hackathons, and connect with Google Cloud AI experts. This unique approach fosters a deeper understanding of AI concepts and encourages experimentation with cutting-edge tools and techniques.

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Addressing the AI skills gap

The AI Playground directly addresses the growing need for AI skills development in today's rapidly evolving technological landscape, providing a dedicated space for:

Hands-on experimentation: Gain practical experience with cutting-edge AI tools and techniques, moving beyond theoretical knowledge to real-world application.
Skills development: Build on-demand AI skills through interactive workshops and hackathons led by Google Cloud experts, equipping individuals and teams with the expertise needed to thrive in the AI era.
Community building: Connect with fellow AI enthusiasts, share knowledge, and facilitate collaboration, creating a vibrant ecosystem for learning and innovation.
Real-world inspiration: Explore AI applications across diverse industries and discover new possibilities for your organization, sparking creativity and driving the development of novel AI solutions.

Empowering the future of AI

By providing accessible learning opportunities and fostering a thriving AI community, the AI Playground empowers individuals and businesses to embrace the transformative power of AI and contribute to shaping a better future. It's a place where curiosity meets innovation, learning is hands-on, and the potential of AI is unlocked. Mark your calendars for Q1 2025 and prepare to embark on your AI journey at Google Cloud's AI Playground.

London Summit: UK businesses turn to Google Cloud AI

Wed, 09 Oct 2024 10:00:00 +0000

The AI era is here, and the UK is at the forefront.

Over the past year, search interest for "AI" has surged by 50% in the country, while inquiries about "how to use AI" have jumped 70%. By 2030, AI could generate more than £400 billion ($524 billion) in value for the UK economy and save more than 700,000 administrative hours annually across sectors like healthcare, education, and finance. Central to this transformation is Gemini, Google Cloud's next-generation AI foundation model, which is empowering UK businesses across all sectors to unlock new possibilities in a rapidly evolving landscape.

This week, the energy is palpable, as we welcome customers and partners to the Google Cloud Summit London at Tobacco Dock to experience how Gemini is helping shape the future of business and unlocking business value. The summit also showcases innovations from across Google Cloud’s extensive portfolio of products and services, as well as from our ecosystem of customers and partners — illustrating how AI is transforming different industries and equipping UK businesses to stay ahead in a competitive landscape.

And what a way to kick off the summit than with yesterday’s announcement of Google’s deepening partnership with Vodafone. This expands beyond our longstanding work together in the cloud, with a 10-year, billion-plus-dollar deal that includes cybersecurity, devices, and cloud services across Europe and Africa. As Google’s CEO, Sundar Pichai, noted: “I’m excited to see how Vodafone’s consumers, small businesses, and governments will use generative AI and Google Cloud to transform the way they work and access information.”

I’m also excited to further support our customers' growth with Google Cloud's $1 billion investment in a new UK data center in Waltham Cross, Hertfordshire. Unveiled in January, this major project reinforces our commitment to AI innovation and delivering reliable digital services across Europe and the world. We’re also fueling this digital growth with key updates and announcements made today from both Google Cloud and our customers.

Empowering customers with data and AI solutions

The momentum behind AI is undeniable — nearly two-thirds of UK organizations have earmarked at least half of their AI budget for generative AI. These tools have the potential to generate £4.8 billion in productivity savings, according to research from Public First.

Today, we are proud to highlight leading UK companies who are already harnessing Gemini and Google Cloud AI capabilities to drive real-world impact:

BUPA UK is using Google Cloud to build a cloud-first digital customer platform providing direct access to care when needed, as well as the tools to enable proactive health outcomes for customers. This new digital platform will allow BUPA to develop innovative ways to improve members’ wellbeing, reducing the cost of care all while delivering a highly personalized healthcare experience.
Dunelm has partnered with Google Cloud to enhance its online shopping experience with a new gen AI-driven product discovery solution. This has shown significant improvements in a number of key areas, including reduced search friction, helping customers find the products they are looking for.
Incubeta is using Gemini for Google Workspace, primarily with their development teams, to streamline architecture documentation and code refinements, and to reduce time spent on these tasks. They also use it for client teams to assist with mundane tasks and for creative conception.
Vodafone — in addition to its 10-year expanded partnership with Google — also recently completed the migration of its SAP ERP resource planning database to Google Cloud. One of the largest shifts of its kind, it was completed with zero business interruptions. At the summit, the team will be sharing more about this work, which boosted performance while shrinking costs and Vodafone’s carbon footprint.

Data residency for machine learning available in the UK

We understand the importance of data sovereignty for certain industries. That's why Google Cloud is expanding our data residency commitment, enabling UK organizations to run machine learning processing for Gemini 1.5 Flash within the UK.

This effort helps address key data sovereignty and compliance concerns that enable UK organizations, including the public sector, to leverage gen AI while maintaining strict control over their data.

Fueling the next generation of UK and EMEA startups

We are also excited to share that since 2023, more than 60% of UK-based gen-AI startups are Google Cloud customers. Yesterday, at our global Startup Summit, we announced several important updates that are relevant for UK AI startups. These include our Startup School for AI skill-building and the ISV Startup Springboard offering extensive go-to-market and co-selling opportunities with Google Cloud and our partners. We’ve also launched a partnership with the startup accelerator 500 Global to provide streamlined access to Google Cloud’s AI platform

As part of today’s summit, we’re launching the Google Cloud Startup Hub in London — a dedicated community space designed to provide in-person education and engagement for startups and developers. Open five days a week with business hours from 9 a.m. to 6 p.m. or until 9 p.m. to accommodate evening events, the hub offers hands-on learning for Google Cloud and partner solutions and access to experts.

At the hub, we will host exclusive sessions with industry leaders and facilitate invaluable networking opportunities with investors and peers. The Google Cloud Startup Hub also serves as a vital community resource where key partners can also host events.

Some examples of how UK startups are driving breakthroughs with Google Cloud are:

BioCorteX, in collaboration with Google Cloud, announces a breakthrough in ADC research for cancer treatment using its “Unified Biology” approach. The research has uncovered a crucial link between the tumor microenvironment and ADC efficacy, potentially transforming personalized cancer therapy.
Motorway uses Google Cloud AI to streamline the process of buying and selling used cars online. With Google Cloud's AI, Motorway has been able to build and deploy AI models faster, automate document processing, and provide industry-leading vehicle valuations.
OnBuy, a fast-growing ecommerce marketplace, partners with Google Cloud for international expansion and enhanced customer experience. Google Cloud's infrastructure, data storage, and AI tools enable OnBuy to scale, improve efficiency, and innovate.

We’re also announcing the opening of the new AI Playground, located within the same space as the Google Cloud Startup Hub. An experiential AI demo space, the AI Playground is aimed at inspiring and empowering developers and organizations to build incredible new applications and solve business challenges with AI and ML.

Google Cloud's commitment to making data and AI more accessible and powerful for all users

To help enhance our overall data platform, we’re also sharing a number of important product updates and announcements. These will deliver better insights and efficiencies for our customers and boost the performance of their AI offerings and projects. Data is the foundation of any AI work, so without having a handle on the former, it’s hard to succeed in the latter.

BigQuery, our unified enterprise gen AI-ready data platform, has now been integrated with our Gemini models. Gemini in BigQuery makes data prep easier, offering an intuitive natural-language interface that can help data teams generate insights from metadata and other large datasets, and it’s especially useful for new batches of data.

We also have new synthetic data capabilities, with BigQuery Dataframes, which make it easier to run and train models when inputs are more limited. Gemini in Looker has added conversational analytics, which lets you use a search experience similar to those found across Google; this allows for data exploration using natural language that surfaces actionable insights more easily.

For enhanced security, BigQuery now supports cross-region disaster recovery with multi-factor authentication, ensuring business continuity and data protection.

New managed BigQuery workflows assist data engineers in building data pipelines. A unified data catalog in BigQuery helps organize and manage all your data and metadata, making it easier to discover and use, while BigQuery catalog semantic search, available in preview, expands the ability to find data using natural language. These search features make BigQuery more intuitive and accessible to everyone.

Google is also launching a new enterprise tier of Code Assist, our AI-powered coding partner, which can help teams with work throughout the software development lifecycle. This enterprise tier will offer enhanced security, improved context for better accuracy and reliability, and wider integration with Google Cloud services.

With these investments and the customer momentum showcased today, Google Cloud is reinforcing the UK’s role in shaping the future of AI and strengthening the future we can build together on the cloud.

Browse upcoming Summit events & sign up!

Your ultimate guide to the latest in generative AI on Vertex AI

Thu, 26 Sep 2024 17:00:00 +0000

The world of generative AI is evolving at a pace that's nothing short of mind-blowing. It feels like just yesterday we were marveling at AI-generated images, and now we're having full-fledged conversations with AI chatbots that can write code, craft poetry, and even serve our customers (check out our list of 101 real-world gen AI use cases from the world's leading organizations).

The pace of innovation can be hard to keep up with — in 2023 we introduced over 500 new features in Vertex AI and we’re not slowing down this year. We put this blog together to help you keep track of the biggest announcements and make sense of what they mean for your business. We’ll keep it updated as new announcements come out, so bookmark this link and check out our new video series below also covering new announcements.

Catch up on the latest announcements

As part of our Gemini at Work global event, we showcased nearly 50 new customer stories from organizations around the world to highlight just how impactful generative AI can be when you put it to work at scale.

We’re inspired by what customers are building and excited to announce a few exciting updates to our models and platform, designed to help move gen AI from experimentation into production with Vertex AI.

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Updates to Gemini Models

What it is: The newly updated versions of Gemini 1.5 Pro and Flash models, both GA, deliver quality improvements in math, long context understanding, and vision.
Why it matters: Our objective is to bring you the best models suited for enterprise use cases, by pushing the boundaries across performance, latency, and costs. From a latency standpoint, the new version of Gemini 1.5 Flash is nearly 2.5x faster than GPT-4o mini.
Get started: Access Gemini 1.5 Pro and Flash in the Google Cloud console.

Reduced Gemini 1.5 Pro Pricing

What it is: We are reducing costs of Gemini 1.5 Pro by 50% across both input and output tokens, effective on Vertex AI on October 7, 2024.
Why it matters: We are committed to making AI accessible for every enterprise. In August, we improved Gemini 1.5 Flash to reduce costs by up to 80% (see below). These world class models can be coupled with capabilities like context caching to even further reduce the cost and latency of your long context queries. Using Batch API instead of standard requests can further optimize costs for latency insensitive tasks.
Get started: Visit the pricing page to learn more.

Updates to Imagen 3

What it is: Google’s latest image generation model, delivering outstanding image quality, multi-language support, built-in safety features like Google DeepMind’s SynthID digital watermarking, and support for multiple aspect ratios.
Why it matters: There are several improvements over Imagen 2 — including over 40% faster generation for rapid prototyping and iteration; better prompt understanding and instruction-following; photo-realistic generations, including of groups of people; and greater control over text rendering within an image.
Get started: Apply for access to Imagen 3 on Vertex AI.

Controlled generation is now GA

What it is: Controlled generation lets customers define Gemini model outputs according to specific formats or schemas.
Why it matters: Most models cannot guarantee the format and syntax of their outputs, even with specified instructions. Vertex AI controlled generation lets customers choose the desired output format via pre-built options like JSON and ENUM, or by defining custom formats.
Get started: Visit documentation to learn more.

Batch API

What it is: Batch API (currently in preview) is a super-efficient way to send large numbers of non-latency sensitive text prompt requests, supporting use cases such as classification and sentiment analysis, data extraction, and description generation.
Why it matters: It helps speed up developer workflows and reduces costs by enabling multiple prompts to be sent to models in a single request.
Get started: View documentation to get started.

Supervised fine tuning (SFT) for Gemini 1.5 Flash and Pro

What it is: SFT for Gemini 1.5 Flash and Pro is now generally available. SFT adapts model behavior with a labeled dataset, adjusting the model's weights to minimize the difference between its predictions and the actual labels.
Why it matters: SFT allows you to tune the model to be more precise for your enterprise task. It's particularly effective for domain-specific applications where the language or content significantly differs from the data the large model was originally trained on.
Get started: Visit documentation to learn more.

Distillation techniques in Vertex AI

What it is: Train smaller, specialized models that inherit the knowledge of the larger Gemini model, achieving comparable performance with the flexibility of self-hosting your custom model on Vertex AI.
Why it matters: Deploying large language models can be a resource-intensive challenge. With distillation techniques in Vertex AI, you can leverage the power of those large models while keeping your deployments lean and efficient.
Get started: Fill out this form for early access.

Prompt Optimizer, now in preview

What it is: Based on Google Research’s publication on automatic prompt optimization (APO) methods, Prompt Optimizer adapts your prompts using the optimal instructions and examples to elicit the best performance from your chosen model.
Why it matters: Vertex AI’s Prompt Optimizer helps you avoid the tedious trial-and-error of prompt engineering. Plus, our prompt strategies guide helps you make the models more verbose and conversational.
Get started: Learn more in documentation.

Prompt Management SDK

What it is: Vertex AI’s Prompt Management SDK allows users to retrieve and organize prompts. It lets you version prompts, restore old prompts, and generate suggestions to improve performance.
Why it matters: This makes it easier for you to get the best performance from gen AI models at scale, and to iterate more quickly from experimentation to production.
Get started: The prompt management SDK will be generally available in the coming weeks.

Multimodal function calling

What it is: Function calling is a built-in feature of the Gemini API that translates natural language into structured data and back.
Why it matters: Now, with multimodal function calling, your agents can also execute functions where your user can provide images, along with text, to help the model pick the right function and function parameters to call.
Get started: Learn more in documentation.

Org policy for models in Model Garden

What it is: You can now govern the models available in Model Garden. This includes limiting access to gen AI features, to only specific vetted models, or to tuning and other advanced capabilities. Org policies can be applied to all models in Model Garden as well as those imported from Hugging Face through Model Garden. These policies can be set on an organization, folder or project resource to enforce the constraint on that resource and any child resources.
Why it matters: The ability to control access to the models made available through the Model Garden has been a top priority for many customers looking for governance and control tooling. Org Policies allows granular access control of models and enhances Google Cloud’s enterprise readiness in an age of many models from many providers.
Get started: Learn more in documentation.

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Previous Announcements

Best models from Google and the industry

We’re committed to providing the best model for enterprises to use - Vertex AI Model Garden provides access to 150+ models from Google, Partners and the open community so customers can select the model for the right price, performance, and latency considerations.

No matter what foundation model you use, it comes with enterprise ready tooling and integration to our end to end platform.

Gemini 1.5 Flash is GA

What it is: Gemini 1.5 Flash combines low latency, highly competitive pricing, and our 1 million-token context window.
Why it matters: Gemini 1.5 Flash is an excellent option for a wide variety of use cases at scale, from retail chat agents, to document processing, to research agents that can synthesize entire repositories.
Get started: Click here to get started now with Gemini 1.5 Flash on Vertex AI.

Lower pricing for Gemini 1.5 Flash

What it is: We've updated Gemini 1.5 Flash to reduce the input costs by up to ~85% and output costs by up to ~80%, starting August 12th, 2024.
Why it matters: This is a big price drop on Gemini Flash, a world-class model with a 1 million context window and multi-modal inputs. Plus, coupled with capabilities like context caching you can significantly reduce the cost and latency of your long context queries. Using Batch API instead of standard requests can further optimize costs for latency insensitive tasks.
Get started: View pricing to learn more and try out Gemini 1.5 Flash today.

Gemini 1.5 Pro, GA with 2-million -token context capabilities

What it is: Now available with an industry-leading context window of up to 2 million tokens, Gemini 1.5 Pro is equipped to unlock unique multimodal use cases that no other model can handle.
Why it matters: Processing just six minutes of video requires over 100,000 tokens and large code bases can exceed 1 million tokens — so whether the use case involves finding bugs across countless lines of code, locating the right information across libraries of research, or analyzing hours of audio or video, Gemini 1.5 Pro’s expanded context window is helping organizations break new ground.
Get started: Click here to get started now

More Languages for Gemini

What it is: We're enabling Gemini 1.5 Flash and Gemini 1.5 Pro to understand and respond in 100+ languages.
Why it matters: We’re making it easier for our global community to prompt and receive responses in their native languages.
Get started: View documentation to learn more.

Gemma 2

What it is: The next generation in Google’s family of open models built to give developers and researchers the ability to share and commercialize their innovations, using the same technologies used to create Gemini.
Why it matters: Available in both 9-billion (9B) and 27-billion (27B) parameter sizes, Gemma 2 is much more powerful and efficient than the first generation, with significant safety advancements built in.
Get started: Access Gemma 2 on Vertex AI here.

Meta’s Llama 3.1

What it is: Llama 3.1 models are now available on Vertex AI as a pay as you go API, this includes 405B, 70B and 8B (coming in early September).
Why it matters: 405B is the largest openly available foundation model to date. 8B and 70B are also new versions that excel at understanding language nuances, grasping context, and performing complex tasks such as translation and dialogue generation. You can access the new models in just a few clicks using Model-as-a-Service, without any setup or infrastructure hassles.
Get started: To access Llama 3.1, visit Model Garden

Mistral AI’s latest models

What it is: We added Mistral Large 2, Nemo and Codestral (Google Cloud is the first hyperscaler to introduce Codestral).
Why it matters: Mistral Large 2 is their flagship model which offers their best performance and versatility to date and Mistral Nemo is a 12B model that delivers exceptional performance at a fraction of the cost. Codestral is Mistral AI’s first open-weight generative AI model explicitly designed for code generation tasks. You can access the new models in just a few clicks using Model-as-a-Service, without any setup or infrastructure hassles.
Get started: To access the Mistral AI models, visit Model Garden (Codestral, Large 2, Nemo) or check out the documentation

Jamba 1.5 Model Family from AI21 Labs

What it is: Jamba 1.5 Model Family — AI21 Labs’ new family of open models — is in public preview on Vertex AI Model Garden, including:

Jamba 1.5 Mini: AI21’s most efficient and lightweight model, engineered for speed and efficiency in tasks including customer support, document summarization, and text generation.
Jamba 1.5 Large: AI21’s most advanced and largest model that can handle advanced reasoning tasks — such as financial analysis — with exceptional speed and efficiency.

Why it matters: AI21’s new models join over 150 models already available on Vertex AI Model Garden, further expanding your choice and flexibility to choose the best models for your needs and budget, and to keep pace with the continued rapid pace of innovation.
Get started: Select the Jamba 1.5 Mini or Jamba 1.5 Large model tile in Vertex AI Model Garden.

Anthropic’s Claude 3.5 Sonnet

What it is: We recently added Anthropic’s newly released model, Claude 3.5 Sonnet, to Vertex AI. This expands the set of Anthropic models we offer, including Claude 3 Opus, Claude 3 Sonnet, Claude 3 Haiku. You can access the new models in just a few clicks using Model-as-a-Service, without any setup or infrastructure hassles.
Why it matters: We're committed to empowering customer choice and innovation through our curated collection of first-party, open, and third-party models available on Vertex AI.
Get started: Begin experimenting with or deploying in production Claude 3.5 Sonnet on Vertex AI.

End-to-end model building platform with choice at every level

Vertex AI Model Builder enables you to build or customize your own models, with all the capabilities you need to move from prototype to production.

Lower cost with context caching for both Gemini 1.5 Pro and Flash

What it is: Context caching is a technique that involves storing previous parts of a conversation or interaction (the "context") in memory so that the model can refer back to it when generating new responses
Why it matters: As context length increases, it can be expensive and slow to get responses for long-context applications, making it difficult to deploy to production. Vertex AI context caching helps customers significantly reduce input costs, by 75 percent, leveraging cached data of frequently-used context. Today, Google is the only provider to offer a context caching API.
Get started: Learn more in documentation.

New model monitoring capabilities

What it is: The new Vertex AI Model Monitoring includes

Support for models hosted outside of Vertex AI (e.g. GKE, Cloud Run, even multi-cloud & hybrid-cloud)
Unified monitoring job management for both online and batch prediction
Simplified configuration and metrics visualization attached to the model, not the endpoint

Why it matters: Vertex AI’s new model monitoring features provide a more flexible, extensible, and consistent monitoring solution for models deployed on any serving infrastructure (even outside of Vertex AI, e.g. Google Kubernetes Engine, Cloud Run, Google Compute Engine and more).
Get started: Learn more in this blog.

Ray on Vertex AI is GA

What it is: Ray provides a comprehensive and easy-to-use Python distributed framework. With Ray, you configure a scalable cluster of computational resources and utilize a collection of domain-specific libraries to efficiently distribute common AI/ML tasks like training, serving, and tuning.
Why it matters: This integration empowers AI developers to effortlessly scale their AI workloads on Vertex AI's versatile infrastructure, which unlocks the full potential of machine learning, data processing, and distributed computing.
Get started: Ready the blog to learn more.

Prompt Management

What it is: Vertex AI Prompt Management, now in preview, provides a library of prompts for use among teams, including versioning, the option to restore old prompts, and AI-generated suggestions to improve prompt performance.
Why it matters: This feature makes it easier for organizations to get the best performance from gen AI models at scale, and to iterate more quickly from experimentation to production. Customers can compare prompt iterations side by side to assess how small changes impact outputs, and the service offers features like notes and tagging to boost collaboration.
Get started: Visit documentation to learn more.

Gen AI Evaluation Services

What it is: We now support Gen AI Evaluation in GA to help users evaluate any generative model or application. While leaderboards and reports offer insights into overall model performance, they don't reveal how a model handles your specific needs. The Gen AI Evaluation Service helps you define your own evaluation criteria, ensuring a clear understanding of how well generative AI models and applications align with your unique use case. Users can define their own evaluation metrics and also access our templates for metrics with various criteria (e.g. text quality, instruction following, fluency).
Why it matters: Evaluation is important at every step of your gen AI development process including model selection, prompt engineering, and model customization. Evaluating gen AI is integrated within Vertex AI to help you launch and reuse evaluations as needed.
Get started: Learn more in documentation.

Develop and deploy agents faster, grounded in your enterprise truth

Vertex AI Agent Builder allows you to easily and quickly build and customize AI Agents - for any skill level. A core component of the Vertex AI Agent Builder is Vertex AI Search, enabling you to ground the models in your data or the web.

Grounding at Vertex

You have many options for Grounding and RAG at Vertex. These capabilities address some of the most significant hurdles limiting the adoption of generative AI in the enterprise: the fact that models do not know information outside their training data, and the tendency of foundation models to “hallucinate,” or generate convincing yet factually inaccurate information. Retrieval Augmented Generation (RAG), a technique developed to mitigate these challenges, first “retrieves” facts about a question, then provides those facts to the model before it “generates” an answer – this is what we mean by grounding. Getting relevant facts quickly to augment a model's knowledge is ultimately a search problem.

Built on a foundation of scale & enterprise readiness

The revolutionary nature of generative AI requires a platform that offers privacy, security, control, and compliance capabilities organizations can rely on. Google Cloud is committed to helping our customers leverage the full potential of generative AI with privacy, security, and compliance capabilities. Our goal is to build trust by protecting systems, enabling transparency, and offering flexible, always-available infrastructure, all while grounding efforts in our AI principles.

Dynamic Shared Quota

What it is: With Dynamic Shared Quota, we offer increasing the quota limits for a model (online serving) to the maximum allowed per region. This way we limit the number of queries per second (QPS) that customers can run by the shared capacity of all the queries running on a Servo station (multi-region), instead of limiting a customer’s QPS by a quota. Dynamic Shared Quota is only applicable to Pay-as-you-go Online Serving. For customers that require a consistent or more predictable service level, including SLAs, we offer Provision Throughput.
Why it matters: By dynamically distributing on-demand capacity among all queries being processed for Pay-as-you-go customers, Google Cloud has eliminated the need to submit quota increase requests (QIRs). Customers can still set a self-imposed quota called a consumer quota override to control cost and prevent budget overruns.
Get started: Learn more in documentation.

Provisioned Throughput is GA

What it is: Provisioned throughput lets customers responsibly scale their usage of Google’s first-party models, like 1.5 Flash, providing assurances for both capacity and price.
Why it matters: This Vertex AI feature brings predictability and reliability to customer production workloads, giving them the assurance required to scale gen AI workloads aggressively. We have also made it easier than ever for customers to set up PT via a Self Service flow. Customers can now estimate their needs and purchase Provisioned Throughput for Google’s 1P foundation models via the console, bringing the E2E experience down from weeks to minutes for pre-approved orders subject to available capacity and removing the need for manual order forms.
Get started: Follow these steps to purchase a Provisioned Throughput subscription.

Data residency for data stored at-rest guarantees in more countries

What it is: We have data residency for data stored at-rest guarantees in 23 countries (13 of which were added in 2024), with additional guarantees around limiting related ML processing to the US and EU. We are also working on expanding our ML processing commitments to eight more countries, starting with four countries in 2024.
Why it matters: Customers, especially those from regulated industries, demand control over where their data is stored and processed when using generative AI capabilities.
Get started: Learn more here.

To keep up with all of the latest releases, don’t forget to check our Vertex AI release notes.

All of these enhancements are a direct response to what you, our customers, have been asking for. We believe an enterprise AI platform is key to success in production and our goal is to not just build the best platform, but to provide an AI ecosystem that makes enterprise-scale AI accessible.

To learn about how Vertex AI can help you, contact us for a free consultation.

Enhancing LLM quality and interpretability with the Vertex AI Gen AI Evaluation Service

Mon, 29 Jul 2024 23:00:00 +0000

Developers harnessing the power of large language models (LLMs) often encounter two key hurdles: managing the inherent randomness of their output and addressing their occasional tendency to generate factually incorrect information. Somewhat like rolling dice, LLMs offer a touch of unpredictability, generating different responses even when given the same prompt. While this randomness can fuel creativity, it can also be a stumbling block when consistency or factual accuracy is crucial. Moreover, the occasional "hallucinations" – where the LLM confidently presents misinformation – can undermine trust in its capabilities. The challenge intensifies when we consider that many real-world tasks lack a single, definitive answer. Whether it's summarizing complex information, crafting compelling marketing copy, brainstorming innovative product ideas, or drafting persuasive emails, there's often room for multiple valid solutions.

In this blog post and accompanying notebook, we'll explore how to tackle these challenges by introducing a new workflow which works by generating a diverse set of LLM-generated responses and employing the Vertex Gen AI Evaluation Service to automate the selection process of the best response and provide associated quality metrics and explanation. This process is also extensible to multimodal input and output and stands to benefit almost all use cases across industries and LLMs.

Picture this: a financial institution striving to summarize customer conversations with banking advisors. The hurdle? Ensuring these summaries are grounded in reality, helpful, concise, and well-written. With numerous ways to craft a summary, the quality varied greatly. Here is how they leveraged the probabilistic nature of LLMs and the Vertex Gen AI Evaluation Service to elevate the performance of the LLM-generated summaries.

Step 1: Generate Diverse Responses

The core idea here was to think beyond the first response. Causal decoder-based LLMs have a touch of randomness built in, meaning they sample each word probabilistically. So, by generating multiple, slightly different responses, we boost the odds of finding a perfect fit. It's like exploring multiple paths, knowing that even if one leads to a dead end, another might reveal a hidden gem.

For example, imagine asking an LLM, "What is the capital of Japan?" You might get a mix of responses like "Kyoto was the capital city of Japan," "Tokyo is the current capital of Japan," or even "Tokyo was the capital of Japan." By generating multiple options, we increase our chances of getting the most accurate and relevant answer.

To put this into action, the financial institution used an LLM to generate five different summaries for each transcript. They adjusted the LLM's "temperature," which controls the randomness of output, to a range of 0.3 to 1.0, to encourage just the right amount of diversity without straying too far from the topic. This ensured a range of options, increasing the likelihood of finding an ideal, high-quality summary.

Step 2: Find the Best Response

Next came the need to search through the set of diverse responses and pinpoint the best one. To do this automatically, the financial institution applied the pairwise evaluation approach available in the Vertex Gen AI Evaluation Service. Think of it as a head-to-head showdown between responses. We pit response pairs against each other, judging them based on the original instructions and context to identify the response that aligns most closely with the user's intent.

Continuing the example above to illustrate, let's say we have those three responses about Japan's capital. We want to find the best one using pairwise comparisons:

Response 1 vs Response 2: The API favors Response 2, potentially explaining, "While Response 1 is technically correct, it doesn't directly answer the question about the current capital of Japan."
Response 2 (best response so far) vs Response 3: Response 2 wins again! Response 3 stumbles by using the past tense.
After these two rounds of comparison, we conclude that Response 2 is the best answer.

In the financial institution's case, they compared their five generated summaries in pairs to select the best one.

Step 3: Assess if the Response is Good Enough

The workflow then takes the top-performing response (Response 2) from the previous step and uses the pointwise evaluation service to assess it. This evaluation assigns quality scores and generates human-readable explanations for those scores across various dimensions, such as accuracy, groundedness, and helpfulness. This process not only highlights the best response but also provides insights into why the model generated this response, and also why it's considered superior to the other responses, fostering trust and transparency in the system's decision-making. In the case of the financial institution, they now used the summarization-related metrics in pointwise evaluation on the winning response to obtaining an explanation of how this answer is grounded, helpful, and high-quality. We can choose to return just the best response or include its associated quality metrics and explanation for greater transparency.

In essence, the workflow (as illustrated in this blog's banner) encompasses generating a variety of LLM responses, systematically evaluating them, and selecting the most suitable one—all while providing insights into why that particular response is deemed optimal. Get started by exploring our sample notebook and adapting it to fit with your use case. You can reverse the order of pairwise and pointwise evaluations, by ranking individual responses based on their pointwise or log likelihood scores and then conducting pairwise comparisons only on the top candidates. Further, while this example focuses on text, this approach can be applied to any modality or any use case including but not limited to question answering and summarization like illustrated in this blog. Finally, if you need to minimize latency, both workflows can benefit greatly from parallelizing the various API calls.

Take the next step

By embracing the inherent variability of LLMs and utilizing the Vertex Gen AI Evaluation Service, we can transform challenges into opportunities. Generating diverse responses, systematically evaluating them, and selecting the best option with clear explanations empowers us to unlock the full potential of LLMs. This approach not only enhances the quality and reliability of LLM outputs but also fosters trust and transparency. Start exploring this approach in our sample notebook and check out the documentation for the Vertex Gen AI Evaluation Service.

What’s new with Google Cloud - 2023

Sun, 31 Dec 2023 17:00:00 +0000

Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more.

Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

Week of Dec 10 - Dec 15

Announcing Launch of Cross Cloud Materialized Views: To help customers on their cross-cloud analytics journey, today we are thrilled to announce the public preview of BigQuery Omni cross-cloud materialized views (aka cross-cloud MVs). Cross-cloud MVs allow customers to very easily create a summary materialized view on GCP from base data assets available on another cloud. Cross-cloud MVs are automatically and incrementally maintained as base tables change, meaning only a minimal data transfer is necessary to keep the materialized view on GCP in sync. The result is an industry-first capability that empowers customers to perform frictionless, efficient, and cost-effective cross-cloud analytics

Week of Nov 27 - Dec 1

Google Cloud Global Cloud Service Provider of the Year. Google Cloud is thrilled to be recognized as Palo Alto Networks 2023 Global Cloud Service Provider of the Year and Global Cortex Partner of the Year. Google Cloud and Palo Alto Networks are dedicated to working together to support customer cloud journeys with an array of jointly engineered and integrated security solutions that enable digital innovation with ease. Read the Palo Alto Networks blog.
GKE Enterprise edition free trial: We have announced the general availability of GKE Enterprise, the premium edition of Google Kubernetes Engine (GKE) recently. With GKE Enterprise, companies can increase velocity across multiple teams, easily and securely run their most important business-critical apps and the latest AI/ML workloads safely at scale with a fully integrated and managed solution from Google Cloud. Start the 90-day free trial today with the GKE Enterprise edition by going to the GKE console and clicking on the “Learn about GKE Enterprise” button.
In our new blog post on Cost Management in BigQuery, you'll learn how to use budgets and custom quota to help you stay on top of your spending and prevent surprises on your cloud bill. The interactive tutorials linked in the article will help you set them up for your own Google Cloud projects in no time!
Leverage the transformative power of generative AI to elevate your customer service. Discover how you can optimize ROI, enhance customer satisfaction, and revolutionize your contact center operations with Google’s latest conversational AI offerings in this new blog.

Week of Nov 13 - Nov 17

In the first of our new Sketchnote series on Cloud FinOps, Erik and Pathik dive into what Cloud FinOps is, and how it can help your organization optimize its cloud budget.

Week of Oct 30 - Nov 3

Join Google Cloud’s product management leadership for a Data Analytics Innovation Roadmap session on November 13th. In this session, we will go through recent innovations, strategy and plans for BigQuery, Streaming Analytics, Data Lakes, Data Integration, and GenAI. This session will give you insight into Google’s feature development and will help your team plan your data analytics strategy.
Hear from Google Cloud experts on modernizing software delivery with generative AI, running AI/ML workloads on GKE, the future of AI-infused apps, and more at Digital Transform: the future of AI-powered apps, November 15th.
Vertex AI Search: Read about exciting new generative AI features coming to Vertex AI Search our platform to create search based applications for your business. Vertex AI Search provides customers with a tunable Retrieval Augmented Generation (RAG) system for information discovery. Learn more in this blog.
Vector similarity search: If you are looking to build an an e ecommerce recommendations engine or ad serving or other DIY application based on ANN aka vector similarity search dive into our vector search capability which is a part of the Vertex AI Search platform. We’ve expanded features and made it easier then ever for developers to get started building their apps.
Cloud Deploy - Deploy hooks (GA) allow users to specify and execute pre- and post- deploy actions using Cloud Deploy. This allows customers to run infrastructure deployment, database schema updates, and other activities immediately before a deploy job, and cleanup operations as part of a post (successful) deploy job. Learn More
Cloud Deploy - Cloud Deploy now uses Skaffold 2.8 as the default Skaffold version for all target types. Learn More
Artifact Registry - Artifact Registry remote repositories are now generally available (GA). Remote repositories store artifacts from external sources such as Docker Hub or PyPI. A remote repository acts as a proxy for the external source so that you have more control over your external dependencies. Learn More
Artifact Registry - Artifact Registry virtual repositories are now generally available (GA). Virtual repositories act as a single access point to download, install, or deploy artifacts in the same format from one or more upstream repositories. Learn More

Week of Oct 2 - Oct 6

ABAP SDK for Google Cloud now supports 40+ more APIs, an additional authentication mechanism and enhanced developer productivity for SAP ABAP developers. Learn more in this blog post.

Week of Sep 25 - Sep 29

Our newly published Storage decision tree helps you research and select the storage services in Google Cloud that best match your specific workload needs and the accompanying blog provides an overview of the services offered for block storage, object storage, NFS and Multi-Writer file storage, SMB storage, and storage for data lakes and data warehouses.

Week of Sep 18- Sep 22

Meet the inaugural cohort of the Google for Startups Accelerator: AI First program featuring groundbreaking businesses from eight countries across Europe and Israel using AI and ML to solve complex problems. Learn how Google Cloud empowers these startups and check out the selected ventures here.
BigQuery is introducing new SQL capabilities for improved analytics flexibility, data quality and security. Some examples include schema support for Flexible column name, Authorized store proceduces, ANY_VALUE (HAVING) also known as MAX_BY and MIN_BY and many more. Check out full details here.
Cloud Logging is introducing to Preview the ability to save charts from Cloud Logging’s Log Analytics to a custom dashboard in Cloud Monitoring. Viewing, copying and sharing the dashboards are supported in Preview. For more information, see Save a chart to a custom dashboard.
Cloud Logging now supports customizable dashboards in its Logs Dashboard. Now you, can add your own charts to see what’s most valuable to you on the Logs Dashboard. Learn more here.
Cloud Logging launches several usability features for effective troubleshooting. Learn more in this blog post.
Search your logs by service name with the new option in Cloud Logging. Now you can use the Log fields to select by service which makes it easier to quickly find your Kubernetes container logs. Check out the details here.
Community Security Analytics (CSA) can now be deployed via Dataform to help you analyze your Google Cloud security logs. Dataform simplifies deploying and operating CSA on BigQuery, with significant performance gains and cost savings. Learn more why and how to deploy CSA with Dataform in this blog post.
Dataplex data profiling and AutoDQ are powerful new features that can help organizations to improve their data quality and build more accurate and reliable insights and models. These features and now Generally Available. Read more in this blog post.

Week of Sep 4 - Sep 8

Introducing Looker’s Machine Learning Accelerator. This easy to install extension allows business users to train, evaluate, and predict with machine learning models right in the Looker interface.
Learn about how Freestar has built a super low latency, globally distributed application powered by Memorystore and the Envoy proxy. This reference walks users through the finer details of architecture and configuration, that they can easily replicate for their own needs.

Week of Aug 28 - Sep 1

You can access comprehensive and up-to-date environmental information to develop sustainability solutions and help people adapt to the impacts of climate change through Google Maps Platform’s environment APIs. The Air Quality, and Solar APIs are generally available today. Get started or learn more in this blog post.
Google Cloud’s Global Partner Ecosystems & Channels team launched the Industry Value Networks (IVN) initiative at Google Cloud NEXT ’23. IVNs combine expertise and offerings from systems integrators (SIs), independent software vendors (ISVs) and content partners to create comprehensive, differentiated, repeatable, and high-value solutions that accelerate time-to-value and reduce risk for customers. To learn more about the IVN initiative, please see this blog post

Week of Aug 21 - Aug 25

You can now easily export data from Earth Engine into BigQuery with our new connector. This feature allows for improved workflows and new analyses that combine geospatial raster and tabular data. This is the first step in toward deeper interoperability between the two platforms, supporting innovations in geospatial sustainability analytics. Learn more in this blog post or join our session at Cloud Next.

Week of Aug 14 - Aug 18

You can now view your log query results as a chart in the Log Analytics page in Cloud Logging. With this new capability available in Preview, users can write a SQL filter and then use the charting configuration to build a chart. For more information, see Chart query results with Log Analytics.

Week of Aug 7 - Aug 11

You can now use Network Analyzer and Recommender API to query the IP address utilization of your GCP subnets, to identify subnets that might be full or oversized. Learn more in a dedicated blog post here.
Memorystore has introduced version support for Redis 7.0. Learn more about the included features and upgrade your instance today!

Week of July 31 - Aug 4

Attack Path Simulation is now generally available in Security Command Center Premium. This new threat prevention capability automatically analyzes a customer’s Google Cloud environment to discover attack pathways and generate attack exposure scores to prioritize security findings. Learn more. Get started now.

Week of July 24-28

Cloud Deploy has updated the UI with the ability to Create a Pipeline along with a Release. The feature is now GA. Read more
Our newly published Data & Analytics decision tree helps you select the services on Google Cloud that best match your data workloads needs, and the accompanying blog provides an overview of the services offered for data ingestion, processing, storage, governance, and orchestration.
Customer expectations from the ecommerce platforms are at all time high and they now demand a seamless shopping experience across platforms, channels and devices. Establishing a secure and user-friendly login platform can make it easier for users to self-identify and help retailers gain valuable insights into customer’s buying habits. Learn more about how they can better manage customer identities to support an engaging ecommerce user experience using Google Cloud Identity Platform.
Our latest Cloud Economics post just dropped, exploring how customers can benchmark their IT spending against peers to optimize investments. Comparing metrics like tech spend as a percentage of revenue and OpEx uncovers opportunities to increase efficiency and business impact. This data-driven approach is especially powerful for customers undergoing transformation.

Week of July 17-21

Cloud Deploy now supports deploy parameters. With deploy parameters you can pass parameters for your release, and those values are provided to the manifest or manifests before those manifests are applied to their respective target. A typical use for this would be to apply different values to manifests for different targets in a parallel deployment. Read more
Cloud Deploy is now listed among other Google Cloud services which can be configured to meet Data Residency Requirements. Read more
Log Analytics in Cloud Logging now supports most regions. Users can now upgrade buckets to use Log Analytics in Singapore, Montréal, London, Tel Aviv and Mumbai. Read more for the full list of support regions.

Week of July 10-14

Cloud CDN now supports private origin authentication in GA. This capability improves security by allowing only trusted connections to access the content on your private origins and preventing users from directly accessing it.
Workload Manager - Guided Deployment Automation is now available in Public Preview, with initial support for SAP solutions. Learn how to configure and deploy SAP workloads directly from a guided user interface, leveraging end-to-end automation built on Terraform and Ansible.
Artifact Registry - Artifact registry now supports clean up policies now in Preview. Cleanup policies help you manage artifacts by automatically deleting artifacts that you no longer need, while keeping artifacts that you want to store. Read more

Week of July 3-7

Cloud Run jobs now supports long-running jobs. A single Cloud Run jobs task can now run for up to 24 hours. Read More.
How Google Cloud NAT helped strengthen Macy’s security. Read more

Week of June 26-30

Cloud Deploy parallel deployment is now generally available. You can deploy to a target that's configured to represent multiple targets, and your application is deployed to those targets concurrently. Read More.
Cloud Deploy canary deployment strategy is now generally available. A canary deployment is a progressive rollout of an application that splits traffic between an already-deployed version and a new version. Read More

Week of June 19-23

Google Cloud’s Managed Service for Prometheus now supports Prometheus exemplars. Exemplars provide cross-signals correlation between your metrics and your traces so you can more easily pinpoint root cause issues surfaced in your monitoring operations.
Managing logs across your organization is now easier with the general availability of user-managed service accounts. You can now choose your own service account when sending logs to a log bucket in a different project.
Data Engineering and Analytics Day - Join Google Cloud experts on June 29th to learn about the latest data engineering trends and innovations, participate in hands-on labs, and learn best practices of Google Cloud’s data analytics tools. You will gain a deeper understanding of how to centralize, govern, secure, streamline, analyze, and use data for advanced use cases like ML processing and generative AI.

Week of June 5-9

TMI: Shifting Down, Not Left- The first post in our new modernization series, The Modernization Imperative. Here, Richard Seroter talks about the strategy of ‘shifting down’ and relying on managed services to relieve burdens on developers.
Cloud Econ 101: The first in a new series on optimizing cloud tools to achieve greater return on your cloud investments. Join us biweekly as we explore ways to streamline workloads, and explore successful cases of aligning technology goals to drive business value.
The Public Preview: of Frontend Mutual TLS Support on Global External HTTPS Load Balancing is now available. Now you can use Global External HTTPS Load Balancing to offload Mutual TLS authentication for your workloads. This includes client mTLS for Apigee X Northbound Traffic using Global HTTPS Load Balancer.
FinOps from the field: How to build a FinOps Roadmap - In a world where cloud services have become increasingly complex, how do you take advantage of the features, but without the nasty bill shock at the end? Learn how to build your own FinOps roadmap step by step, with helpful tips and tricks from FinOps workshops Google has completed with customers.
We are now offering up to $1M of financial protection to help cover the costs of undetected cryptomining attacks. This is a new program only for Security Command Center Premium customers. Security Command Center makes Google Cloud a safe place for your applications and data. Read about this new program in our blog.
Global External HTTP(S) Load Balancer and Cloud CDN’s advanced traffic management using flexible pattern matching is now GA. This allows you to use wildcards anywhere in your path matcher. You can use this to customize origin routing for different types of traffic, request and response behaviors, and caching policies. In addition, you can now use results from your pattern matching to rewrite the path that is sent to the origin.
Security Command Center (SCC) Premium, our built-in security and risk management solution for Google Cloud, is now generally available for self-service activation for full customer organizations. Customers can get started with SCC in just a few clicks in the Google Cloud console. There is no commitment requirement, and pricing is based on a flexible pay-as-you-go model.
Dataform is Generally Available. Dataform offers an end-to-end experience to develop, version control, and deploy SQL pipelines in BigQuery. Using a single web interface, data engineers and data analysts of all skill levels can build production-grade SQL pipelines in BigQuery while following software engineering best practices such as version control with Git, CI/CD, and code lifecycle management. Learn more.

Week of May 29 - June 2

Google Cloud Deploy. The price of an active delivery pipeline is reduced. Also, single-target delivery pipelines no longer incur a charge. Underlying service charges continue to apply. See Pricing Page for more details.

Week of May 22-26

Security Command Center (SCC) Premium pricing for project-level activation is now 25% lower for customers who use SCC to secure Compute Engine, GKE-Autopilot, App Engine and Cloud SQL. Please see our updated rate card. Also, we have expanded the number of finding types available for project-level Premium activations to help make your environment more secure. Learn more.
Vertex AI Embeddings for Text: Grounding LLMs made easy: Many people are now starting to think about how to bring Gen AI and large language models (LLMs) to production services. You may be wondering "How to integrate LLMs or AI chatbots with existing IT systems, databases and business data?", "We have thousands of products. How can I let LLM memorize them all precisely?", or "How to handle the hallucination issues in AI chatbots to build a reliable service?". Here is a quick solution: grounding with embeddings and vector search. What is grounding? What are embedding and vector search? In this post, we will learn these crucial concepts to build reliable Gen AI services for enterprise use with live demos and source code.

Week of May 15-19

Introducing the date/time selector in Log Analytics in Cloud Logging. You can now easily customize the date and time range of your queries in the Log Analytics page by using the same date/time-range selector used in Logs Explorer, Metrics Explorer and other Cloud Ops products. There are several time range options, such as preset times, custom start and end times, and relative time ranges. For more information, see Filter by time in the Log Analytics docs.
Cloud Workstations is now GA. We are thrilled to announce the general availability of Cloud Workstations with a list of new enhanced features, providing fully managed integrated development environments (IDEs) on Google Cloud. Cloud Workstations enables faster developer onboarding and increased developer productivity while helping support your compliance requirements with an enhanced security posture. Learn More

Week of May 8 - 14

Introducing BigQuery differential privacy, SQL building blocks that analysts and data scientists can use to anonymize their data. We are also partnering with Tumult Labs to help Google Cloud customers with their differential privacy implementations.
Scalable electronic trading on Google Cloud: A business case with BidFX: Working with Google Cloud, BidFX has been able to develop and deploy a new product called Liquidity Provision Analytics (“LPA”), launching to production within roughly six months, to solve the transaction cost analysis challenge in an innovative way. LPA will be offering features such as skew detection for liquidity providers, execution time optimization, pricing comparison, top of book analysis and feedback to counterparties. Read more here.
AWS EC2 VMs discovery and assessment - mFit can discover EC2 VMs inventory in your AWS region and collect guest level information from multiple VMs to provide technical fit assessment for modernization. See demo video.
Generate assessment report in Microsoft Excel file - mFit can generate detailed assessment report in Microsoft Excel (XLSX) format which can handle large amounts of VMs in a single report (few 1000’s) which an HTML report might not be able to handle.
Regulatory Reporting Platform: Regulatory reporting remains a challenge for financial services firms. We share our point of view on the main challenges and opportunities in our latest blog, accompanied by an infographic and a customer case study from ANZ Bank. We also wrote a white paper for anyone looking for a deeper dive into our Regulatory Reporting Platform.
Google is partnering with regional carriers Chunghwa Telecom, Innove (subsidiary of Globe Group) and AT&T to deliver the TPU (Taiwan-Philippines-U.S.) cable system — connecting Taiwan, Philippines, Guam, and California — to support growing demand in the APAC region. We are committed to providing Google Cloud customers with a resilient, high-performing global network. NEC is the supplier, and the system is expected to be ready for service in 2025.

Week of May 1 - 5

Microservices observability is now generally available for C++, Go and Java. This release includes a number of new features and improvements, making it easier than ever to monitor and troubleshoot your microservices applications. Learn more on our user guide.
Google Cloud Deploy Google Cloud Deploy now supports Skaffold 2.3 as the default Skaffold version for all target types. Release Notes.
Cloud Build You can now configure Cloud Build to continue executing a build even if specified steps fail. This feature is generally available. Learn more here

Week of April 24-28

General Availability: Custom Modules for Security Health Analytics is now generally available. Author custom detective controls in Security Command Center using the new custom module capability.
Next generation Confidential VM is now available in Private Preview with a Confidential Computing technology called AMD Secure Encrypted Virtualization-Secure Nested Paging (AMD SEV-SNP) on general purpose N2D machines. Confidential VMs with AMD SEV-SNP enabled builds upon memory encryption and adds new hardware-based security protections such as strong memory integrity, encrypted register state (thanks to AMD SEV-Encrypted State, SEV-ES), and hardware-rooted remote attestation. Sign up here!
Selecting Tier_1 networking for your Compute Engine VM can give you the bandwidth you need for demanding workloads. Check out this blog on Increasing bandwidth to Compute Engine VMs with TIER_1 networking.

Week of April 17-21

Use Terraform to manage Log Analytics in Cloud Logging You can now configure Log Analytics on Cloud Logging buckets and BigQuery linked datasets by using the following Terraform modules:
- Google_logging_project_bucket_config
- google_logging_linked_dataset

Week of April 10-14

Assured Open Source Software is generally available for Java and Python ecosystems. Assured OSS is offered at no charge and provides an opportunity for any organization that utilizes open source software to take advantage of Google’s expertise in securing open source dependencies.
BigQuery change data capture (CDC) is now in public preview!
BigQuery CDC provides a fully-managed method of processing and applying streamed UPSERT and DELETE operations directly into BigQuery tables in real time through the BigQuery Storage Write API. This further enables the real-time replication of more classically transactional systems into BigQuery, which empowers cross functional analytics between OLTP and OLAP systems. Learn more here.

Week of April 3-7

New Visualization tools for Compute Engine Fleets The Observability tab in the Compute Engine console VM List page has reached General Availability. The new Observability tab is an easy way to monitor and troubleshoot the health of your fleet of VMs
Datastream for BigQuery is Generally Available! Datastream for BigQuery is generally available, offering a unique, truly seamless and easy-to-use experience that enables near-real time insights in BigQuery with just a few steps. Using BigQuery’s newly developed change data capture (CDC) and Storage Write API’s UPSERT functionality, Datastream efficiently replicates updates directly from source systems into BigQuery tables in real-time. You no longer have to waste valuable resources building and managing complex data pipelines, self-managed staging tables, tricky DML merge logic, or manual conversion from database-specific data types into BigQuery data types. Just configure your source database, connection type, and destination in BigQuery and you’re all set. Datastream for BigQuery will backfill historical data and continuously replicate new changes as they happen.
Now available! Build an analytics lakehouse on Google Cloud whitepaper. The analytics lakehouse combines the benefits of data lakes and data warehouses without the overhead of each. In this paper, we discuss the end-to-end architecture which enable organizations to extract data in real-time regardless of which cloud or datastore the data reside in, use the data in aggregate for greater insight and artificial intelligence (AI) - all with governance and unified access across teams. Download now.
Now Available! Google Cloud Deploy now supports canary release as a deployment strategy. This feature is supported in Preview. Learn more
General Availability : Cloud Run services as backends to Internal HTTP(S) Load Balancers and Regional External HTTP(S) Load Balancers. Internal load balancers allow you to establish private connectivity between Cloud Run services and other services and clients on Google Cloud, on-premises, or on other clouds. In addition you get custom domains, tools to migrate traffic from legacy services, Identity-aware proxy support, and more. Regional external load balancer, as the name suggests, is designed to reside in a single region and connect with workloads only in the same region, thus helps you meet your regionalization requirements. Learn more.

Week of March 27-31

Last chance: Register to attend Google Data Cloud & AI Summit
Join us on Wednesday, March 29, at 9 AM PDT/12 PM EDT to discover how you can use data and AI to reveal opportunities to transform your business and make your data work smarter. Find out how organizations are using Google Cloud data and AI solutions to transform customer experiences, boost revenue, and reduce costs. Register today for this no cost digital event.
New BigQuery editions: flexibility and predictability for your data cloud
At the Data Cloud & AI Summit, we announced BigQuery pricing editions—Standard, Enterprise and Enterprise Plus—that allow you to choose the right price-performance for individual workloads. Along with editions, we also announced autoscaling capabilities that ensure you only pay for the compute capacity you use, and a new compressed storage billing model that is designed to reduce your storage costs. Learn more about latest BigQuery innovations and register for the upcoming BigQuery roadmap session on April 5, 2023.
Introducing Looker Modeler: A single source of truth for BI metrics
At the Data Cloud & AI Summit, we introduced a standalone metrics layer we call Looker Modeler, available in preview in Q2. With Looker Modeler, organizations can benefit from consistent governed metrics that define data relationships and progress against business priorities, and consume them in BI tools such as Connected Sheets, Looker Studio, Looker Studio Pro, Microsoft Power BI, Tableau, and ThoughtSpot.
Cloud Workstations is now available in more regions. Cloud Workstations is now available in asia-south1 (India), us-east4 (Virginia, North America), europe-west6 (Switzerland), and europe-west9 (France). The full list of regions is here.
Bucket based log based metrics—now generally available—allow you to track, visualize and alert on important logs in your cloud environment from many different projects or across the entire organization based on what logs are stored in a log bucket.
NEW Customer Blog! Faced with strong data growth, Squarespace made the decision to move away from on-premises Hadoop to a cloud-managed solution for its data platform. Learn how they reduced the number of escalations by 87% with the analytics lakehouse on Google Cloud. Read now

Week of March 20-24

Chronicle Security Operations Feature Roundup
Bringing a modern and unified security operations experience to our customers is and has been a top priority with the Google Chronicle team. We’re happy to show continuing innovation and even more valuable functionality. In our latest release roundup we’ll highlight a host of new capabilities focused on delivering improved context, collaboration, and speed to handle alerts faster and more effectively. Learn how our newest capabilities enable security teams to do more with less here.
Announcing Google’s Data Cloud & AI Summit, March 29th!
Can your data work smarter? How can you use AI to unlock new opportunities? Join us on Wednesday, March 29, to gain expert insights, new solutions, and strategies to reveal opportunities hiding in your company’s data. Find out how organizations are using Google Cloud data and AI solutions to transform customer experiences, boost revenue, and reduce costs. Register today for this no cost digital event.
Artifact Registry Feature Preview - Artifact Registry now supports immutable tags for Docker repositories. If you enable this setting, an image tag always points to the same image digest, including the default latest tag. This feature is in Preview. Learn more

Week of March 13 - 17

Building the most open and innovative AI ecosystem
In addition to the news this week on AI products, Google Cloud has also announced new partnerships, programs, and resources. This includes bringing bringing the best of Google’s infrastructure, AI products, and foundation models to partners at every layer of the AI stack: chipmakers, companies building foundation models and AI platforms, technology partners enabling companies to develop and deploy machine learning (ML) models, app-builders solving customer use-cases with generative AI, and global services and consulting firms that help enterprise customers implement all of this technology at scale. Learn more.
From Microbrows to Microservices
Ulta Beauty is building their digital store of the future, but to maintain control over their new modernized application they turned to Anthos and GKE - Google Cloud’s managed container services, to provide an eCommerce experience as beautiful as their guests. Read our blog to see how a newly-minted Cloud Architect learnt Kubernetes and Google Cloud to provide the best possible architecture for his developers. Learn more.
To prepare for the busiest shopping season of the year, Black Friday and Cyber Monday, Lowe’s relies heavily on Google’s agile SRE Framework to ensure business and technical alignment, manage bots, and create an always-available shopping experience. Read more.
Now generally available, understand and trust your data with Dataplex data lineage - a fully managed Dataplex capability that helps you understand how data is sourced and transformed within the organization. Dataplex data lineage automatically tracks data movement across BigQuery, BigLake, Cloud Data Fusion (Preview), and Cloud Composer (Preview), eliminating operational hassles around manual curation of lineage metadata. Learn more here.
Rapidly expand the reach of Spanner databases with read-only replicas and zero-downtime moves. Configurable read-only replicas let you add read-only replicas to any Spanner instance to deliver low latency reads to clients in any geography. Alongside Spanner’s zero-downtime instance move service, you have the freedom to move your production Spanner instances from any configuration to another on the fly, with zero downtime, whether it’s regional, multi-regional, or a custom configuration with configurable read-only replicas. Learn more here.

Week of March 6 - 10

Automatically blocking project SSH keys in Dataflow is now GA. This service option allows Dataflow users to prevent their Dataflow worker VMs from accepting SSH keys that are stored in project metadata, and results in improved security. Getting started is easy: enable the block-project-ssh-keys service option while submitting your Dataflow job.
Celebrate International Women’s Day Learn about the leaders driving impact at Google Cloud and creating pathways for other women in their industries. Read more.
Google Cloud Deploy now supports Parallel Deployment to GKE and Cloud Run workloads. This feature is in Preview. Read more.
Sumitovant doubles medical research output in one year using Looker
Sumitovant is a leading biopharma research company that has doubled their research output in one year alone. By leveraging modern cloud data technologies, Sumitovant supports their globally distributed workforce of scientists to develop next generation therapies using Google Cloud’s Looker for trusted self-service data research. To learn more about Looker check out https://cloud.google.com/looker

Week of Feb 27 - March 3

Accelerate Queries on your BigLake Tables with Cached Metadata (Preview!)
Make your queries on BigLake Tables go faster by enabling metadata caching. Your queries will avoid expensive LIST operation for discovering files in the table and experience faster file and hive partition pruning. Follow the documentation here.
Google Cloud Deploy support for deployment verification is now GA! Read more or Try the Demo
Add geospatial intelligence to your Retail use cases by leveraging the CARTO platform on top of your data in BigQuery
Location data will add a new dimension to your Retail use cases, like site selection, geomarketing, and logistics and supply chain optimization. Read more about the solution and various customer implementations in the CARTO for Retail Reference Guide, and see a demonstration in this blog.

Week of Feb 20 - Feb 24

Start your digital transformation by embarking on a hybrid cloud journey with Anthos. Anthos helps you modernize your application and infrastructure in place and build a unified Kubernetes fabric between your on prem environments and the Google cloud. The newly published Anthos hybrid cloud architecture reference design guide provides opinionated guidance to deploy Anthos in a hybrid environment to address some common challenges that you might encounter. Check out the architecture reference design guide here to accelerate your journey to hybrid cloud and containerization.
Logs for Network Load Balancing and logs for Internal TCP/UDP Load Balancing are now GA!
Logs are aggregated per-connection and exported in near real-time, providing useful information, such as 5-tuples of the connection, received bytes, and sent bytes, for troubleshooting and monitoring the pass-through Google Cloud Load Balancers. Further, customers can include additional optional fields, such as annotations for client-side and server-side GCE and GKE resources, to obtain richer telemetry.

Week of Feb 13 - Feb 17,

Announcing Google’s Data Cloud & AI Summit, March 29th!
Can your data work smarter? How can you use AI to unlock new opportunities? Register for Google Data Cloud & AI Summit, a digital event for data and IT leaders, data professionals, developers, and more to explore the latest breakthroughs. Join us on Wednesday, March 29, to gain expert insights, new solutions, and strategies to reveal opportunities hiding in your company’s data. Find out how organizations are using Google Cloud data and AI solutions to transform customer experiences, boost revenue, and reduce costs. Register today for this no cost digital event.
Leverege uses BigQuery as a key component of its data and analytics pipeline to deliver innovative IoT solutions at scale. As part of the Built with BigQuery program, this blog post goes into detail about Leverege IoT Stack that runs on Google Cloud to power business-critical enterprise IoT solutions at scale.
Download white paper Three Actions Enterprise IT Leaders Can Take to Improve Software Supply Chain Security to learn how and why high-profile software supply chain attacks like SolarWinds and Log4j happened, the key lessons learned from these attacks, as well as actions you can take today to prevent similar attacks from happening to your organization.
Running SAP workloads on Google Cloud? Upgrade to our newly released Agent for SAP to gain increased visibility into your infrastructure and application performance. The new agent consolidates several of our existing agents for SAP workloads, which means less time spent on installation and updates, and more time for making data-driven decisions. In addition, there is new optional functionality that powers exciting products like Workload Manager, a way to automatically scan your SAP workloads against best-practices. Learn how to install or upgrade the agent here.
Deploy PyTorch models on Vertex AI in a few clicks with prebuilt PyTorch serving containers - which means less code, no need to write Dockerfiles, and faster time to production.
Confidential GKE Nodes on Compute-Optimized C2D VMs are now GA. Confidential GKE Nodes help to increase the security of your GKE clusters by leveraging hardware to ensure your data is encrypted in memory, helping to defend against accidental data leakage, malicious administrators and “curious neighbors”. Getting started is easy, as your existing GKE workloads can run confidentially with no code changes required.

Week of Feb 3 - Feb 10

Immersive Stream for XR leverages Google Cloud GPUs to host, render, and stream high-quality photorealistic experiences to millions of mobile devices around the world, and is now generally available. Read more here.
Reliable and consistent data presents an invaluable opportunity for organizations to innovate, make critical business decisions, and create differentiated customer experiences. But poor data quality can lead to inefficient processes and possible financial losses. Today we announce new Dataplex features: automatic data quality (AutoDQ) and data profiling, available in public preview. AutoDQ offers automated rule recommendations, built-in reporting, and serveless execution to construct high-quality data. Data profiling delivers richer insight into the data by identifying its common statistical characteristics. Learn more.
Cloud Workstations now supports Customer Managed Encryption Keys (CMEK), which provides user encryption control over Cloud Workstation Persistent Disks. Read more
Google Cloud Deploy now supports Cloud Run targets in General Availability. Read more
Learn how to use NetApp Cloud Volumes Service as datastores for Google Cloud VMware Engine for expanding storage capacity. Read more

Week of Jan 30 - Feb 3

Oden Technologies uses BigQuery to provide real-time visibility, efficiency recommendations and resiliency in the face of network disruptions in manufacturing systems. As part of the Built with BigQuery program, this blog post describes the use cases, challenges, solution and solution architecture in great detail.
Lytics is a next generation composable CDP that enables companies to deploy a scalable CDP around their existing data warehouse/lakes. As part of the Built with BigQuery program for ISVs, Lytics leverages Analytics Hub to launch secure data sharing and enrichment solution for media and advertisers. This blog post goes over Lytics Conductor on Google Cloud and its architecture in great detail.
Now available in public preview, Dataplex business glossary offers users a cloud-native way to maintain and manage business terms and definitions for data governance, establishing consistent business language, improving trust in data, and enabling self-serve use of data. Learn more here.
Security Command Center (SCC), Google Cloud’s native security and risk management solution, is now available via self-service to protect individual projects from cyber attacks. It’s never been easier to secure your Google Cloud resources with SCC. Read our blog to learn more. To get started today, go to Security Command Center in the Google Cloud console for your projects.
Global External HTTP(S) Load Balancer and Cloud CDN now support advanced traffic management using flexible pattern matching in public preview. This allows you to use wildcards anywhere in your path matcher. You can use this to customize origin routing for different types of traffic, request and response behaviors, and caching policies. In addition, you can now use results from your pattern matching to rewrite the path that is sent to the origin.
Run large pods on GKE Autopilot with the Balanced compute class. When you need computing resources on the larger end of the spectrum, we’re excited that the Balanced compute class, which supports Pod resource sizes up to 222vCPU and 851GiB, is now GA!
Manage table and column-level access permissions using attribute-based policies in Dataplex. Dataplex attribute store provides a unified place where you can create and organize a Data Class hierarchy to classify your distributed data and assign behaviors such as Table-ACLs and Column-ACLs to the classified data classes. Dataplex will propagate IAM-Roles to tables, across multiple Google Cloud projects, according to the attribute(s) assigned to them and a single, merged policy tag to columns according to the attribute(s) attached to them. Read more.

Week of Jan 23 - Jan 27

Starting with Anthos version 1.14, Google supports each Anthos minor version for 12 months after the initial release of the minor version, or until the release of the third subsequent minor version, whichever is longer. We plan to have Anthos minor release three times a year around the months of April, August, and December in 2023, with a monthly patch release (for example, z in version x.y.z) for supported minor versions. For more information, read here.
Anthos Policy Controller enables the enforcement of fully programmable policies for your clusters across the environments. We are thrilled to announce the launch of our new built-in Policy Controller Dashboard, a powerful tool that makes it easy to manage and monitor the policy guardrails applied to your Fleet of clusters. New policy bundles are available to help audit your cluster resources against kubernetes standards, industry standards, or Google recommended best practices. The easiest way to get started with Anthos Policy Controller is to just install Policy controller and try applying a policy bundle to audit your fleet of clusters against a standard such as CIS benchmark.
Dataproc is an important service in any data lake modernization effort. Many customers begin their journey to the cloud by migrating their Hadoop workloads to Dataproc and continue to modernize their solutions by incorporating the full suite of Google Cloud’s data offerings. Check out this guide that demonstrates how you can optimize Dataproc job stability, performance, and cost-effectiveness.
Eventarc adds support for 85+ new direct events from the following Google services in Preview: API Gateway, Apigee Registry, BeyondCorp, Certificate Manager, Cloud Data Fusion, Cloud Functions, Cloud Memorystore for Memcached, Database Migration, Datastream, Eventarc, Workflows. This brings the total pre-integrated events offered in Eventarc to over 4000 events from 140+ Google services and third-party SaaS vendors.
mFit 1.14.0 release adds support for JBoss and Apache workloads by including fit analysis and framework analytics for these workload types in the assessment report. See the release notes for important bug fixes and enhancements.
Google Cloud Deploy Google Cloud Deploy now supports Skaffold version 2.0. Release notes
Cloud Workstations - Labels can now be applied to Cloud Workstations resources. Release notes
Cloud Build - Cloud Build repositories (2nd gen) lets you easily create and manage repository connections, not only through Cloud Console but also through gcloud and the Cloud Build API. Release notes

Week of Jan 16 - Jan 20

Cloud CDN now supports private origin authentication for Amazon Simple Storage Service (Amazon S3) buckets and compatible object stores in Preview. This capability improves security by allowing only trusted connections to access the content on your private origins and preventing users from directly accessing it.

Week of Jan 9 - Jan 13

Revionics partnered with Google Cloud to build a data-driven pricing platform for speed, scale and automation with BigQuery, Looker and more. As part of the Built with BigQuery program, this blog post describes the use cases, problems solved, solution architecture and key outcomes of hosting Revionics product, Platform Built for Change on Google Cloud.
Pub/Sub Lite now offers export subscriptions to Pub/Sub. This new subscription type writes Lite messages directly to Pub/Sub - no code development or Dataflow jobs needed. Great for connecting disparate data pipelines and migration from Lite to Pub/Sub. See here for documentation.
GPU Pods on GKE Autopilot are now generally available. Customers can now run ML training, inference, video encoding and all other workloads that need a GPU, with the convenience of GKE Autopilot’s fully-managed Kubernetes environment.
Kubernetes v1.26 is now generally available on GKE. GKE customers can now take advantage of the many new features in this exciting release. This release continues Google Cloud’s goal of making Kubernetes releases available to Google customers within 30 days of the Kubernetes OSS release.
Comprehensive guide for designing reliable infrastructure for your workloads in Google Cloud. The guide combines industry-leading reliability best practices with the knowledge and deep expertise of reliability engineers across Google. Understand the platform-level reliability capabilities of Google Cloud, the building blocks of reliability in Google Cloud and how these building blocks affect the availability of your cloud resources. Review guidelines for assessing the reliability requirements of your cloud workloads. Compare architectural options for deploying distributed and redundant resources across Google Cloud locations, and learn how to manage traffic and load for distributed deployments. Read the full blog here.

The year in Google Cloud: Top news of 2023

Tue, 19 Dec 2023 17:00:00 +0000

In the world of technology and cloud computing, the news comes fast and furious. Blink and you’ll miss it. As we wind down the final days of 2023, here’s a look at the top stories of the year that we published on the Google Cloud blog — the product launches, research findings, and initiatives that resonated most with you.

January

The Google Cloud community started the year in a contemplative mood, thirsty for tools to give them deeper insights from their data, and more holistic views of their environments. The top stories of the month were:

February

In February, readers looked ahead. We unveiled a new pricing approach that is decidedly forward-looking, made inroads on futuristic immersive technology, and planted seeds with the telecommunications community at Mobile World Congress. Readers were also really excited for the global roll-out of AlloyDB. The top stories for the month were:

March

If there has been one overarching theme to Google Cloud for 2023, it’s been generative AI, which made its first real showing this month, with the launch of support for the technology in Vertex AI, alongside an avalanche of news from our first-ever Data Cloud & AI Summit.

April

We kicked the AI story up a notch in April with segment-specific news, and a glimpse into our AI-optimized infrastructure. We also made it easier for customers to interact with Google Cloud professional services, and introduced gamified training!

May

Google I/O is usually a consumer-focused show, but Google Cloud’s foundational role in enabling generative AI let our news take center stage, including the launch of the Duet AI brand. With that as the backdrop, it’s no surprise readers were also excited about new multicloud connectivity capabilities.

June

Three short months after announcing support for generative AI for Vertex AI, we made good by bringing it to general availability, and expanding it to search experiences. We also helped thread the needle between generative AI and databases with vector support, and shared how generative AI is helping to evolve the threat landscape.

July

July is usually a relatively quiet month, as people head out on vacations, but not this year — the launch of even more models for our AI builder tools proved just as enticing as a day at the beach. We also shook up the MySQL community with a bold new Cloud SQL Enterprise Plus offering, and introduced a new, visual way for developers to connect their applications.

August

If you think that generative AI news dominated Google Cloud Next this month, you’re only half right. It was certainly a thread in all our leading announcements, but there was also a lot of excitement for more traditional Google Cloud specialties around data analytics and Kubernetes.

September

Enough with the generative AI news already :) In September, readers remembered that it’s a big world out there, and that Google Cloud is a cloud provider with global coverage. From dashboards to databases, from subsea cables to blockchain, this month’s most popular stories showcased the breadth and depth of Google Cloud’s offerings.

October

Around here, we like to talk about having a healthy disregard for the impossible. Like mitigating the largest-ever DDoS attack — again. Or rethinking Ethernet. Or halving the cost of Spanner compared to the competition. Whatevs, no big deal.

November

Security researchers never sleep, and neither do our systems engineers, uncovering significant new vulnerabilities, and beating records for the world’s largest distributed training job for large language models. Oh, and Memorystore for Redis got an upgrade that delivered 60X more throughput.

December

Halfway through the month, we’re pretty sure we know what the top stories will have been: anything related to Gemini, Google’s latest and most capable AI model. Also a special shout out to Google Cloud’s Learning Content team, whose post about free generative-AI trainings shot up to be the top-viewed post of the entire year — in the matter of a few days. Seems like you are all as excited as we are about the Gemini era!

And that’s a wrap! On behalf of the Google Cloud blog team, wishing you peaceful and happy holiday season, and looking forward to seeing you here on these pages in 2024.

Looker Studio brings powerful explorations, fresher data and faster filtering

Thu, 16 Nov 2023 17:00:00 +0000

Looker Studio supports self-serve analytics for ad hoc data, and together with Looker, contributes to the more than 10 million users who access the Looker family of products each month. Today, we are introducing new ways for analysts to provide business users with options to explore data and self-serve business decisions, expanding ways all our users can analyze and explore data — leading to faster and more informed decisions.

Introducing personal report links

Business users often leverage shared dashboards from data analysts, which contain key company metrics and KPIs, as a starting point and want to explore beyond the curated analysis to arrive at more specific insights for their own data needs. The introduction of personal reports in Looker Studio enables this activity, delivering a private sandbox for exploration so users can self-serve their own questions and find insights faster – without modifying the original curated report.

Whether you share a report link in group chats or direct messages, an individual copy is created for each user that opens it so that everyone gets their own personal report.

Personal Looker Studio reports are designed to be ephemeral, meaning you don’t need to worry about creating unwanted content, but if you land on valuable insights that you want to keep, you can save and share these reports with new links, separate from the original report you built from.

You can learn more about how personal reports work and how to use them in our Help Center.

Looker Studio Personal Link Visual

Automated report updates

Your analysis and insights are only as good as the freshness of your reports. Looker Studio users can now enable their reports to auto-refresh data at a predefined cadence, so critical business decisions are based on current and updated information.

To learn more about how auto-refresh works, including details on how it works with cache, presentation mode, and existing data freshness settings, visit our Help Center.

Looker Studio Auto refresh feature Visual

Faster filtering in reports

Quick filters enable powerful exploration to slice data and uncover hidden patterns and insights within the context of your report. Quick filters don’t affect other users’ views, so whether you are exploring in a shared or personal report, your unique view is only shared once you are ready. The filter bar also gives you a complete picture of whether applied filters originate from interactive cross-chart filtering or quick filters.

Learn more about how to add quick filters in reports in our Help Center.

Looker Studio Quick filters and filter bar feature Visual

Pause updates

Configuring multiple filters and charts for exploration can quickly add to the query volume, even with presence of a cache. We’ve heard from analysts that they want better control over running queries, so they can optimize query volume and, thus, query costs.

We have added the ability to pause updates, giving you the flexibility to fully configure chart elements like fields, filters, parameters, sorting, and calculated formulas before running any data updates. You can then simply resume updates to see the updated data. Pausing updates does not prevent any style changes, so you can continue to modify design elements and other detailed styles and formatting without running a single query. Learn more about this feature in our Help Center.

The new pause report updates feature in Looker Studio has meaningfully improved the report creation experience. Asset producers can build and test reports without wasting database resourcing waiting for data to reload. Caroline Bollinger
BI Tooling Product, Wayfair

View underlying data

Data accuracy is one thing — being able to see its detail is another. As analysts configure charts to build reports and design information hierarchy, previewing the underlying data is important for understanding context and seeing what data is available and its structure so you can make the best decisions about what to include in your analysis. It’s also handy when troubleshooting or customizing your reports.

This feature allows analysts to preview all the data that appears in a chart, including the primary dimensions, breakdown dimensions, and metrics. Learn more about how to view underlying data in our Help Center.

Looker Studio Data preview feature Visual

With this collection of updates, Looker Studio users can now easily know the data they share is up-to-date, inspect it in detail, rapidly create filters, and share personal links to reports. The goal remains, as always, to empower users to make smart and impactful decisions based on their enterprise data. To stay on top of all our latest features, view our release notes. Access Looker Studio for free and learn more about Looker Studio Pro.

Looker Studio Pro now available for Android and iOS

Fri, 03 Nov 2023 15:00:00 +0000

Looker Studio enables millions of users to bring their data to life with insightful dashboards and visualizations, connecting to more than 1,000 data sources and a host of community-sourced report templates. Looker Studio Pro expands on this self-service business intelligence platform with enterprise capabilities, including team content management and Google Cloud support. Today, we are bringing Looker Studio Pro to your mobile devices through a new application available for Android on Google Play and for iOS on the App Store, enabling you to view reports and get real-time data about your business from anywhere.

Looker Studio Pro mobile app features

Dynamic report layout: Visualize your data your way

No need to build new mobile specific layouts for your existing or new reports. If you choose a mobile friendly view, reports will be rendered to fit your mobile screen. This means that you can access all of the same information and functionality in a format that is optimized for viewing on a mobile screen.

Mobile Friendly view of your reports enables:

Improved usability: A mobile friendly view makes it easier to navigate and interact with your reports, even on a small screen.
Enhanced readability: Mobile friendly reports are designed to be easy to read on small screens, with larger fonts and more white space.

In the app, you can choose to view your reports in:

Original view - Optimized for desktop
Mobile-Friendly view - Optimized for your mobile screen

You can easily switch between the two views in the app from ‘options’.

Access all your reports hassle-free

Given the large number of reports to sift through, we sought to simplify and expedite the search for data by integrating Looker Studio Pro's categories into the app.

In the app, your reports are categorized:

My workspace - access all reports you created here.
Team workspaces - access all reports for teams you are part of.
Recents - a handy option to quickly find and access the reports you've looked at recently.
Shared with me - view and collaborate on reports shared with you.

Further, you can sort the reports by ‘Last opened by me,’ ‘Last modified by me,’ and ‘Last modified and Created’ to find the report you are looking for easily.

Share your reports with a simple tap

We know that collaboration and sharing insights with your team is important. Collaboration on mobile is made easy as you can now share reports with your team on the app of your choice with a single click. A link to the report will be generated that others can access easily on any device.

Moreover, you can access all the reports shared with you in ‘Team Workspaces’ and ‘Shared with me’ folders.

Seamlessly access interactive reports from your scheduled email or chat

Looker Studio Pro Mobile makes it easy to access your reports from your scheduled emails and chats. When you receive a scheduled report in your email/chat, tap the link to view and interact with your data live in the app. No more static PDFs!

How to get the Looker Studio Pro App

Getting your hands on the app is easy. Simply download the app from: Play Store or App Store and sign in with your corporate credentials.

Note: The mobile app is only available for Looker studio Pro customers. Learn more about Looker Studio Pro subscription here.

Getting to know Systems insights, a simplified database system monitoring tool

Fri, 13 Oct 2023 16:00:00 +0000

A slow running database is hard to diagnose. When problems arise, database teams want to immediately understand what is causing the issue. Is the database experiencing resource saturation? If so, what is consuming resources — an application query or an operation initiated by Cloud SQL? Are there any new changes in the database? Are there any background tasks such as vacuum and backup operations? Answering these questions isn't easy. It requires investigating and correlating metrics from multiple different sources to understand the state of the system and pinpoint where the issue is.

To make this process a bit easier, we designed System insights.

System insights is a database system monitoring tool that brings together critical metrics, events, and logs to provide a comprehensive view of both the external database performance and the internal system resources. Bringing all the signals into a single dashboard allows you to quickly identify potential sources of problems without having to switch between tools. Right now, System insights is available for Cloud SQL for PostgreSQL and Spanner in GA and Cloud SQL for MySQL in preview.

Fig 1: Cloud SQL System insights for PostgreSQL

Why design a new system monitoring tool?

Before System insights, users had two options for monitoring performance, both of which had significant user friction.

Metrics on the instance overview page:
While metrics shown here are useful, the interface did not allow users to compare and correlate multiple metrics at the same time — a key feature for successfully troubleshooting a database problem.
Custom dashboards:
Some users set up custom monitoring dashboards, either within Google Cloud or using third-party tools like Grafana. This provides great flexibility, but presents a steep learning curve to get going. Perhaps most importantly, it doesn’t let you see everything you need in a central place.

System insights addresses both of these challenges with a pre-built dashboard with richer, customizable metrics. This reduces the barrier to entry for novice users and facilitates complex troubleshooting journeys for advanced users, all within the database console UI.

In this blog, we walk you through the key features of System insights and the design rationale behind each of them.

Quick snapshot of system status

At the top of the System insights page, you’ll see a snapshot of your system’s key resources and its current status — a quick visual check on any potential issues that you may need to investigate. The system configuration helps you understand how the system is performing relative to the resources that were originally provisioned.

Fig 2: Snapshot of key system resources and their current status

Pre-built dashboard with actionable metrics

When designing system monitoring dashboards, we often run into the “problem of plenty” — there’s a wealth of metrics and data points to expose to users. How to decide on the right ones to surface? In Google Cloud, we cater to users with a wide range of database expertise and expectations. So, the key design challenge was to balance the needs of generalists and specialists.

We drew inspiration from observability frameworks such as RED (Rates, Errors, and Durations) and USE (Usage, Saturation, and Errors) and applied it to key system resources such as CPU, memory, active connections etc. This gives a comprehensive-yet-easily-digestible view of both the external and internal factors influencing database throughput. Within each metric, special attention is given to the way it is represented to make the underlying causes of behavior more readily identifiable. For example, disk storage is shown with a breakdown of data, logs and others, to clearly indicate which aspect of the disk is contributing to the increasing storage utilization.

Fig 3: Break-down of disk storage by type

Also, wherever possible, saturation limit lines are shown on resource utilization graphs. For metrics like CPU or memory, utilization percentage intuitively communicates saturation. However, the saturation limit for metrics like active connections is not straightforward. So, in such cases, based on the user’s current system configuration, we show a computed saturation limit to make it complete and actionable for users.

Correlation beyond metrics

To troubleshoot a database problem successfully, users often go beyond metrics and look into logs and system events. For example, a slow-running query without a proper index can create a long lock wait, resulting in increased error rates or CPU utilization, even if there is no change in the external workload. And it is not easy to understand the ‘why’ behind such behaviors just by looking at metrics. A trained database specialist will use a cloud logging tool along with system monitoring to see if there are any system events that coincide with a resource anomaly. This process involves not only switching between multiple tools, but also extracting fine-grained information from an ocean of logs that are not human-friendly to begin with.

Fig 4: Events timeline brings metrics and logs together

With System insights, we simplified this journey for users. Instead of expecting users to maneuver between multiple tools, we integrated events into the System insights dashboard. Key system operations are filtered from cloud logs and presented to users as a visual timeline, referred to as the ‘events timeline’. It’s a dynamic experience that allows users to naturally scroll through time and correlate metrics with system events.

Customizable views for specialist use cases

The dashboard defaults are a great starting point for most users. But for those who need more control over what metrics to monitor, we also provide customizable views. Users can add to the default selection of metrics, or choose a specific set of metrics that are more relevant to them. As System insights continues to expand its telemetry coverage and adds more dimensions to customize, this feature will address more of the needs of database specialists without making the default experience daunting for generalists.

Fig 5: Customisable views

To summarize, no matter where you are in your database system observability journey, System insights provides an easy to use starting point for generalists as well as provides customisable views for specialist use cases.

What next?

Our observability strategy is to build on a foundation of telemetry depth and ease of use. This effort began with Query Insights, a tool that helps developers fix query inefficiencies through visual query plans and latency analysis. In 2022, we launched proactive database wellness recommenders to help you increase the security, performance, reliability, and cost efficiency of your Cloud SQL fleet. Now, System insights extends that capability to system resource monitoring and troubleshooting.

We are actively working on improving complex troubleshooting journeys. Stay tuned for more!