Infrastructure

Cross-cloud infrastructure innovation for the agentic enterprise

Wed, 22 Apr 2026 12:00:00 +0000

The era of agentic AI is accelerating from human- to machine-speed operations, while also creating profound stress on legacy technology infrastructure. This new reality pushes foundational systems to their limits: agents generate thousands of internal messages and complex queries, spawning more agents, all of which can rapidly overwhelm traditional networks and databases, and expose new security vulnerabilities.

Unlocking AI's full potential in the era of agents requires a secure, adaptive foundation. We call it cross-cloud infrastructure for the agentic enterprise – and at Google Cloud Next ‘26, we’re launching a powerful set of new innovations across four areas:

What’s new:

Fluid compute: Google Compute Engine and Kubernetes services work together to enable cost-effective, high-speed AI agents and enterprise workloads with new compute and orchestration capabilities.
Secure cross-cloud connectivity: Agent Gateway, Cloud Armor, and other tools deliver a secure, governed, and simplified networking foundation for AI agents, including observability of agentic traffic across clouds.
Unified data layer: Smart Storage, Knowledge Catalog, and other innovations transform passive data archives into dynamic reasoning engines, giving AI agents the context they need to execute.
Digital sovereignty: Confidential External Key Management and new features in Google Distributed Cloud bring Google’s leading models and AI enablers wherever your data lives.

Let’s take a closer look at all the news for each of these four areas.

Fluid compute

Agentic workloads are dynamic and unpredictable, impacting both traditional enterprise applications and the AI agents themselves. Fluid compute is enabled by Google Compute Engine and Google Kubernetes services working together to dynamically adapt and shift weight in real-time, enabling cost-effective, high-speed AI agents and operational enterprise workloads for all customers.

While our AI Hypercomputer delivers raw power for large-scale AI model training, fluid compute addresses the needs of operational workloads and agents. As agents move toward reasoning and reinforcement learning, CPUs are reclaiming a central role, excelling at the "branchy" logic, complex control flows, and secure execution sandboxes (like those for agentic orchestration, RL, SLM inference, and RAG) that agent workflows demand. CPUs also provide the critical isolation needed for secure agent execution, complementing the parallel processing strength of GPUs and TPUs used in training.

We are introducing new CPU families, GKE capabilities, and Hyperdisk block storage capabilities to run traditional workloads and AI agents securely at scale, including:

Google C4N Series: These VMs help ensure your enterprise workloads don't slow down under the demands of agentic AI by processing up to 95 million packets per second, up to 40% faster than other leading hyperscalers. This eliminates I/O bottlenecks for demanding workloads like security appliances, streaming media, and open source databases, even when utilizing smaller instance sizes.
Google M4N Series with Hyperdisk Extreme: M4N removes data pipeline bottlenecks and eliminates overprovisioning to deliver industry leading per-core IOPS and throughput required to handle massive data I/O from agents, analytics, and mission-critical databases. M4N provides 26.57 GB of RAM per vCPU, allowing you to scale mission-critical workloads cost-effectively on fewer cores. For example, M4N with Hyperdisk Extreme reduces Oracle workload total cost of ownership by over 20% compared to leading hyperscale clouds.
GKE Agent Sandbox: This solution secures agents with trusted gVisor isolation and handles demand spikes, launching up to 300 sandboxes per second, per cluster. Backed by the only managed sandbox technology available among leading hyperscale clouds, it achieves up to 30% better price-performance than competitors when running AI agents on GKE Agent Sandbox with Google Axion N4A.

“Wayfair's AI strategy is built on years of systematic infrastructure modernization on Google Cloud — migrating our core eCommerce engine and databases off legacy systems, decomposing monolithic services into cloud-native architecture, and unifying our data and analytics platform. That foundation is what makes everything else possible. Today, Gemini Enterprise Agent Platform is powering everything from catalog enrichment to generative shopping experiences that help customers create a home that's just right for them — and it's the same foundation preparing us for the agentic era, where AI doesn't just assist but actively drives discovery, personalization, and commerce across every customer touchpoint and across our business.” - Fiona Tan, Chief Technology Officer, Wayfair

Explore all our latest compute innovations in this blog.

Secure cross-cloud connectivity

Agentic AI replaces predictable human requests with autonomous “reasoning loops,” in which agents call other agents that, in turn, call LLMs, triggering massive, sudden surges in compute and machine-to-machine traffic. This shift creates unique challenges for network predictability and security of non-human identities. Optimized for agentic AI, our Cross-Cloud Network moves data across diverse environments, connecting employees, customers, and agents with visibility and security. New in Cross-Cloud Network are:

Agent Gateway: Governs and orchestrates your enterprise agentic traffic as the “air traffic controller” in Gemini Enterprise Agent Platform. It natively understands agent protocols like MCP and A2A to inspect and govern every agent interaction. By integrating with Google and third-party identity and AI safety services, it enables deep inspection to verify access, block attacks, and protect sensitive data, maintaining compliance across your core business.
Cloud Network Insights: Delivers broad visibility across your hybrid and multi-cloud infrastructure to drive faster troubleshooting and network resolutions. Continuously monitor your end-to-end agent, network and web performance across Google Cloud, AWS, Azure, data centers, internet applications, and agentic workloads. Using synthetic traffic analytics, Cloud Network Insights provides hop-by-hop network path visibility to help you pinpoint the source of degradations and is coupled with AI-powered insights from Gemini Cloud Assist to deliver more autonomous operations.
Enhanced Cloud Next Generation Firewall (NGFW) and Cloud Armor: Provides machine-speed, AI-powered protection to combat the rapid explosion of AI-generated polymorphic malware and zero-day exploits. Cloud NGFW advanced malware sandbox delivers real-time inline prevention of AI-generated threats, while Cloud Armor managed rules provides automated protection against both known and unknown Common Vulnerabilities and Exposures (CVEs). Together with Model Armor, these services analyze the intent and content of AI agent communications.

Discover more about how we optimized networking for AI in and outside of the data center.

Unified data layer

AI agents are only as powerful as the data they can access and the context they’re given. More applications and platforms are using structured and unstructured data, but it can be difficult to catalog, find, and act on that data at scale, leading to less effective agent interactions. To close the gap, your agents need all of your data brought together into a cohesive, queryable knowledge engine, or unified data layer. This way, your agents can identify and access accurate sources. At Next ‘26, we’re enhancing the unified data layer with:

Smart Storage: This solution transforms dark data into a powerful knowledge asset for AI agents and training by embedding new semantic intelligence directly into your data objects. With new Google Cloud Storage capabilities like automated annotation, entity extraction, and semantic search, your agents can instantly find and use the specific data they need — whether it's hidden in spreadsheets, PDFs, or other unstructured formats across your entire organization. This significantly speeds up the development and deployment of your AI solutions. Learn more about storage innovations to accelerate your AI workloads.
Knowledge Catalog: Knowledge Catalog maps business meaning across your entire data estate, providing a grounded source of truth so agents can deliver the most accurate results. This foundation enables AI training and inferencing and doesn’t require you to migrate your data; your agents interact with it directly, wherever it lives, with full context and governance, making modernization easier.

Part of our Agentic Data Cloud, Smart Storage and Knowledge Catalog can take your data from a passive archive into a dynamic reasoning engine.

“AI is critical to making our customers’ smart home and security solutions more intelligent and convenient. By leveraging Google Cloud’s Smart Storage, we auto-annotate rich metadata delivered in BigQuery. We’ve scaled and accelerated our data discovery and curation efforts, speeding up our AI development process from months to weeks, continuously delivering innovations that build trust and enhance the overall home experience.” - Brandon Bunker, VP of Product, AI, Vivint

Digital sovereignty

In the agentic era, digital sovereignty is a fundamental requirement for public sector and enterprise customers looking to accelerate innovation — without sacrificing control. There’s no one-size-fits-all solution, which is why we’ve designed a comprehensive set of offerings to meet different sovereign AI needs anywhere: public cloud, on-premises, or hybrid. New capabilities in our sovereign AI portfolio include:

Confidential External Key Management: Organizations can use Confidential External Key Management to maintain complete possession, custody, and control of your encryption keys and the policies that govern them. Confidential External Key Management leverages Confidential Compute to host the key management endpoint in a tamper-proof environment within Google Cloud. You are in control and determine where your keys are stored, who can access them, and under what circumstances. Even highly privileged Google administrators cannot access your keys without authorization, which you can revoke at any time. Your data, your control.
Gemini on Google Distributed Cloud: With Gemini on GDC, companies can securely deploy Gemini in sensitive environments, while meeting data sovereignty needs. Your choice of deployment models includes managed software on your connected hardware or a fully disconnected, air-gapped solution. You can now scale with Google's leading AI capabilities even in the most restricted, high-security environments — from powerful Gemini models to advanced coding, search, and other agentic capabilities.

In addition, Google Distributed Cloud supports an end-to-end AI stack, combining our latest-generation AI infrastructure with Gemini models to accelerate and enhance all your sovereign AI workloads. This stack includes:

NVIDIA Blackwell GPUs: NVIDIA Blackwell (NVIDIA HGX B200) and NVIDIA Blackwell Ultra platforms (NVIDIA HGX B300) GPUs accelerate AI performance, leveraging fifth-gen NVIDIA NVLink to deliver data-center scale bandwidth directly to your environment.
New VM families: New A4 family offerings provide the ability to handle the most demanding inference tasks, delivering a 2.25x increase in peak compute. Memory-Optimized M2 and M3 brings the high memory-to-vCPU ratios needed for massive ERP and data analytics workloads on-premises.
Enhanced storage: Eliminate storage bottlenecks with 6x storage capacity per zone and a 10x performance boost, giving you the ability to do AI reasoning on-premises. Now, your data infrastructure moves at the speed of AI reasoning.

"Our customers demand high-performance, private AI inference without the risks of multi-tenancy. Google Distributed Cloud allows us to provide dedicated, low-latency environments that meet strict sensitive data requirements. With the ability to run Gemini on B200s and B300s, we can significantly increase inference speeds and provide the token throughput our clients need to scale." - Dave Driggers, CEO & Co-founder, Cirrascale Cloud Services

Transforming vision into reality

When these product areas converge, your infrastructure evolves into a high-performing, secure, adaptive foundation for the agentic era. We're not just offering tools; we're providing the architectural blueprint to enable enterprises and the public sector to rapidly embrace the full power of AI and agents with confidence.

To learn more about key industry trends for AI Infrastructure, read our State of Infrastructure in the Agentic AI Era report.

Introducing Virgo Network, Google’s scale-out AI data center fabric

Wed, 22 Apr 2026 12:00:00 +0000

The AI era requires a fundamental rethink of physical cloud architecture — networking, in particular. With foundational model parameters growing exponentially, traditional general-purpose networks are reaching their breaking points. To fuel the next decade of machine learning, Google designed Virgo Network, a new megascale AI data center fabric that embraces a "campus-as-a-computer" philosophy, and that underpins our AI Hypercomputer.

Legacy network designs simply cannot handle some of the constraints of modern AI:

Massive scale: Training demands now exceed the power and space of a single data center, requiring unified, multi-data-center domains.
Explosive bandwidth growth: Because foundational model training is heavily network-bound, the required bandwidth per accelerator has surged significantly over the last few years, creating throughput and congestion bottlenecks for older architectures.
Synchronized bursts: Intense, millisecond-level traffic spikes (figure 1) put immense pressure on network buffers. The outcome is that even a single "straggler" node can throttle the entire cluster’s performance.
Low latency: ML serving requires fast, consistent response times to deliver real-time inference, making strict latency control a critical architectural constraint.

Figure 1: Sub-millisecond line-rate bursts of an AI training workload

Reimagining the data center network

Meeting the demands of the AI era requires a fundamental shift away from general-purpose network design towards a specialized flat, low-latency network architecture. To address the unique scale and latency constraints, we leverage our proven Jupiter network for north-south traffic and are introducing a new fabric for east-west communication. The resulting architecture consists of three distinct and specialized layers that operate as one unified compute domain:

Scale-up domain: A high-bandwidth, low-latency interconnect fabric designed for tightly coupled communication between accelerators within a single pod.
Scale-out accelerator fabric (east-west): A dedicated accelerator-to-accelerator remote direct memory access (RDMA) fabric optimized for massive horizontal scale across pods. This layer is engineered for deterministic latency and maximum resilience, to provide high “goodput” for the ML workload.
Jupiter front-end network (north-south): A high-capacity fabric that provides fast, reliable access to distributed storage and general-purpose compute resources. It ensures that data access does not become a bottleneck for training and serving workloads, and is also used to scale-across multiple sites for very large training runs.

This architectural decoupling provides key strategic advantages:

Independent evolution: We can evolve and upgrade each network domain independently, preventing system-wide disruptions while accelerating the innovation cycle.
Dedicated scale-out bandwidth: A non-blocking network delivers massive bisectional bandwidth to accelerators for critical training tasks.
ML and network co-design: The network is built in lockstep with each new generation of ML accelerators, helping ensure the fabric is matched to the hardware it supports.

Figure 2: Data center network architecture

Introducing Virgo Network: Megascale data center fabric

Virgo Network is a scale-out fabric designed for the extreme requirements of modern AI workloads. Built on high-radix switches that reduce network layers by allowing more ports per switch, it employs a flat, two-layer non-blocking topology. Compared with traditional datacenter networks, this significantly reduces latency by minimizing network tiers. It features a multi-planar design with independent control domains to connect accelerators (figure 3). The accelerator racks also connect with the Jupiter north-south fabric to access compute and storage services. Together, this streamlined architecture delivers the massive bisection bandwidth and deterministic low latency necessary for both distributed training and serving workloads.

Figure 3: Megascale data center fabric (Virgo Network)

Virgo Network is the foundation of our next-generation accelerator designs and delivers the following advantages:

Massive fabric scale: Virgo Network can link 134,000 chips (TPU 8t) with up to 47 petabits/sec of non-blocking bi-sectional bandwidth in a single fabric.
Generational performance leap: With up to 4x the bandwidth per accelerator (TPU 8t) over the previous generation, Virgo Network delivers the bandwidth you need to get the full power of every chip.
Predictable low latency: Virgo Network delivers 40% lower unloaded fabric latency for TPUs compared to previous generation leading to more predictable performance for latency sensitive AI workloads.

Improving reliability at scale

In a system supporting hundreds of thousands of chips, hardware failures are inevitable. Because a single faulty component can disrupt a synchronized training job, reliability at scale is a primary focus. To maximize workload goodput, we designed the Virgo Network architecture around fault isolation, deep observability, and the rapid mitigation of hangs and stragglers.

At this scale, system-wide resilience requires a solid network foundation. Virgo Network integrates independent switching planes that provide robust fault isolation, protecting cluster-wide goodput from being degraded by localized hardware failures.

Figure 4: How fail-stop and fail-slow impact MTTR

Building on this foundation, we optimize the software and orchestration stack to maximize mean-time between interruptions (MTBI) and minimize mean-time to recovery (MTTR) through two primary areas:

Observability: Reliability at scale requires high-fidelity visibility. We use sub-millisecond telemetry to monitor network systems. This deep visibility allows us to detect transient congestion, optimize buffer management, and pinpoint the root causes of slowdowns across the hardware and software stack.
Identifying stragglers and hangs: Proactive monitoring is critical for identifying nodes that are experiencing performance degradation (stragglers) or that have stopped responding completely (hangs). By rapidly localizing these bottlenecks, with automated straggler and newly added hang detection, we accelerate the training job and protect it from localized slowdowns.

The foundation of the AI Hypercomputer

Virgo Network is a reimagined scale-out data center network custom-built for the stringent demands of modern AI workloads. This flat, multi-planar architecture unifies accelerators across pods into a single compute domain, addressing the bandwidth and scale limitations of traditional networks. By providing robust fault isolation directly at the hardware level, Virgo Network serves as the foundation for system-wide resilience, protecting synchronized workloads from localized hardware faults.

Ultimately, Virgo Network delivers the scale, predictable latency, and reliability necessary to accelerate the agentic AI era. To learn more about how we are building infrastructure for the future of AI, visit our AI infrastructure solutions page, explore the technical documentation, or attend the dedicated breakout session at Google Cloud Next.

Startups are building the next big thing with Google Cloud AI

Wed, 22 Apr 2026 12:00:00 +0000

The future is taking shape in Las Vegas this week, where the world’s leading startups are showcasing their groundbreaking AI work at Google Cloud Next. Whether they need the top AI models, infrastructure purpose-built for the most demanding workloads, intelligent multi-model databases, or security and sovereignty solutions to grow with confidence — or all of the above — startups of all sizes and in every industry are choosing Google Cloud.

To take one example of thousands, Lovable, the popular software creation platform, utilizes Google Cloud infrastructure and builders are now creating over 200,000 new projects on Lovable every day. OpenEvidence, a medical AI platform widely used by U.S. physicians, uses Google's AI tools to provide evidence-based answers at the point of care; the company recently announced major partnerships with Wiley and Mount Sinai, expanding access to trusted medical research and to its AI assistant.

This week at Next we’re announcing even more startups who are starting or expanding their work with Google Cloud. These include leading names in areas like vibe coding, model training and inference; healthcare and life sciences; image generation; financial services; gaming and much more. It also includes an exciting new group of startups who have recently begun accessing GPU-based AI compute through Google Cloud’s AI Hypercomputer, thanks to our deep technical partnership with NVIDIA.

Google Cloud has always offered startups the entire AI stack in one unified place, giving founders maximum velocity. With new exciting launches at Next ‘26, the options and opportunities are only growing.

We’re helping you build complex agents faster with Gemini Enterprise Agent Platform, optimize compute needs with two differentiated TPU offerings for inference and training, reduce data friction with our next-gen cross-cloud Lakehouse, and better govern everything with the help of Wiz AI agents and Google-scale threat intelligence, to name just a few advances. We’ve also unveiled the ability to sell your AI solutions directly to enterprise users through marketplace integrations in Gemini Enterprise.

Supporting startup innovation across categories

If there was any question of just how dramatically AI is transforming every facet of work and life, look no further than the innovations being brought to market by our startup customers.

Coding and developer tools

Emergent AI uses Google Kubernetes Engine (GKE) as its core infrastructure to orchestrate thousands of on-demand, isolated development sandboxes for vibe coding. By leveraging GKE Autopilot for automated scaling and Gemini models for code generation, the platform transforms natural language prompts into live, production-ready applications with instant previews.
Lovable is expanding its work with Google Cloud and launching a new coding agent, now available to purchase through Google Cloud Marketplace and Gemini Enterprise.
Parallel builds high accuracy web search and research APIs, purpose-built for AI agents. Leveraging Google Cloud offerings including Gemini, BigQuery, BigTable, and Spanner has enabled them to build, launch and massively scale their platform since launching in August 2025.
Temporal partnered with Google Cloud to build a unique Application Development Kit (ADK) integration. This enables organizations to deploy agentic systems for mission-critical use cases that require strong execution guarantees, uptime, auditability, and operational resilience.

Customer engagement

Satisfi Labs is an agentic AI platform designed to transform customer engagement for destinations and experiences, serving 800 clients across sports, entertainment, and tourism. They are built on the Google Cloud ecosystem and leverage technologies including Gemini, BigQuery, AlloyDB, and Datastream to enable intelligent interactions, scalable data processing, and real-time insights.
Vapi, a platform for developers creating conversational voice AI, leverages Gemini as its intelligence core, allowing developers to maximize their compute-per-dollar while delivering a premium voice experience with incredible scale.
Vurvey Labs leverages Gemini Enterprise Agent Platform to build agent populations and simulated worlds that predict human behavior and future needs.
ZenBusiness utilizes Gemini Enterprise Agent Platform to simplify business ownership, including the launch of 'Velo,' an AI assistant for formation, compliance, and website building.

Document intelligence

Reducto leverages Gemini Enterprise Agent Platform and AI Studio alongside its core models to automate high-stakes workflows and power production AI pipelines at scale.

Finops

Mavvrik is a FinOps platform using Gemini and BigQuery to power their unified cost management platform for FinOps, IT, and finance leaders to gain visibility, automate allocation, and govern spending across public clouds, private clouds, AI workloads, and SaaS applications.

Generative media

ComfyUI is streamlining complex node-based gen media production by integrating Nano Banana Pro while improving the consistency of their customer experience using Provisioned Throughput.
Gamma used by over 70 million people to create presentations, documents, websites, and social posts, uses Nano Banana 2 to instantly generate localized visuals and transform ideas into ready-to-share content in seconds.

Industry AI

Chorus, spun out of Alphabet’s X innovation arm, is building AI orchestration tools to transform how physical assets are made, moved, and managed across industries. Leveraging Gemini Enterprise Agent Platform, advanced machine learning, and proprietary algorithms, Chorus provides granular asset-level visibility, temperature intelligence, and dynamic delivery estimates across mission-critical supply chains.
ExaCare AI, an AI platform for post-acute care operations, is using Gemini Enterprise Agent Platform and Gemini for inference and to process clinical and operational data across admissions and reimbursement workflows.
Industrility, an AI-powered aftersales platform for machine manufacturers, built a maintenance intelligence engine on Google Gemini’s advanced language models, extracting critical maintenance tasks and reducing a two-month manual project to three hours.
Insilica uses Gemini Enterprise Agent Platform to process millions of toxicology regulatory documents and thousands of databases into a unified, agentic risk assessment platform.
Proximal Health utilizes DocAI to automate the insurance claims adjudication process.
Stord, which offers fulfillment and software for omnichannel retail, leverages Gemini Enterprise Agent Platform to build ML models that predict estimated delivery dates and optimize demand planning across millions of orders, all grounded in real data behind over $10B in annual Gross Merchandise Value.
Stylitics uses Gemini Enterprise Agent Platform to power more than 1,000 AI programs for world-class retailers, including AI image generation, outfitting and styling, and AI analytics.
Wand uses Gemini Enterprise Agent Platform to power Game Guide, a contextual companion built into their single-player PC gaming platform, that helps players get unstuck, uncover hidden content, and stay immersed without ever leaving the game window.

Productivity

Notion is at the forefront of integrating artificial intelligence into productivity tools, leveraging Gemini models to power its text and image generation capabilities.

Sustainability

Watershed, a leading enterprise climate platform, is leveraging Google Cloud to transform sustainability data into actionable climate intelligence, accelerating global decarbonization and enabling organizations to move from measurement to action at an unprecedented scale.

Supporting model training and inference with NVIDIA AI infrastructure

Today, when the world’s most innovative startups need to access NVIDIA’s cutting-edge GPU-powered compute, many of them rely on Google Cloud. Our longstanding partnership with NVIDIA allows these startups to train and serve AI models, support fine-tuning, and scale new agentic workloads.

This week at Google Cloud Next, we’re highlighting the successes these customers are having and announcing exciting new partnerships with more startups choosing to utilize NVIDIA AI infrastructure and Google Cloud’s AI stack.

These new and expanding customers include:

Thinking Machines Labs will utilize the latest NVIDIA Blackwell chips through Google Cloud, including being among the first Google Cloud customers to utilize GB300s. In early testing, Thinking Machines has seen training and serving speed increase 2x on GBs compared to prior generation chips.
GMI Cloud leverages Google Cloud’s GPU infrastructure to run its inference-optimized platform, powering GMI Studio workflows and large-scale model serving across text, image, and video. This infrastructure supports high utilization driven by growing demand for real-time, production-grade AI applications.
Inferact is developing vLLM, a highly performant and easy to use inference engine for open source LLMs on data center hardwares, utilizing a cluster of NVIDIA GB200’s on Google Cloud, as well as services like Managed Lustre and Cloud Filestore.
Neuraldefend is partnering with Google Cloud and NVIDIA to utilize H100 and L4 GPUs for model training and production inference for their real-time deepfake detection engine and digital trust solutions.
Nucleus AI is utilizing a cluster of NVIDIA H100 GPUs through Google Cloud for model training.
Reflection has expanded its usage of G4 VMs through Google Cloud, as well as services like Google Cloud Storage to support training and inference for its open-weight models.
Reve uses A3 Mega (NVIDIA H100 GPUs) and A3 Ultra (NVIDIA H200 GPUs) instances on Google Cloud to handle their intensive multimodal foundation model training and inference. They also use GKE to manage their high performance training needs.

Startups most commonly access NVIDIA GPUs through Google Cloud G4 VMs, powered by NVIDIA’s latest Blackwell infrastructure. We’re also excited to offer NVIDIA’s Vera Rubin architecture to startups later this year. Our deep partnership with NVIDIA means that the Google Cloud AI Hypercomputer is highly optimized for GPU customers and can deliver ultra-low latency, high-throughput, and cost-effective performance across multiple use cases.

Startups are choosing to utilize GPUs through Google Cloud in part because of this high level of optimization and because they can easily access highly differentiated technologies in our AI stack like Gemini Enterprise, Agent Builder, BigQuery, Google Kubernetes Engine, and much more.

We also continue to partner closely with NVIDIA to support early-stage startups with AI compute and cloud services through NVIDIA’s Inception program and our Google for Startups Cloud Program. This includes exciting startups like:

Aible, which is building long‑running enterprise AI agents. They are accessing GPUs on Google Cloud to help them run Nemotron 3 and NemoClaw agents directly on BigQuery, orchestrate multi‑agent blueprints like the Intelligent Warehouse, and deploy serverless RTX‑accelerated inference on Cloud Run.
Baseten provides a managed inference platform that optimizes model serving for large language and vision models. They are accessing GPUs on Google Cloud to help them deploy NVIDIA‑accelerated inference with improved tokens‑per‑second, cost‑per‑1K requests, and GPU utilization for customers running production gen AI workloads.
CodeRabbit builds AI-driven workflows for code review, supported by Nemotron models via Google Enterprise Agent Platform, delivering low-latency PR summarization, embedding-powered context enrichment, and scalable concurrent review pipelines.
Factory AI develops autonomous code-editing agents that can traverse complex code graphs, resolve dependencies, and generate safe patches against real production repositories. Nemotron models in Gemini Enterprise Agent Platform allow Factory AI to power Nemotron Super-driven "droids" that perform semantic code search, tool-augmented reasoning such as grep and static analysis, and multi-file refactoring plans at scale.
Mantis AI is creating a video‑native intelligence layer for media and entertainment companies across Latin America. Through NVIDIA’s Inception program and its collaboration with Google Cloud, Mantis AI operates high‑throughput inference pipelines, rigorously benchmarks fine‑tuned model variants, and experiments with NVIDIA NIM, Blueprints, and vector search services to deliver production‑ready stacks that help broadcasters and streamers move from analyzing video to orchestrating it at scale.
Photoroom specializes in diffusion‑ and transformer‑based vision models that generate and edit product imagery. Photoroom processes over 1,000 images per minute. They use TensorRT for a 4x speedup and xformers to make their Stable Diffusion pipelines 100% faster. They’ve consumed over 500,000 GPU hours on Google Cloud without issue.

Sell agents directly in Gemini Enterprise

For AI startups, navigating enterprise procurement is often a bottleneck to growth. By integrating the Google Cloud Marketplace into Gemini Enterprise, we're providing a global commercialization engine to accelerate procurement.

Today at Next, we’re announcing an exciting group of more than 70 startups who are bringing their agents to our Marketplace.

And, to help even more startups build agents and bring them to market, we are launching a new $750 million dollar fund to support agent development and marketing for our partners. You can read more about this new funding here.

Presenting the Gemini Startup Forum

In June, we’re hosting our Global Gemini Startup Forum, a two-day, in-depth gathering of founders from seed to Series A startups. At the event, attendees will engage with Google leaders and engineers, build and scale products, and share feedback with teams across our AI portfolio. Take a deeper look at the program and register your interest for upcoming editions.

Building on that momentum, we’re also excited to share that applications are open for our next program: the Gemini Startup Forum: Cybersecurity, taking place in London later this year. If your startup is utilizing AI to build the next generation of security solutions, we invite you to apply to join us!

Join the Google for Startups AI Agents Challenge

Startups, it's your chance to get hands-on right away. We’re excited to launch the Google for Startups AI Agents Challenge at Next 2026.

Open to anyone (not just Next attendees), this global competition runs for six weeks and gives teams $500 in credits and the tools — like the new Gemini Enterprise Agent Platform — to build autonomous systems and compete for a share of a $90,000 prize pool.

The challenge, which opens for submissions today, and runs until June 5, 2026, offers three tracks tailored to where you are in your development journey. You can build a net-new agent from scratch, optimize an existing prototype for production reliability, or refactor a business-ready agent for potential enterprise distribution on Google Cloud Marketplace and the Gemini Enterprise app.

Submissions will be evaluated based on the following weighted criteria: Technical Implementation (30%), Business Case (30%), Innovation and Creativity (20%), and Demo and Presentation (20%). Learn more here and sign-up now.

Read the new Future of AI: Perspectives on generative media for Startups report

Our Future of AI: Perspectives on generative media for Startups report compiles exclusive interviews with eight startup founders, investors, and Googlers to capture their candid advice, strategic priorities, and predictions for the industry's future.

Understanding these firsthand founder perspectives will be instrumental in helping you navigate the next wave of AI-driven content creation and build the future of generative media on Google Cloud — and maybe find yourself featured on a list like this at a future Next.

How WPP accelerates humanoid robot training 10x with G4 VMs

Thu, 16 Apr 2026 16:00:00 +0000

Editor’s note: Today we hear from Perry Nightingale, SVP of Creative AI at WPP about the workflow that cuts training time for humanoid robots from days to minutes — plus access to the open-source code to do it yourself.

Robots are pushing the boundaries of what content creators and directors can capture. These technologies have become critical in the film industry because they open up new possibilities for controlled camera moves in locations where traditional methods would be unsafe or infeasible.

We’ve found that the programming for these robots is arguably as technical and complicated as the shoots they’re being tested on. To achieve our goals, we needed a hardware stack that was equally advanced as the robots we’d be programming. In this post, we explore how WPP used the new G4 VM instance powered by NVIDIA RTX PRO 6000 Blackwell on Google Cloud, which is a great fit for the unique challenges of training physical AI.

While robotics is always a complex task, especially in the environments we were working in, thanks to the unique software stack we were using, we were able to reduce training cycles from 24 hours down to less than one.

Below, we detail the specific reinforcement learning (RL) workflow, the challenges of the "sim-to-real" gap, and the infrastructure that makes it possible.

The lessons learned here apply far beyond entertainment. Our process for mastering complex, natural movement on a film set can be replicated across industries to overcome the massive computational complexity of training robots.

Where we started: redefining the agency model

WPP is one of the world’s largest marketing organizations, handling $70 billion of media for enterprise clients. For us, building AI into our production workflows meant fundamentally redefining the agency model, both in terms of processes, relationships, and tools. Notably, we launched WPP Open last year, our proprietary AI operating platform, where we’re able to take the best of Gemini’s multimodal intelligence, along with other models, and incorporate them directly into every creative step.

The results were immediate. For one of our clients, Verizon, we built an AI-infused promo pipeline that delivered 15 videos in 70% less time, with 50% to 70% efficiency gains across the production cycle. WPP Open has proven so effective for our teams, we’ve begun offering it to our clients, so they can tackle projects in new ways and we can collaborate faster and better.

WPP Open has also challenged and inspired us to look for more ambitious applications of AI. With the latest advancements in Google Cloud’s AI Infrastructure, we saw an opportunity to tackle more complex problems at the cutting edge of creative development.

Why teach a robot to dance?

Our robotics work started with teaching a machine to dance — and not just because we knew a dancing robot would make for a compelling demo. Dance, along with martial arts, is generally accepted as the cutting edge of complex human motion. Mastering these complex movements is a critical step toward achieving natural robotic motion.

For our benchmarking project, we trained our robot to perform a dance sequence captured in a previous project with Universal Music Group.

The workflow

To achieve this complex motion, we needed a workflow that could iterate fast.

We captured human motion with the OptiTrack mocap system and retargeted it to an official OpenUSD digital twin of the robot. This is a complex engineering challenge: a human has over 200 degrees of freedom compared to just 29 on a robot. So our team needed to remap the human skeletal data to the far more constrained physical structure of our robot, creating a sophisticated 3D model.

MuJoCo, an open source physics engine by Google DeepMind, was a critical piece of simulation software that validated our accuracy continuously, in real-time.

The workflow then moves to reinforcement learning. In our previous environment using single on-premises GPUs, training took ten hours. This time, we used G4 VMs, together with the NVIDIA Isaac Sim image on Google Cloud Marketplace. Google Cloud’s unique innovations, such as… with G4 enabled us to utilize a P2P topology that moves data directly between GPUs without the bottleneck of central processing.

Using Google Cloud’s AI Hypercomputer, we saw speed increases in excess of 10x, taking training times down to less than one hour.

Over the training run, the digital twins of the robots are rewarded for getting closer to the intended motion sequence while under the simulated effects of physical gravity, momentum, friction, and small simulated “pushes” that might be expected forces in the real world.

As the simulation training begins, the robots fall almost instantly (top). After about 3 billion simulations, the robots have learned the complex dance sequence (bottom).

Bridging the "sim-to-real" gap

The "sim-to-real" gap is one of the toughest challenges in robotics. A policy that works perfectly in the above workflow often fails in the physical world due to unmodeled physics or sensor noise. A foot might land differently due to small changes in carpet friction or a gap in the floor.

We ran billions of simulations to develop the reinforcement learning model, which was then condensed into an ONNX policy and deployed to the robots. These policies take real-time observations from the robot — IMU data, joint positions, etc. — and return the necessary movements.

Through high-volume simulation, the humanoid learned how to respond to these small changes and determine what move to make next to keep the sequence on track. Again, we used MuJoCo for critical real-time validation, ensuring that the robot's ability to adapt in simulation translated to safety and stability in the real world.

What’s Next

To accompany this project, Unitree has released their in-house reinforcement learning code as a sample project on GitHub. Alongside the NVIDIA Isaac Sim image on Google Cloud Marketplace, this means almost instant access to advanced robotic motion research.

Check it out for yourself:

Explore: Unitree Robotics RL on GitHub
Deploy: NVIDIA Isaac Sim on Google Cloud Marketplace
Learn more: G4 VM Instance Documentation

Top Infrastructure and GKE Sessions at Cloud Next '26

Wed, 01 Apr 2026 17:00:00 +0000

With Cloud Next '26 rapidly approaching, we couldn't be more excited to showcase how Google Cloud empowers organizations to scale smarter, faster, and more efficiently.

Be sure to anchor your experience by attending the Infrastructure spotlight by Mark Lohmeyer, VP & GM of AI and Computing Infrastructure, where he will detail the future of our AI and compute ecosystem. And to learn more about Google’s agentic cross-cloud infrastructure, the spotlight by Muninder Sambi, VP & GM Networking, and Drew Bradstock , Sr. Director, Compute and Kubernetes Orchestration is a great starting point.

To help you navigate the immense opportunities at Cloud Next, we've curated a set of essential breakout sessions across cloud infrastructure encompassing: Compute, AI Infrastructure, and Google Kubernetes Engine (GKE). Here are our top picks to optimize your Next agenda:

1. Overview and big picture

If you want to understand the strategic direction of Google Cloud infrastructure, this is where you start. We are covering the massive momentum behind our AI Hypercomputer and Compute Engine's AI-ready architecture, alongside the latest advancements in Google Distributed Cloud. This is also a particularly special year after wrapping up a decade of innovation on GKE, so join us as we explore what's new on GKE and how it helps businesses transform with AI.

BRK2:110 - What’s new with GKE: A look towards the next decade

BRK2-118 - What’s new for AI on GKE: Training, serving, and agents

BRK1-075 - What's new with Google Distributed Cloud?

BRK1-068 - AI hypercomputer: Resilient AI infrastructure at scale

BRK2-166 - What’s new with Compute Engine's workload and AI-ready infrastructure

BRK2-111 - Automating excellence: How Gemini and Config Connector help create 10x cloud teams

2. Migration and modernization

Transforming legacy environments is no longer just about moving to the cloud—it is about establishing a secure, AI-ready foundation wherever your workloads need to live. This category explores how to modernize complex enterprise infrastructure—from mainframes and VMware to Oracle —and seamlessly extend those capabilities to the edge and across multi-cloud environments. Discover how to reimagine modernization with Gemini and AI-driven migration factories.

BRK2-123 - From assessment to production: Building an AI-driven migration factory

BRK2-180 - Unlock Google Cloud's migration secrets: Security, speed, scale

BRK2-181 - Reimagining mainframe modernization in the Gemini era

BRK2-183 - Accelerate your path to AI readiness with Google Cloud VMware Engine

BRK2-184 - Migration accomplished: a customer panel on moving to an AI-ready cloud

BRK1-143 - Beyond lift & shift: Building AI-powered Oracle Workloads on Google Cloud

BRK2-195 - AI at the edge: Transform operations at the edge with Google Distributed Cloud

3. High-performance compute and AI infrastructure

The bedrock of any modern AI application is powerful, efficient, and scalable compute. In this category, we are diving deep into the hardware and architecture powering frontier AI, showcasing the latest advancements across our Cloud TPU and GPU roadmap. You will hear directly from industry pioneers like OpenAI, Anthropic, and Citadel on how they are architecting their hybrid HPC and Kubernetes clusters to push the absolute limits of inference, training, and research at scale.

BRK2-171 - The latest from Google’s TPU roadmap: Architecting for frontier AI

BRK2-125 - Build and scale SOTA inference: High performance on TPUs and GPUs with llm-d

BRK2-120 - How OpenAI builds Kubernetes GPU clusters

BRK2-124 - Build resilient AI: Maximize TPU performance and scale with GKE workloads

BRK2-172 - Scaling Claude: Inside Anthropic’s TPU strategy and architecture

BRK2-192 - Scaling Cloud’s infrastructure for the AI era

BRK2-176 - Accelerating alpha: Citadel's hybrid HPC and AI strategy

BRK2-177 - Next-gen pharma R&D: Redefining discovery with HPC and agentic AI

BRK3-036 - Architecting rack-scale AI: Inside Nvidia Blackwell on Google Cloud

BRK2-174 - AIOps at scale: Production-ready infrastructure with Cluster Director

4. Agentic AI and the full AI workload lifecycle

Moving from AI experimentation to production requires orchestration and highly specialized infrastructure. This category explores the lifecycle of AI workloads, from large-scale model pre-training and reinforcement learning to cost-effective inference on CPUs and TPUs. Dive into the practical deployment of secure, agentic AI applications to rapidly accelerate developer velocity.

BRK2-112: Untrusted code, unprecedented speed: High velocity runtimes for AI agents

BRK2-126: Vibe coding agentic apps: Build secure workflows with GKE and Data Cloud

BRK2-194: Build agentic AI with Gemini & dev platforms on Google Distributed Cloud

BRK3-039: Improve AI developer velocity with Agentic cross-cloud network

BRK1-070: Accelerating the full lifecycle of large-scale model pre-training and RL fine-tuning

BRK1-067: CPU infrastructure for the age of inference: Lessons from industry leaders

5. Scale, Performance and Cost

For the practitioners engineering at the cutting edge, balancing extreme performance with cost efficiency is the ultimate challenge. This highly technical track explores how to build a global compute fabric and orchestrate secure, planetary-scale AI on Kubernetes. Discover how to navigate infrastructure complexity with Gemini, implement next-gen FinOps, and leverage advanced inference playbooks to maximize your compute value without ever compromising on reliability.

BRK2-127: Engineering the future of Kubernetes for AI at scale

BRK1-069: Scale open model serving on TPUs

BRK2-121: Snap-scale AI: Building a global compute fabric with GKE custom compute classes

BRK2-114: Limitless AI orchestration on GKE: Powering secure, planetary-scale AI

BRK2-113: The GKE inference playbook: Optimizing cost and performance

BRK1-072: Next-gen FinOps for the AI era

BRK2-167: Intelligent compute infra: Designing for performance, reliability, and cost

Register now. We hope to see you in Vegas, where you can join fellow visionaries, practitioners, and Google engineering experts to architect the future of cloud infrastructure. Whether you are scaling the next massive foundation model or celebrating a decade of Kubernetes innovation alongside us, we cannot wait to help you unlock new possibilities for your organization. See you there!

Firefly: Illuminating the path to nanosecond-level clock sync in the data center

Mon, 23 Feb 2026 17:00:00 +0000

From the high-frequency trading floors of Wall Street to orchestrating cloud data centers, the ability to synchronize events with nanosecond accuracy is critical. Yet, achieving this level of temporal precision across thousands of interconnected devices in a modern data center is fraught with challenges like clock drift, network jitter, and path asymmetries. And doing so on cloud-hosted infrastructure has traditionally been impossible, preventing a certain class of applications from running there.

This is where Firefly, a clock synchronization system developed by researchers and engineers at Google, comes in. Firefly isn't just a clock synchronization protocol; it's a software-driven approach that combines theoretical insights and practical engineering to deliver ultra-accurate, scalable, and cost-effective time synchronization on commodity hardware within a demanding data center environment.

The nanosecond race: Why precise timing matters

Precise clock synchronization is the foundation of distributed systems. It is non-negotiable in financial exchanges, where regulatory requirements mandate sub-100µs external synchronization to Coordinated Universal Time, or UTC, and fairness demands sub-10ns internal clock synchronization. In high-frequency trading, a minuscule timing advantage can translate to significant financial gains, making accurate timestamping critical for market integrity. Beyond finance, numerous data center operations, including database consistency, distributed logging, virtual machine management, and network telemetry, rely on accurate temporal ordering of events. And as data centers scale, the need for a robust, scalable synchronization solution becomes even more important.

But achieving nanosecond-level synchronization in a dynamic data center environment is difficult. Several factors conspire to undermine precision:

Clock drift: Crystal oscillators, which are fundamental to all clocks, have inherent imperfections that cause them to gradually deviate over time. Although these deviations were considered minor previously, they are substantial when targeting sub-10ns.
Jitter: Network components such as switches and network interface cards (NICs) introduce unpredictable delays. These delays, often stemming from queuing in network buffers or the intricate processing of packets, can manifest as jitter, disrupting the timing of synchronization messages.
Asymmetry: The network path between two devices is rarely symmetrical. Differences in cable lengths, the number of hops, or the internal workings of network equipment can cause signals to take different amounts of time to travel in opposite directions. This asymmetry can introduce significant errors when estimating one-way delays and clock offsets.
Scalability: As data centers expand to house tens of thousands of servers, any synchronization solution must be able to scale efficiently without becoming a bottleneck or requiring disproportionate resources.
Fault tolerance: In a distributed system, failures are inevitable. A synchronization protocol must be resilient to the loss or misbehavior of individual nodes or network links, so that the overall synchronization accuracy is not compromised.

Firefly: Bridging software and theory

Firefly uses a multi-faceted strategy to tackle these challenges, distinguishing itself from prior synchronization protocols. Its core innovations lie in its architectural design and its theoretical underpinnings.

1. Layered synchronization: Firefly employs a novel layered synchronization technique. Instead of relying on a central clock, which can be a single point of failure or introduce delays, it first establishes tight internal synchronization amongst NICs within the data center. Each NIC in the network constantly communicates with a set of its peers, comparing times and making adjustments. From this "swarm" of devices emerges a highly stable and accurate consensus time that the entire group agrees upon. This internal synchronization is rapid and robust, effectively shielding it from external timing disturbances. Concurrently, Firefly synchronizes the entire swarm to UTC. Decoupling of these two processes is crucial, as it prevents external factors like time-server jitter or drift from directly impacting internal synchronization.

2. Distributed consensus over Random graphs: Unlike traditional hierarchical approaches that can be brittle and susceptible to single points of failure, Firefly uses a distributed consensus algorithm built on a d-regular random graph. This means each NIC communicates with a randomly selected set of 'd' peers. Theoretical analysis, as presented in the Firefly research paper, demonstrates that such random graphs offer significant advantages:

Faster convergence: Random graphs promote a more rapid dissemination of clock information across the network, leading to quicker synchronization.
Scalability: The theoretical bounds show that random graphs can maintain synchronization accuracy even as the size of the network grows, provided the number of peers ('d') scales logarithmically with the total number of nodes.
Resilience to asymmetry: The diverse probing paths inherent in random graphs help to average out and mitigate the impact of path asymmetries.

3. Mitigating jitter and asymmetry in practice: Beyond the theoretical advantages of random graphs, Firefly incorporates practical techniques to further refine accuracy:

RTT filtering: By analyzing round-trip time (RTT) measurements, Firefly can identify and discard probe samples that are likely affected by queuing jitter, thereby improving the accuracy of delay estimations.
Path profiling: Firefly actively probes network paths to identify and favor those with minimal asymmetry. This proactive approach helps to select the most reliable paths for synchronization.
Leveraging hardware: Where available, Firefly can utilize features like Transparent Clock (TC) in network switches to accurately account for in-switch delays, further reducing measurement error.

4. Robustness and fault tolerance: Firefly’s use of distributed consensus, combined with its averaging mechanisms, makes it inherently resilient to failures. By not relying on a single time server or a fixed hierarchical structure, the system can gracefully handle the loss or misbehavior of individual nodes.

Performance in the real world

The results discussed in our Firefly research paper are compelling:

Internal synchronization: Firefly consistently achieves sub-10ns NIC-to-NIC synchronization when used in conjunction with Google's latest data center fabric technology. This can be used to determine order of events like packets, logs, remote procedure calls (RPCs) across machines.
External synchronization: The system also delivers significantly better synchronization to UTC than the 100µs regulatory requirement for financial exchanges.

The offset between a pair of clocks that are six hops away in a Firefly-synced network, measured by an oscilloscope via 1 pulse per second.

The accompanying video illustrates the accuracy of NIC-to-NIC synchronization, as quantified by an oscilloscope utilizing a one-pulse-per-second (1PPS) signal from the NICs. Each row corresponds to a NIC clock, with the rising edge indicating the precise moment the NIC clock attains an integer second. The oscilloscope observations confirm that all measured NICs exhibit close synchronization, maintaining alignment within a few nanoseconds.

These results are particularly impressive given that Firefly operates purely in software on commodity hardware, avoiding the need for expensive, specialized synchronization equipment. This makes ultra-accurate time synchronization accessible to a broader range of data center applications.

A foundation for future applications

Firefly's success in delivering nanosecond-level accuracy in a scalable and cost-effective manner has far-reaching implications:

Democratizing high-precision timing: Firefly allows cloud-hosted financial services that traditionally rely on expensive dedicated hardware, to achieve the required precision using standard cloud infrastructure.
Enabling new applications: The availability of precise, synchronized clocks across data center devices can unlock new possibilities in areas like fine-grained network telemetry and congestion control, time-coordinated distributed systems, and deterministic fabric for ML workloads.
Transforming data center operations: By creating a tightly integrated and precisely timed computing entity, Firefly can enhance data centers’ overall efficiency, reliability, and performance.

In conclusion, Firefly represents a significant advancement in the field of clock synchronization. By ingeniously combining theoretical insights into graph theory and consensus algorithms with practical network engineering techniques, it overcomes the long-standing challenges of achieving nanosecond-level precision in complex, distributed environments. As data centers continue to evolve, systems like Firefly will be instrumental in building the high-performance, reliable, and fair infrastructure of the future.

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Announcing America-India Connect and new investments to advance global AI access

Wed, 18 Feb 2026 10:30:00 +0000

AI is poised to accelerate progress at an unprecedented rate — but only if we democratize access to it from the start. Today, Google is announcing new investments in digital infrastructure and skilling initiatives designed to increase connectivity, accelerate economic growth, and deepen resilience in India and around the world.

Expanding AI connectivity across four continents

Infrastructure is foundational to prevent the digital divide from becoming an AI divide. Today we’re announcing America-India Connect, a collaborative infrastructure initiative anchored by our five-year $15 billion AI infrastructure investment in India to increase the reach, reliability, and resilience of digital connectivity across four continents.

In collaboration with local partners, America-India Connect will establish a new international subsea gateway in Visakhapatnam (Vizag); three new subsea paths connecting India to Singapore, South Africa, and Australia; and four strategic fiber-optic routes that bolster network resilience and capacity between the United States, India, and multiple locations across the Southern Hemisphere.

Connecting India’s east coast (Vizag)

We are developing a direct fiber-optic path between Vizag and Chennai on India’s east coast to South Africa. When combined with the Equiano and Nuvem subsea cable systems, this will create a redundant high-capacity route that connects the American east coast around Africa to Vizag.

Additionally, we are delivering a direct path between Vizag and Singapore. When combined with the Bosun and Tabua subsea cable systems, this will create a South Pacific route that connects the American west coast through Australia to Vizag.

These investments will establish Vizag as a major international subsea gateway, adding vital diversity from existing landings in Mumbai and Chennai. For a nation of more than 1 billion people, this will increase the resilience of India’s digital backbone and improve economic security.

Connecting India’s west coast

We are constructing a direct fiber-optic path between Mumbai and Western Australia. In combination with the TalayLink and Honomoana subsea cable systems, this will create a South Pacific route connecting the American west coast around Australia to Mumbai.

This new path complements the Blue, Raman, and Sol subsea cables, which together form a data corridor from the American east coast through the Red Sea to Mumbai.

Closing the AI divide

Reliable infrastructure is the best counter to a digital divide. It is well documented that greater connectivity via subsea cables improves internet affordability and reliability, driving productivity and long-term economic growth. With greater access to digital services, individuals can develop new skills, and businesses and public sector organizations can better serve their communities — helping prevent the AI divide from taking root.

These routes do more than move bits and bytes — they honor a long history of global connection by turning maritime merchant shipping routes between the “New World” and India into digital trade routes between two countries, bringing their economies closer together.

America-India Connect builds on our work with dozens of regional partners to advance AI access in Africa, Australia, and the Pacific. We look forward to sharing more as we collaborate closely with these partners to strengthen global connectivity and bring the benefits of AI to all.

Realizing the benefits of AI transformation

While resilient infrastructure is the foundation for technology access, realizing the full benefits of AI also requires investing in the people who use it. That’s why we’re working in lockstep with governments to empower people with the digital skills they need to seize new opportunities.

Karmayogi Bharat (the Government of India’s digital mission) and Google Cloud are collaborating to accelerate Mission Karmayogi’s vision of building a future-ready civil service by leveraging AI-enabled digital capabilities across the nation. As the primary cloud partner, Google Cloud provides secure and resilient infrastructure for the iGOT (Integrated Government Online Training) Karmayogi platform. This scalable infrastructure supports over 20 million public servants across 800+ districts, providing reliable access to learning services at scale.

AI will also enhance the access, discoverability, and effectiveness of learning on the iGOT platform. By digitizing and structuring legacy training repositories into searchable knowledge assets and progressively enabling content in 18+ Indian languages, the partnership will help advance professional development and empower officials to learn in their preferred language.

We’re as committed as ever to partnering with communities, businesses, and governments to foster AI innovation for all, and we are excited to support the next generation of global connection.

Sawasdee Thailand! Google Cloud launches new region in Bangkok

Wed, 21 Jan 2026 05:00:00 +0000

ยินดีต้อนรับสู่ Google Cloud, Thailand! (Welcome to Google Cloud, Thailand!)

Thai businesses and public sector institutions share a common goal: to deliver high performance applications with low latency, while ensuring that valuable data remains securely within the country.

Today, Google Cloud is addressing that challenge by launching our new cloud region in Bangkok — a key milestone in our USD $1 billion investment in Thailand’s digital infrastructure. This launch serves as a powerful economic engine for the nation. Over the next five years, the Bangkok region is projected to contribute THB 1.4 trillion (US$41 billion) in economic value to Thailand and support an average of 130,000 jobs per year.

Our commitment extends beyond infrastructure; we are also investing deeply in Thailand’s digital ecosystem to foster talent development and support local communities. By combining world-class infrastructure with a focus on skill-building, this new region will empower organizations to accelerate their digital and AI transformations — opening doors for innovation and sustained growth within Thailand and across the globe.

As a transformation and innovation leader in Thailand, True Digital Group states:

“The launch of Google Cloud’s Thailand region marks a watershed moment for the nation’s digital economy and a significant milestone in accelerating Thailand’s adoption of data and AI at scale.

By bringing world-class cloud infrastructure and advanced AI capabilities directly to Bangkok, Google Cloud enables organizations to bridge the critical gap between global innovation and local data sovereignty — while strengthening trust, security, and compliance under PDPA standards.

For True Digital Group, this local region provides a strong foundation to securely unify and analyze large-scale datasets across our ecosystem, allowing us to translate data into meaningful, real-world impact. It supports our AI-First direction while enabling the delivery of more personalized, trusted, and accessible digital services for Thai consumers, alongside scalable digital solutions for organizations across industries.

Importantly, this development advances digital equity by lowering the barriers to adopting advanced cloud and AI technologies — empowering individuals, SMEs, enterprises, and public sector organizations to innovate with confidence. Through closer integration of connectivity, cloud, data, and AI, we aim to turn technology into practical value that improves everyday life and drives productivity across the economy.

Together with Google Cloud, we are excited to leverage this secure, low-latency foundation to co-innovate faster, build a resilient digital infrastructure, and strengthen Thailand’s long-term competitiveness on the global stage.” - Ekaraj Panjavinin, President, True Digital Group

High performance without compromising on data residency

This new Google Cloud region further enables businesses, organizations, and developers to provide exceptional experiences for customers in Thailand. With high-performance and low-latency cloud services in Thailand, we're enabling richer user experiences, and enhanced business agility for customers like KASIKORN Business-Technology Group (KBTG), the technology arm of Kasikorn Bank (KBank), one of Thailand’s leading commercial banks.

“As we drive KBTG toward becoming the region’s top technology organization, the launch of the Google Cloud Thailand region is a pivotal milestone for our strategy.

First, the reduced network latency and enhanced performance will ensure our users experience faster, more seamless digital banking. Second, a local cloud region simplifies our adherence to Bank of Thailand (BOT) regulatory standards, providing us with greater control over data residency and privacy. Finally, this move enables us to optimize our cloud architecture for cost efficiency while maintaining the highest security standards, ultimately empowering us to innovate faster for the Thai people.” - Voranuch Dejakaisaya, Executive Chairman - KASIKORN Business-Technology Group

This region provides a local infrastructure option, removing key barriers to cloud adoption for many Thai organizations. By providing the ability to store and process data domestically, we are helping businesses and public sector institutions align with regulations like the Personal Data Protection Act (PDPA). We see this as a pivotal moment, unlocking possibilities and empowering Thai ingenuity to flourish. From startups to established businesses and public sector organizations, the new region in Bangkok will provide the infrastructure and tools needed to thrive in the AI era.

The new region also strengthens the country's digital infrastructure capabilities, reinforcing our partnership in the Thai innovation ecosystem.

Translating modern infrastructure into business outcomes

For regulated industries like insurance and financial services, modernization requires trust, security, and reliability. The Bangkok region provides a modern and enterprise-grade foundation that enables:

High performance and low latency: Deliver millisecond-level latency to end users in Thailand, for ultra-fast application response times and integrated digital experiences with Google Cloud global infrastructure, spanning 43 regions and over 7.75 million kilometers of terrestrial and subsea fiber, including the TalayLink submarine cable that connects Australia and Thailand. These investments significantly increase the reach, reliability, and resilience of digital connectivity across Asia Pacific and around the world.
Strong data residency and security: The Bangkok region supports data residency, enabling customers to keep specified data within Thailand's borders. All data is encrypted at rest and in transit by default and customers can manage their own encryption keys with Cloud Key Management. The Bangkok (asia-southeast3) region launches with the following certifications:

ISO/IEC 27001, 27017, 27018
PCI DSS
SOC 1, 2, 3

Optimized TCO with a hybrid-by-design architecture: Run predictable and high-performance general-purpose workloads without compromising on cost. This allows customers to strategically access specialized, large-scale AI resources on our global network on an as-needed basis, avoiding the cost of maintaining specialized local hardware.

Global and domestic businesses alike are partnering with Google Cloud to enhance the customer experience.

“As a leader in the insurance industry, trust, security, and reliability are at the core of our digital strategy. The launch of the Google Cloud region in Thailand enables us to build a modern, cloud-native platform with enhanced security and faster time-to-market. This local cloud infrastructure strengthens our ability to deliver high-quality services, enhance customer experience, and accelerate sustainable growth, while fully complying with regulatory and data-residency requirements.” - Nithivadee Suksomboonwattana, Chief of TIP Group Tech, Dhipaya Group Holdings Public Company Limited

“As KTC accelerates its digital transformation to deliver a superior financial experience for Thai customers, we are pleased to welcome the launch of the Google Cloud region in Thailand. Leveraging local cloud infrastructure enables us to enhance application performance, reduce latency, and deliver faster, more seamless services to our customers. This milestone strengthens KTC’s ability to innovate with greater efficiency, security, and scalability — supporting our long-term commitment to sustainable digital growth.” – Wilaiwan Nopparat, Head of Information Technology, Krungthai Card Public Company Limited (KTC)

Gateway to AI innovation

With the new Bangkok region, your business gets the best of both worlds: secure, high-performance, and low-latency infrastructure to build and deploy your applications locally, and access to globally available AI products and models, such as Gemini.

The Bangkok region serves as a secure, high-speed on-ramp to access the Google Cloud AI ecosystem hosted across our global network. Businesses can leverage Vertex AI, our industry-leading development platform, to build and deploy agentic solutions using Gemini 3, alongside best-in-class generative media models like Imagen and Veo. Access Gemini Enterprise to empower employees and automate complex work by connecting AI to specific enterprise data sources and existing workflows. With access to Google Cloud AI you can use agents to securely transform how work gets done, using the same state-of-the-art technology that powers Google.

Our long-term commitment to Thailand's digital future

Google's commitment to Thailand extends beyond this new cloud region. We're making significant investments in the country's digital ecosystem to foster talent development and support local communities. Through programs like Google Skills and initiatives like our PanyaThAI customer success program, we’re helping to build a strong local talent pipeline and showcase the incredible innovation happening in the country.

Exclusive launch partnerships: We are thrilled to launch our new cloud region in collaboration with our key launch partners: Accenture, Beryl8, Deloitte, MFEC, and NTT Data. The deep engineering and consulting expertise of our partners helps customers quickly realize the benefits of the new region.
Local collaboration: We're working with Thai businesses, educational institutions, and government organizations to create a thriving cloud ecosystem. These collaborations focus on skills development, knowledge sharing, and supporting local innovation.

The launch of the new cloud region in Thailand is just the first step in our journey together. We're dedicated to ongoing investment in Thailand, continuing to expand the availability of Google AI services with in-country processing, partnering with local businesses and organizations to transform their industries, unlock new opportunities, and shape the world of tomorrow. We can't wait to see what you create. Look for Bangkok (asia-southeast3) in the Google Cloud console to get started today!

Nutanix NC2 is now officially supported on Google Cloud

Tue, 09 Dec 2025 14:00:00 +0000

Today, we are thrilled to announce Nutanix Cloud Clusters (NC2) is generally available on Google Cloud.

NC2 on Google Cloud is designed to migrate and modernize specialized, regulated, and mission-critical applications without refactoring your workloads or compromising on performance. This partnership brings the power of Google Cloud’s infrastructure and advanced AI models to your hybrid cloud, without compromising on data residency, connectivity, or operational consistency. You can now run your Nutanix Hybrid Cloud directly on Google Compute Engine.

"The General Availability of Nutanix Cloud Clusters (NC2) on Google Cloud is a significant milestone empowering our joint customers to become AI-ready. We are excited to extend the simplicity and resilience of Nutanix NC2 onto Google Cloud's high-performance workload-optimized compute. Nutanix on Google Cloud enables our customers to migrate and modernize their critical workloads while unlocking the full power of Google’s industry-leading data and AI capabilities." - Saveen Pakala, VP, Product Management, Hybrid Cloud, Nutanix

Nutanix and Google Cloud allow you to maximize agility and minimize disruption for your critical applications. By combining NC2's enterprise flexibility with Google Cloud's power, you gain access to three core advantages. First, your workloads run on Compute Engine’s dynamically scalable workload-optimized infrastructure powering all machine families. Nutanix NC2 supports Compute Engine bare metal instances in the Z3 and C4 families. These are powered by the Titanium offload system and leverage Titanium SSDs for low-latency, high-throughput storage performance, hosted in Google Cloud with global reach, enterprise-grade security, and commitment to sustainability. Second, you accelerate AI innovation by co-locating data and machine learning services like Gemini Enterprise and Vertex AI. Finally, you can save costs by dynamically scaling capacity and utilizing committed use discounts (CUDs) and Flex CUDs.

Key use cases to accelerate your cloud journey

The integration of NC2 on Google Cloud offers flexible, strategic options for hybrid cloud operations. Beyond consolidation and cost control, these capabilities set the stage for true modernization:

Seamless workload migration: Move entire applications between your on-premises Nutanix environment and Google Cloud without re-factoring or re-architecting. This capability saves significant time during data center consolidation.
Consistent operations: Maintain the same management plane, security policies, and automation across your private data center and Google Cloud, which dramatically reduces operational complexity and training costs.
Disaster recovery (DR): Leverage Google Cloud as a robust and cost-efficient recovery target. Usage of a minimal “pilot light” cluster reduces compute costs, so you scale up only when a disaster event occurs.
Capacity bursting: Instantly add capacity in the cloud to handle seasonal demands, VDI workloads, development/test cycles, or requirements from mergers and acquisitions (M&A).
License portability: Protect your software investments by easily moving your existing Nutanix software licenses to Google Cloud as your business needs evolve.

“Like many others, we are always on a journey to modernize and shift to achieve the best outcomes for our customers. Nutanix Cloud Clusters (NC2) on Google Cloud brings us a solid platform to continue our hybrid cloud expansion. Our ability to seamlessly run workloads on-premises and on NC2 on Google Cloud without having to re-factor is increasingly valuable as we continue our modernization journey. We look forward to continuing our strong partnership with Google Cloud and Nutanix.” - VP of IT at a global oil & gas company based in Oklahoma

The architecture

NC2 on Compute Engine simplifies building a hybrid cloud by deploying the Nutanix Cloud Infrastructure (NCI) software stack, including the Acropolis Hypervisor (AHV), directly onto high-performance Compute Engine infrastructure.

The key components of the solution include:

Compute Engine instances: NC2 runs on Google Compute Engine bare metal instances in the recently introduced C4 and Z3 machine families. These powerful instances provide the foundation with high-density compute, memory, local NVMe storage, and high network bandwidth.

Machine Family	GCE Machine Type	vCPUs	Memory	Storage	Processor
Z3, Storage Optimized	z3-highmem-192-highlssd-metal	192	1536GB	72TB of NVMe Local SSD	Intel, Sapphire Rapid
C4, General Purpose	c4-highmem-288-lssd-metal	288	1080GB	18TB of NVMe Local SSD	Intel, Granite Rapid
C4, General Purpose	c4-standard-288-lssd-metal	288	2232GB	18TB of NVMe Local SSD	Intel, Granite Rapid

Simplified networking : NC2 runs entirely within your existing Google Cloud Virtual Private Cloud (VPC). Built-in Nutanix Flow Virtual Networking for overlay is integrated to reduce hybrid cloud complexity.
Unified management: The entire environment, both on-premises and in Google Cloud, is managed through the familiar Prism Central console, simplifying day-to-day operations and skill requirements for your IT teams.
Easy procurement: Later this month, you’ll be able to purchase Nutanix NC2 licensing directly from Google Cloud Marketplace . This offers a single, unified billing experience for both your Google Cloud infrastructure and Nutanix NC2, in one simple process. A key benefit is the ability to use your existing Google Cloud spend commitments for Nutanix NC2 software. This helps you maximize your investment and streamline your financial operations, providing more value from your cloud budget.

Connect your data to Google Cloud AI and analytics

A significant modernization opportunity comes from connecting your stable, trusted Nutanix workloads with Google Cloud's powerful data and AI tools. Your applications running on NC2 can tap directly into services like BigQuery and Vertex AI with low latency, enabling you to:

Derive deeper business value: Easily send application log data, transactional records, and other operational data from your Nutanix VMs to BigQuery for real-time, scalable data warehousing and complex analysis.
Build custom machine learning models: Use Vertex AI to create, deploy, and manage custom ML models that analyze data generated by your core applications (e.g., predictive maintenance or fraud detection).
Use conversational AI: Quickly build and deploy conversational agents using technologies like Dialogflow that interact directly with the application data residing on your NC2 cluster.

Ready to simplify your cloud operations?

NC2 on Google Cloud is currently available across 17 Google Cloud regions, with a planned expansion continuing through 2026. For precise details on regional and zonal availability, please check the official Google Compute Engine bare metal regional availability documentation, and reference the Compute Engine pricing page for infrastructure costs. To learn more about the solution, try taking a test drive or visit Nutanix partner page. Available later this month, you will be able to explore NC2 on Google Cloud licensing through the Google Cloud Marketplace.

Introducing the TalayLink subsea cable and new connectivity hubs

Sun, 23 Nov 2025 18:00:00 +0000

Today we’re announcing TalayLink, a new subsea cable connecting Australia and Thailand to significantly increase the reach, reliability, and resilience of digital connectivity across Asia Pacific and around the world.

Named after the Thai word for sea or ocean, “talay,” TalayLink will extend the interlink cable that we announced as part of the Australia Connect initiative last year. The new subsea cable will establish a new, diverse path to Thailand via the Indian Ocean, west of the Sunda Strait, where many existing subsea cables currently route. This strategic routing will further integrate our future data centers and cloud region in Thailand into our global network.

In addition to the TalayLink subsea cable system, we’re announcing plans for new connectivity hubs in Western Australia (Mandurah) and South Thailand. These strategic investments are designed to future-proof regional connectivity and accelerate the delivery of advanced digital and AI services through cable switching, content caching, and colocation capabilities.

The Mandurah connectivity hub will establish a diverse landing point from Perth, where the majority of existing subsea cables currently land in Western Australia. In South Thailand, an established crossroads for subsea cables, we are partnering with colocation provider AIS to accelerate our deployment and benefit from existing local infrastructure investments.

“The TalayLink cable will serve as a pivotal piece of digital infrastructure, enhancing Thailand’s connectivity and resilience. Together with Google’s upcoming Google Cloud region and data center in Thailand, these forward‑looking investments will significantly expand regional network and computing capacity, while firmly positioning Thailand as a critical digital gateway for next‑generation cloud and AI innovation in Southeast Asia.

“The Thailand Board of Investment (BOI) is fully committed to supporting Google’s investment in Thailand, fostering the growth of the nation’s digital economy, and advancing digital skills to ensure inclusive and sustainable development.” - Narit Therdsteerasukdi, Secretary General, Thailand Board of Investment (BOI)

“AIS is excited to be extending our relationship with Google as a strategic partner by supporting the connectivity hub in Southern Thailand. The combination of Google’s new, diverse submarine cable path and AIS’s high-reliability colocation capabilities will ensure the digital infrastructure in the region is capable of supporting the country’s AI strategy.” - Pratthana Leelapanang Chief Executive Officer, AIS

“International Gateway Company (IGC), a subsidiary of ALT Telecom PLC, is delighted to be a key Google partner in landing a new submarine cable in Thailand. IGC brings to the project its extensive experience operating a nationwide network and international cable gateway. This cable is an important new piece of digital infrastructure that will accelerate Thailand's ambitious digital economy development strategy." - Preeyaporn Tangpaosak, President, ALT Telecom

When they’re complete, TalayLink and the connectivity hubs will support network resilience across Australia, Africa and Southeast Asia. When combined with our previously announced connectivity hubs in the Maldives and Christmas Island, these investments will provide onward connectivity across the Indian Ocean and beyond to the Middle East.

This new cable and our regional connectivity hubs will directly support Western Australia’s roadmap to secure a safe, inclusive digital future, as well as the Royal Thai Government’s objective of economic transformation through AI and digital inclusion. We are excited to contribute to the economic and social growth across Australia, Thailand and Southeast Asia through resilient, reliable internet infrastructure.

A new Google Cloud region is coming to Türkiye as part of $2 billion investment

Thu, 20 Nov 2025 08:30:00 +0000

We are excited to announce plans to bring a new Google Cloud region to Türkiye, as part of Google’s 10-year, $2 billion investment in the country.

The establishment of this world-class digital infrastructure, in collaboration with Turkcell, marks a significant multi-year investment to accelerate digital transformation in Türkiye and cloud innovation across the region.

"The partnership between Google Cloud and Turkcell will further accelerate Türkiye’s digital transformation journey. It reflects the confidence of global technology leaders in the strength, resilience, and innovation capacity of our economy. By integrating advanced data infrastructure and next-generation cloud technologies into our digital ecosystem, this alliance will enhance efficiency and foster innovation across public and private sectors. Furthermore, it supports our long-term vision of strengthening digital sovereignty and positioning Türkiye as a regional hub for technology, connectivity, and sustainable growth.” Cevdet Yılmaz, Vice President of the Republic of Türkiye

"Our partnership with Google Cloud clearly reinforces Turkcell’s leadership in driving Türkiye’s digital transformation. This strategic partnership is more than a technology investment — it is a milestone for Türkiye’s digital future, accelerating our national vision by leveraging Google Cloud’s global technologies and unlocking opportunities for AI innovations. This collaboration gives our customers seamless access to Google Cloud’s cutting-edge capabilities. This new Google Cloud region will enable enterprises to innovate faster and compete globally. As part of this partnership Turkcell plans to invest $1 billion in data centers and cloud technologies.” - Dr. Ali Taha Koç, CEO, Turkcell

When it is open, the Türkiye region will help meet growing customer demand for cloud services and AI-driven innovation in the country and across EMEA, delivering high-performance services that make it easier for organizations to serve their end users faster, securely, and reliably. Local customers and partners will benefit from key controls that enable them to maintain low latency and the highest international security and data protection standards.

"Cloud technologies are a critical enabler of the financial sector’s ongoing digital transformation. With Google Cloud’s new region in Türkiye, Garanti BBVA will be able to strengthen its operational resilience while continuing to innovate by securely deploying artificial intelligence and advanced data analytics. This collaboration reinforces our commitment to delivering reliable, high-performance digital services to our customers, while ensuring that data sovereignty, privacy, and trust remain at the core of everything we do." — İlker Kuruöz, Garanti BBVA

"As a global airline connecting Türkiye to the world, Turkish Airlines relies on high-performance, resilient technology to deliver an uninterrupted customer journey, 24/7. Google Cloud’s plan to launch a local region in Türkiye, combined with its global network, is a game-changer for our flight operations, passenger systems, and data-intensive applications. Having hyperscale cloud infrastructure closer to home ensures the low latency required to adopt advanced analytics, robust cybersecurity solutions, and future AI capabilities, accelerating our digital strategy and reinforcing our commitment to service excellence." — Kerem Kızıltunç, Turkish Airlines

“Yapı Kredi is focused on continuous innovation and modernizing our core banking infrastructure to deliver a limitless banking experience to our customers. The planned Google Cloud region in Türkiye provides the robust, scalable, and secure infrastructure of a hyperscale cloud, which is necessary to power our advanced artificial intelligence and cybersecurity initiatives. This local presence will significantly enhance the performance and flexibility needed to support our growth and empower us to build the next generation of secure, digital-first financial products." — Gökhan Özdinç, Yapı Kredi Bank

With 42 regions and 127 zones currently in operation around the world, Google Cloud’s global network of cloud regions forms the foundation to support customers of all sizes and across industries. From retail and media and entertainment to financial services, healthcare and the public sector, leading organizations come to Google Cloud as their trusted technology partner.

Key features of the Google Cloud region in Türkiye will include:

Advanced capabilities and technologies: The region will deliver leading Google Cloud services across data analytics, cybersecurity and digital business solutions. Google’s cutting-edge AI innovations will strengthen Türkiye’s digital ecosystem and enable enterprises and public sector entities to operate with greater efficiency, speed and security.
Uncompromising data sovereignty and security: The new region in Türkiye will benefit from our robust infrastructure, including data encryption at rest and in transit, granular data access controls, data residency, and sophisticated threat detection systems. We adhere to the highest international security and data protection standards to help ensure the confidentiality, integrity, and sovereignty of your data.
High performance and low latency: Serves end users across Türkiye and neighboring countries with fast, low latency experiences, and transfers large amounts of data between networks easily across Google’s global network.
Scalability and flexibility on demand: Google Cloud’s infrastructure is designed to scale easily with any business. Whether you’re a small startup or a large corporation, you can easily adjust your resources to meet your evolving needs.

Learn more about our global cloud infrastructure, including new and upcoming regions.

Introducing Dhivaru and two new connectivity hubs

Mon, 17 Nov 2025 15:00:00 +0000

Today, we’re announcing Dhivaru, a new Trans-Indian Ocean subsea cable system that will connect the Maldives, Christmas Island and Oman. This investment will build on the Australia Connect initiative, furthering the reach, reliability, and resilience of digital connectivity across the Indian Ocean.

Reach, reliability and resilience are integral to the success of AI-driven services for our users and customers. Tremendous adoption of groundbreaking services such as Gemini 2.5 Flash Image (aka Nano Banana) and Vertex AI, mean resilient connectivity has never been more important for our users. The speed of AI adoption is also outpacing anyone’s predictions, and Google is investing to meet this long-term demand.

“Dhivaru” is the line that controls the main sail on traditional Maldivian sailing vessels, and signifies the skill, strength, and experience of the early sailors navigating the seas.

In addition to the cable investment, Google will be investing in creating two new connectivity hubs for the region. The Maldives and Christmas Island are naturally positioned for connectivity hubs to help improve digital connectivity for the region, including Africa, the Middle East, South Asia and Oceania.

“Google’s decision to invest in the Maldives is a strong signal of confidence in our country’s stable and open investment environment, and a direct contribution to my vision for a diversified, inclusive, and digitized Maldivian economy. As the world moves rapidly toward an era defined by digital transformation and artificial intelligence, this project reflects how the Maldives is positioning itself at the crossroads of global connectivity — leveraging our strategic geography to create new economic opportunities for our people and to participate meaningfully in the future of the global economy.” - His Excellency the President of Republic of Maldives, Dr. Mohamed Muizzu

“We are delighted to partner with Google on this landmark initiative to establish a new connectivity hub in the Maldives. This project represents a major step forward in strengthening the nation’s digital infrastructure and enabling the next wave of digital transformation. As a leading digital provider, Ooredoo Maldives continues to expand world-class connectivity and digital services nationwide. This progress opens new opportunities for businesses such as tourism, enabling smarter operations, improved customer experiences and greater global reach. We are proud to be powering the next phase of the Digital Maldives." - Ooredoo Maldives CEO and MD, Khalid Al Hamadi

"Dhiraagu is committed to advancing the digital connectivity of the Maldives and empowering our people, communities, and businesses. Over the years, we have made significant investments in building robust subsea cable systems — transforming the digital landscape — connecting the Maldives to the rest of the world and enabling the rollout of high-speed broadband across the nation. We are proud and excited to partner with Google on their expansion of subsea infrastructure and the establishment of a new connectivity hub in Addu City, the southernmost city of the Maldives. This strategic collaboration with one of the world’s largest tech players marks another milestone in strengthening the nation’s presence within the global subsea infrastructure, and further enhances the reliability and resiliency of our digital ecosystem." - Ismail Rasheed, CEO & MD, DHIRAAGU

Connectivity hubs for the Indian Ocean region

Connectivity hubs are strategic investments designed to future-proof regional connectivity and accelerate the delivery of next-generation services through three core capabilities: Cable switching, content caching, and colocation.

Cable switching: Delivering seamless resilience

Google carefully selects the locations for our connectivity hubs to minimize the distance data has to travel before it has a chance to ‘switch paths’.. This capability improves resilience, and ensures robust, high-availability connectivity across the region. The hubs also allow automatic re-routing of traffic between multiple cables. If one cable experiences a fault, traffic will automatically select the next best path and continue on its way. This ensures high availability not only for the host country, but minimizes downtime for services and users across the region.

Content caching: Accelerating digital services

Low latency is critical for optimal user experience. One of Google’s objectives is to serve content from as close to our users and customers as possible. By caching — storing copies of the most popular content locally — Google can reduce the latency to retrieve or view this content, improving the quality of services.

Colocation: Fostering a local ecosystem

Connectivity hubs are often in locations where users have limited access to high quality data centers to house their services and IT hardware, such as islands. Although these facilities are not very large as compared to a Google data center, Google understands the benefits of shared infrastructure, and is committed to providing rack space to carriers and local companies.

Energy efficiency

Subsea cables are very energy efficient. As a result, even when supporting multiple cables, content storage and colocation, a Google connectivity hub requires far less power than a typical data center. They are primarily focused on networking and localized storage and not the large demands supporting AI, cloud and other important building blocks of the Internet. Of course, the power required for a connectivity hub can still be a lot for some smaller locations, and where it is, Google is exploring using its power demand to accelerate local investment in sustainable energy generation, consistent with its long history of stimulating renewable energy solutions.

These new connectivity hubs in the Maldives and Christmas Island are ideally situated to deepen the resilience of internet infrastructure in the Indian Ocean Region. The facilities will help power our products, strengthen local economies and bring AI benefits to people and businesses around the world. We look forward to announcing future subsea cables and connectivity hubs and further enhancing the Internet’s reach, reliability, and resilience.

Agile AI architectures: A fungible data center for the intelligent era

Mon, 13 Oct 2025 23:00:00 +0000

It’s not hyperbole to say that AI is transforming all aspects of our lives: human health, software engineering, education, productivity, creativity, entertainment… Consider just a few of the developments from Google this past year: Magic Cue on the Pixel 10 for more personal, proactive, and contextually-relevant assistance; our viral Nano Banana Gemini 2.5 Flash image generation; Code Assist for developer productivity; and AlphaFold, which won its creators the Nobel prize for chemistry. We like to joke that the past year in AI has been an amazing decade!

Underpinning all these advances in AI are equally amazing advances in the computing infrastructure powering AI. If AI researchers are like space explorers discovering new worlds, then systems and infrastructure designers are the ones building the rockets. But keeping up with the demands of AI services will require even more from us. At Google I/O earlier this year, we announced nearly 50X annual growth in the monthly tokens processed by Gemini models, hitting 480 trillion tokens per month. Since then we have seen an additional 2X growth, hitting nearly a quadrillion monthly tokens. Other statistics paint a similar picture: AI accelerator consumption has grown by 15X in the last 24 months; our Hyperdisk ML data has grown 37X since GA; and we’re seeing more than 5 billion AI-powered retail search queries per month.

With great AI comes great computing

This kind of growth brings with it new challenges. When planning for data centers and systems, we are accustomed to long lead times, paralleling the long time to build out hardware. However, AI demand projections are now changing dynamically and dramatically, creating a significant divergence in supply and demand. This mismatch requires new architectures and system design approaches that can respond to extreme volatility and growth.

Rapid technology innovations are essential, but must be carefully managed across the stack. For example, each generation of AI hardware (like TPUs and GPUs) has introduced new features, functionality, but also power, rack, networking and cooling requirements. The rate of introduction of these new generations is also on the rise, making it hard to build a coherent end-to-end system that can accommodate such a vast rate of change. Further, changes in form factors, board densities, networking topologies, power architectures, liquid cooling solutions, etc., all incrementally compound heterogeneity, so that when taken together, there is a combinatorial increase in the complexity of designing, deploying, and maintaining systems and data centers. In addition, we need to design for a spectrum of data center facilities — beyond traditional hyperscalar- or cloud-optimized offerings to “neoclouds” and industry-standard colocation providers – across multiple geographical regions. This adds yet another layer of diversity and dynamism, further constraining data center design for the new AI era.

We can address these two challenges — dealing with dynamic growth and compounding heterogeneity — if we design data centers with fungibility and agility as first-class considerations. Architectures need to be modular, where components can be designed and deployed independently. They should be interoperable across different vendors or generations. Equally important, they should support the ability to late-bind the facility and systems to handle dynamically changing requirements (for example, reuse infrastructure designed for one generation to the next ). Data centers should also be built on agreed-upon standard interfaces, so data center investments can be reused across multiple customer segments. And finally, these principles need to be applied holistically across all components of the data center – power delivery, cooling, server hall design, compute, storage, and networking.

With great computing comes great power (and cooling and systems)

To achieve agility and fungibility in power, we must standardize power delivery and management to build a resilient end-to-end power ecosystem, including common interfaces at the rack power level. Partnering with other members of the Open Compute Project (OCP), we introduced new technologies around +/-400Vdc designs and an approach for transitioning from monolithic to disaggregated solutions using side-car power, a.k.a. Mt. Diablo. Promising new technologies, like low-voltage DC power combined with solid state transformers, will enable these systems to transition to future fully integrated data center solutions.

We are also evaluating solutions for data centers to become suppliers to the grid, not just consumers from it, with corresponding standardization around battery-operated storage and microgrids. We already used such solutions to manage the challenges around the “spikiness” of AI training workloads and are also applying them for additional savings around power efficiency and grid power usage.

Data center cooling, meanwhile, is also being reimagined for the AI era. Earlier this year, we announced Project Deschutes, a state-of-the-art liquid cooling solution that we contributed to the Open Compute community, and have since published the specification and design collateral. The community is responding enthusiastically, with liquid cooling suppliers like Boyd, CoolerMaster, Delta, Envicool, Nidec, nVent, and Vertiv showcasing demos at major events this year, including the OCP Global Summit and SuperComputing 2025. But we have more opportunities to collaborate on: industry-standard cooling interfaces, new components like rear-door-heat exchangers, reliability, etc. One particularly important area is standardizing layouts and fit-out scopes across colos and third-party data centers, so we as an industry can enable more fungibility.

Finally, we need to bring together compute, networking, and storage in the server hall, including physical attributes of the data center design such as rack height, width, and depth (and more recently, weight); aisle widths and layouts; as well as rack and network interfaces. We also need standards for telemetry and mechatronics to build and maintain these future data centers. With our fellow OCP partners, we are standardizing telemetry integration for third-party data centers, including establishing best practices, developing common naming and implementations, and creating standard security protocols.

Beyond physical infrastructure, we are collaborating with our partners to deliver open standards for more scalable and secure systems. A few highlights include:

Resilience: We’ve expanded our multi-year effort on manageability, reliability and serviceability from GPUs to include CPU firmware updates and debuggability.
Security: Caliptra 2.0, the open-source hardware root of trust, now defends against future threats with post-quantum cryptography, while OCP S.A.F.E. makes security audits routine and cost-effective.
Storage: OCP L.O.C.K. builds on Caliptra’s foundation to provide a robust, open-source key management solution for any storage device.
Networking: Congestion Signaling (CSIG) has been standardized and is delivering measured improvements in load balancing. Alongside continued advancements in SONiC, a new effort is underway to standardize Optical Circuit Switching.

Sustainability is embedded in our work. To provide insight into the environmental impact of AI, we developed a new methodology for measuring the energy, emissions, and water impact of emerging AI workloads, demonstrating that the median Gemini Apps text prompt consumes less than five drops of water and has the energy impact of watching TV for under nine seconds. We apply this type of data-driven approach to other collaborations across the OCP community: on an embodied carbon disclosure specification, green concrete, clean backup power, and reduced manufacturing emissions.

A call to action: community-driven innovation and AI-for-AI

Google has a long history of collaboration with open ecosystems that have demonstrated the compounding power of community collaborations, and we have the opportunity to repeat as we design agile and fungible data centers for the AI era. Join us in the new OCP Open Data Center for AI Strategic Initiative on common standards and optimizations for agile and fungible data centers.

As we look ahead to the next waves of growth in AI, and the amazing advances they will unlock, we will need to leverage these AI advances in our own work, to amplify our productivity and innovation. An early example is Deepmind AlphaChip, which uses AI to accelerate and optimize chip design. We are seeing more promising uses of AI for systems: across hardware, firmware, software, and testing; for performance, agility, reliability, and sustainability; and across design, deployment, maintenance, and security. These AI-enhanced optimizations and workflows are what will bring the next order-of-magnitude improvements to the data center. We look forward to the innovations ahead, and to your continued collaboration in driving them forward.

The future of media sanitization at Google

Mon, 13 Oct 2025 16:00:00 +0000

At Google, protecting your data is our most important responsibility, and we are committed to keeping your data safe. To further this commitment, we are proud to announce that starting in November 2025, we will start transitioning our approach to media sanitization to fully rely on a robust and layered encryption strategy.

This marks a move away from the "brute force disk erase" process we have used for nearly two decades. While overwriting data has been an effective method, the storage technology landscape has changed dramatically. This process is no longer sustainable due to the size and technological complexity of today's modern media.

A smarter approach: Cryptographic erasure

To address these challenges, we are embracing a more modern and efficient method of media sanitization: cryptographic erasure.

By default, all user data in Google's services is protected by multiple layers of encryption. Cryptographic erasure leverages this encryption to sanitize media. Instead of overwriting the entire drive, we securely delete the cryptographic keys that are used to encrypt the data. Once the keys are gone, the data is rendered unreadable and unrecoverable.

This method is not only faster but also aligns with industry best practices. The National Institute of Standards and Technology (NIST) recognizes cryptographic erasure as a valid sanitization technique in its special publication 800-88. We are committed to meeting and exceeding these standards to ensure the security of your data.

Enhancing security through innovation

We implement cryptographic erasure with multiple layers of security, employing a defense in depth strategy. Our trust-but-verify model uses independent verification mechanisms to ensure permanent deletion of media encryption keys.

We also protect secrets involved in this process, like storage device keys, with industry-leading measures. Multiple key rotations enhance the security of customer data through independent layers of trusted encryption.

Sustainability and the circular economy

Our previous method of media erasure had an environmental cost. Any storage device that failed our rigorous verification process was physically destroyed. This resulted in the destruction of a significant number of devices each year.

Cryptographic erasure allows us to move towards a more sustainable, circular economy. By eliminating the need to physically destroy drives, we can reuse more of our hardware. This also allows us to recover valuable rare earth materials, such as neodymium magnets, from end-of-life media. This innovative magnet recovery process is a major accomplishment in sustainable manufacturing, showcasing our commitment to responsible growth.

Our path forward

We have consistently been strong advocates for doing what is truly right for our users, the broader industry, and the world at large. This transition to cryptographic erasure is a direct reflection of that commitment. It allows us to enhance security, align with the highest industry standards, and build a more sustainable future for our infrastructure. We believe this is the right path forward for our users, the industry, and the environment.

For more information about encryption at rest, including encryption key management, see our default encryption at rest security whitepaper.

How Google Cloud’s AI tech stack powers today’s startups

Thu, 18 Sep 2025 13:00:00 +0000

AI has accelerated startup innovation more than any technology since perhaps the internet itself, and we’ve been fortunate to have a front row seat to much of this innovation here at Google Cloud. Nine of the top ten AI labs use Google Cloud, as does nearly every AI unicorn and more than 60% of the world’s gen AI startups overall.

And the trend of AI startups choosing us is accelerating. For example, we’ve seen more than a 20% increase over the past year in the number of newer AI Startups (those who recently raised a Series A or B round) choosing to use Google Cloud.

These startups are building with our differentiated AI stack, which offers leading technology and choice at every layer, including a variety of TPU and GPU specialized chips for compute, and a broad range of models that excel at coding, image and video generation, collaboration, and more.

Today, we’re hosting our first-ever Google's AI Builders Forum, bringing hundreds of startup founders, and builders together in the heart of Silicon Valley to hear about how Google’s AI, infrastructure, and services, as well as our unique resources to support startups’ growth, can help them build.

To further that support, and help startups jumpstart work in the exciting realm of AI agents, we’ve just published our Startup technical guide: AI agents. It’s an in-depth collection of best practices and step-by-step guidance for building and scaling agentic AI, so you can get from idea to production faster. It also covers core agentic concepts like RAG and multimodality, and how to leverage Google Cloud’s agentic offerings, like ADK and A2A.

We’re also excited to announce momentum with dozens of startup customers who are building platforms, products, and entire businesses on Google Cloud, including:

Afooga, an AI-powered content experimentation factory, enables businesses to generate, test, and distribute content at massive scale from a single hypothesis, automatically optimizing across TikTok, Meta, YouTube, and more. Afooga now leverages Vertex AI and Veo for generative video capabilities.
Anara, a generative AI research assistant, helps users find and understand scientific documents with verifiable AI summaries and insights. It now uses Google Cloud's scalable infrastructure, AI Studio, and Cloud Functions to power its models and data processing for a global user base.
Aptori, an AI security company, detects vulnerabilities in AI-generated code, prioritizes risks, and automates code fixes in real-time. Now, Aptori uses Gemini to analyze code for security weaknesses and generate context-aware fixes, integrating its AI Security Engineer directly into developer workflows.
aSim, an AI-powered mobile app development tool, allows people to quickly generate, share, and discover mini-apps. Users can now instantly generate an app from a prompt using Gemini models and multimodal capabilities from models like Gemini 2.5 Flash Image (i.e. Nano Banana) and Veo 3.
CerebraAI develops AI software for analyzing non-contrast CT scans (NCCT), with a focus on early stroke and cancer detection. It fine-tunes MedGemma on NCCT images and leverages Gemini's few-shot generalization capabilities to rapidly adapt its model for various diagnostic tasks.
Clavata.ai delivers an integrated AI governance and safety platform with multi-modal, real-time evaluations powered by Gemini models. Its suite of tools enable proactive policy enforcement, dynamic debugging, iteration, observability, and problem prevention.
Clip Media built Reclip, a social media application that leverages Google's generative media models like Veo and Imagen to create short, engaging animated videos from real-time audio clips, captured by their proprietary app.
CoVet is an AI assistant built for veterinary professionals, that uses Gemini, Cloud Functions, and other Google Cloud solutions to help veterinary teams automate administrative work, save hours every day, and refocus on what matters most: exceptional patient care.
ColomboAI has built its next-generation social engagement super-app on Google Cloud, integrating search, social, commerce, and news into one experience. The company is now launching CAIRO, its in-app AI Operator agent that can automate actions across modules — from GenAI to feeds, shop, and beyond — powered by Google Cloud AI infrastructure.
Corma is using Google Cloud’s AI infrastructure to train novel, domain-specific foundation models.
Factory, a platform for agent-driven software development, accelerates engineering by unifying context from sources like GitHub and Jira to delegate tasks such as feature development and migrations. It uses Gemini 2.5 Flash for data ingestion and Gemini 2.5 Pro for advanced code and document generation.
Gobii provides AI Agents that automate complex web tasks like forms and workflows directly in the browser. To power these intelligent agents, Gobii is building on Vertex AI and scalable GKE infrastructure.
InstaLILY is using Gemini 2.5 and Vertex AI to power AI agents, called InstaWorkers, that can support sales, service, and operations.
Inworld AI, an AI platform for builders of consumer applications, uses Google Cloud and Gemini to cost-effectively handle tens of millions of concurrent users with response times measured in milliseconds, meeting strict requirements for quality, cost control, and security.
Krea.ai, a creative suite of AI tools, offers real-time image and video generation and personalized model training for artists and marketers. It integrates with Google Cloud to provide access to advanced models like Veo3 and Gemini 2.5 Flash Image (Nano Banana), enabling users to create high-quality ads, product photos, and game assets.
Lovable, which is building an AI software engineer, uses Vertex AI to deploy and unify its core language models, including Gemini and Anthropic's models. This unique orchestration enables users to create complete, full-stack web applications from plain English descriptions while choosing the model on Google Cloud that best suits their needs.
MaestroQA, a conversation analytics data platform, is now using Gemini to enhance its AI-powered conversation analytics. Gemini helps MaestroQA improve its ability to analyze customer interactions across every channel, providing deeper insights that help businesses boost customer satisfaction and drive growth and retention.
Markups.ai created Agent Marko, an AI contract negotiation agent, which helps turn days-long human legal review into an easier automated process. Users simply email contracts to a designated inbox and receive customized revisions and analysis back within minutes. Gemini 2.5 Pro has enabled Markups.ai to go from handling only first revisions of NDAs to effectively any revision of any contract.
MLtwist, an AI data pipeline services company, processes, transcribes, translates, and labels large, complex data streams for enterprise applications. It now uses Gemini models and AI Studio for transcription and labeling tasks.
MNTN uses Google Cloud to power its connected-TV ad platform which helps users better measure the impact of their TV campaigns. MNTN uses Google’s Security Command Center to ensure users have a secure experience on its platform.
Mosaic lets users build and run multimodal video-editing AI agents. It uses Gemini 2.5 Pro, Cloud Storage, and Cloud Run to power its platform.
OpenArt, an AI video production company, empowers social media creators and SMBs to turn ideas into videos complete with motion, music, and a narrative arc with Gemini models and Veo3.
OpenEvidence is building its AI-powered medical search platform on Google Cloud, enabling healthcare professionals to access medical information more quickly, safely, and accurately.
Owl.AI, Owl.AI, a sports technology company, delivers AI-powered solutions to professional sports leagues. Their offerings, which include judging and scoring, aim to enhance accuracy, consistency, and eliminate bias. Owl.AI achieves this by leveraging AI models built on Gemini and fine-tuned on Google Cloud to analyze real-time video footage of athletic performances.
Parallel uses Gemini models via Vertex AI to orchestrate “micro agents” that can manage AI search requests more effectively.
Prodia, a generative AI API, offers developers APIs to integrate generative AI into their creative tools. Prodia relies on GPUs on Google Cloud to serve text-to-image and instruct-to-edit models, our low-latency network to ensure scalable, cost-effective performance, Dynamic Workload Scheduler for burst capacity, and models like Veo 3 and Gemini 2.5 Flash Image to power advanced video and image generation.
Provenbase has built its talent recruitment tool for businesses on Google Cloud and is now powering its transformative deep search for talent feature using Google Cloud AI.
Qualia Clear is an agentic system that improves real estate closings by automating manual title and escrow workflows. It uses tool calling, Gemini 2.5 Flash and Gemini 2.5 Pro, and Google Agent Development Kit to process emails and documents and simplify reporting, improving efficiency and customer service.
Rembrand is an AI-powered advertising platform that facilitates in-video product placements for content creators and advertisers on social media and connected TV. Powered by Google Cloud's AI infrastructure, Rembrand enables brands to genuinely connect with audiences without disrupting the content.
Resolve AI, an always-on AI SRE, autonomously investigates incidents and helps run production systems using code, infrastructure, and observability data. With the intelligence and performance of Gemini on Vertex AI, Resolve AI improves MTTR, reliability, and engineering velocity for its customers.
SandboxAQ is scaling its AI-accelerated drug discovery and materials design platform on Google Cloud by training Large Quantitative Models (LQMs) on high-accuracy, physics-based simulations run across massively parallel compute nodes on Google Cloud. SandboxAQ recently made two large-scale datasets, called SAIR and AQCat25, publicly available for building and benchmarking LQMs for structure-aware drug potency prediction and for catalyst design, respectively.
Satlyt, a space compute company, enables in-orbit AI workloads by orchestrating intersatellite communication and routing. It uses Google Kubernetes Engine, Vertex AI, and scalable data infrastructure to deploy AI agents and plans to deploy Google's Gemma models in orbit.
Satisfi Labs will begin using Gemini models to power a new agentic platform for hundreds of customers in sports, entertainment, and tourism. The Satisfi AI platform will offer specialized agents supporting guest experiences, ticketing, on-site safety, and merchandise. Tuned by industry experts, Satisfi AI helps live experience businesses sell more, service faster, and gain real-time insights.
Savvy, an education-focused AI startup, revolutionizes learning by automatically generating flashcards and quizzes from PDFs, notes, videos, and podcasts. As students answer, Savvy uses Gemini to dynamically grade their answers, providing instant feedback and personalized learning.
Skyvern helps companies automate browser-based workflows with AI. Skyvern uses models like Gemini 2.5 Pro and computer vision to interact with websites, enabling it to automate tasks like filling out forms, procuring materials, and downloading invoices.
Subject.com, an AI-powered platform for grades 6 to 12, blends cinematic storytelling with advanced AI to make learning more engaging. Vertex AI, CloudSQL, and BigQuery power Subject’s teacher assistant tool, Spark, that provides instant feedback, an ‘Explain This’ text simplifier, a 24/7 Homework Helper, and personalized learning tied to student interests.
Tali.ai is a medical AI scribe designed to reduce the administrative burden on clinicians. Integrated with multiple electronic medical record systems across the U.S. and Canada, the company uses Vertex AI and Gemini models to automate clinical note-taking during patient visits and extract key insights.
Tinuiti, a performance marketing agency, used Vertex AI to develop an AI-powered service that develops and optimizes ad copy to increase performance.
Toonsutra, an India-based webcomic platform, is using Google Gemini to go global. By making stories accessible in regional languages and adding Lyria 2 for music, Gemini for voices, and Veo 3 for animation, the company is creating next-generation immersive comics for more audiences.
turbopuffer offers serverless vector and full-text search, helping AI businesses overcome the high costs and complexity of traditional database architectures. Its solution, built on Google Cloud infrastructure, has reduced AI database cost by up to 90% for customers, manages more 1 trillion documents, and handles more than 10 million writes and 10,000 queries every second.
Upwork, a human and AI-powered work marketplace, connects businesses with independent professionals. By leveraging the Vertex AI Text-to-Speech API, Upwork now delivers faster, more accurate talent matching and improved hiring efficiency for clients and freelancers.
Visla is an AI-powered video creation platform that helps businesses and creators produce pro videos in minutes. Using Imagen 4, Gemini Flash Image 2.5, Veo 3, and Visla’s AI Video Agent with Avatars, it adapts visuals and narration while automating polished content creation for learning, training, and marketing.
Windsurf, a leader in AI-assisted coding, is using Gemini 2.5 Pro to power its coding assistance IDE, as well as using Gemini models to support integrations with Cognition’s Devin AI.
Zapia AI, a retail technology company, uses AI agents to support millions of users with product discovery, local business searches, and purchase assistance, resulting in over 90% positive user feedback. Its multi-agent orchestration is powered by Gemini to improve agent reasoning, reduce latency, and lower operational costs.
Zefr, a global leader in responsible AI, powers Fortune 500 brand advertising with safety and suitability on platforms like YouTube and TikTok. Using patented “cognitive AI” with Gemini Flash and Vertex AI, Zefr analyzes video, image, audio, and text to deliver trusted, scalable solutions.

Google Cloud’s differentiated AI stack for startups

Around the world, startups are using Google Cloud at every step of development and every stage of growth. They’re using our AI and infrastructure to train and serve models, to handle multimodal requests, to build and scale applications, to develop AI agents, to manage underlying data, to support developers, to reach customers through our marketplace, and more.

Whatever the use case, our full AI-optimized tech stack provides startups with leading technology and choice at every layer, including:

Highly-performant first-party chips (TPUs) and third-party chips (GPUs) for training and inferencing, as well as the ability to move workloads between the two.
AI Studio, a fast way for startups to get started building applications with our models.
Our unified AI development platform, Vertex AI.
More than 200 AI foundation models, including our leading multimodal models like Nano Banana and Veo 3, and popular third-party models like Anthropic’s Claude and the open-source Llama and Mystral.
Developer tools like Gemini CLI, which provides access to Gemini directly within the coding terminal, streamlining workflows and boosting productivity.
Products like BigQuery and AlloyDB to handle an organization’s underlying data.
Security operations and threat intelligence tools to help startups secure their applications.
Services like Google Kubernetes Engine, Cloud Storage, and CloudSQL to quickly scale workloads and applications up and down.

If you can dream it, then you can build it, launch it, scale it, and sell it with Google Cloud.

More programs, initiatives, and product access for startups

One of the biggest challenges to starting up isn’t having the idea or the right technology — it’s access to the resources to make it happen and expertise to help tackle any challenges that arise.

Google Cloud provides a unique set of resources and benefits for startups through our Google for Startups Cloud Program, including cloud credits that are applicable across all Google Cloud products and AI models — including those from our partners like Anthropic and Meta. The program also offers mentorship and go-to-market programs that help startups reach customers, and even on-the-ground engineers who engage with and support startups as they build.

We also engage deeply with the startup community through our global series of Accelerators and our partnerships with top venture capital firms, incubators, and community organizations — making it easier than ever for startups to access Google Cloud’s unique technology and our extensive set of resources to help startups build and market their products.

For example, AI startups who work with firms like Lightspeed, Sequoia, or Y Combinator all have preferred access to our Google for Startups Cloud Program — supercharging their work with up to $350,000 in cloud credits along with mentorship and engineering support.

We’re committed to making it as easy as possible for startups to get building with Google Cloud AI. If you’re interested in our technology and want to get started, head to Google AI Studio or our Google for Startups Cloud Program and let’s get building together!

How much energy does Google’s AI use? We did the math

Thu, 21 Aug 2025 12:00:00 +0000

AI is unlocking scientific breakthroughs, improving healthcare and education, and could add trillions to the global economy. Understanding AI’s footprint is crucial, yet thorough data on the energy and environmental impact of AI inference — the use of a trained AI model to make predictions or generate text or images — has been limited. As more users use AI systems, the importance of inference efficiency rises.

That’s why we’re releasing a technical paper detailing our comprehensive methodology for measuring the energy, emissions, and water impact of Gemini prompts. Using this methodology, we estimate the median Gemini Apps text prompt uses 0.24 watt-hours (Wh) of energy, emits 0.03 grams of carbon dioxide equivalent (gCO₂e), and consumes 0.26 milliliters (or about five drops) of water¹ — figures that are substantially lower than many public estimates. The per-prompt energy impact is equivalent to watching TV for less than nine seconds.

At the same time, our AI systems are becoming more efficient through research innovations and software and hardware efficiency improvements. For example, over a recent 12 month period, the energy and total carbon footprint of the median Gemini Apps text prompt dropped by 33x and 44x, respectively,² all while delivering higher quality responses. These results are built on our latest data center energy emissions reductions and our work to advance carbon-free energy and water replenishment. While we’re proud of the innovation behind our efficiency gains so far, we’re committed to continuing substantial improvements. Here’s a closer look at these ongoing efforts.

Calculating the environmental footprint of AI at Google

Detailed measurement lets us compare across different AI models, and the hardware and energy they run on, while enabling system-wide efficiency optimizations — from hardware and data centers to the models themselves. By sharing our methodology, we hope to increase industry-wide consistency in calculating AI’s resource consumption and efficiency.

Measuring the footprint of AI serving workloads isn’t simple. We developed a comprehensive approach that considers the realities of serving AI at Google’s scale, which include:

Full system dynamic power: This includes not just the energy and water used by the primary AI model during active computation, but also the actual achieved chip utilization at production scale, which can be much lower than theoretical maximums.
Idle machines: To ensure high availability and reliability, production systems require a degree of provisioned capacity that is idle but ready to handle traffic spikes or failover at any given moment. The energy consumed by these idle chips must be factored into the total energy footprint.
CPU and RAM: AI model execution doesn't happen solely in ML accelerators like TPUs and GPUs. The host CPU and RAM also play a crucial role in serving AI, and use energy.
Data center overhead: The energy consumed by the IT equipment running AI workloads is only part of the story. The infrastructure supporting these computations — cooling systems, power distribution, and other data center overhead — also consumes energy. Overhead energy efficiency is measured by a metric called Power Usage Effectiveness (PUE).
Data center water consumption: To reduce energy consumption and associated emissions, data centers often consume water for cooling. As we optimize our AI systems to be more energy-efficient, this naturally decreases their overall water consumption as well.

Many current AI energy consumption calculations only include active machine consumption, overlooking several of the critical factors discussed above. As a result, they represent theoretical efficiency instead of true operating efficiency at scale. When we apply this non-comprehensive methodology that only considers active TPU and GPU consumption, we estimate the median Gemini text prompt uses 0.10 Wh of energy, emits 0.02 gCO2e, and consumes 0.12 mL of water. This is an optimistic scenario at best and substantially underestimates the real operational footprint of AI.

Our comprehensive methodology’s estimates (0.24 Wh of energy, 0.03 gCO2e, 0.26 mL of water) account for all critical elements of serving AI globally. We believe this is the most complete view of AI’s overall footprint.

Our full-stack approach to AI — and AI efficiency

Gemini’s dramatic efficiency gains stem from Google’s full-stack approach to AI development — from custom hardware and highly efficient models, to the robust serving systems that make these models possible. We’ve built efficiency into every layer of AI, including:

More efficient model architectures: Gemini models are built on the Transformer model architecture developed by Google researchers, which provide a 10-100x efficiency boost over the previous state-of-the-art architectures for language modeling. We design models with inherently efficient structures like Mixture-of-Experts (MoE) and hybrid reasoning. MoE models, for example, allow us to activate a small subset of a large model specifically required to respond to a query, reducing computations and data transfer by a factor of 10-100x.
Efficient algorithms and quantization: We continuously refine the algorithms that power our models with methods like Accurate Quantized Training (AQT) to maximize efficiency and reduce energy consumption for serving, without compromising response quality.
Optimized inference and serving: We constantly improve AI model delivery for responsiveness and efficiency. Technologies like speculative decoding serve more responses with fewer chips by allowing a smaller model to make predictions that are then quickly verified by a larger model, which is more efficient than having the larger model make many sequential predictions on its own. Techniques like distillation create smaller, more efficient models (Gemini Flash and Flash-Lite) for serving that use our larger, more capable models as teachers. Faster machine learning hardware and models enable us to use more efficient larger batch sizes when handling requests, while still meeting our latency targets.
Custom-built hardware: We’ve been designing our TPUs from the ground up for over a decade to maximize performance per watt. We also co-design our AI models and TPUs, ensuring our software takes full advantage of our hardware — and that our hardware is able to efficiently run our future AI software when both are ready. Our latest-generation TPU, Ironwood, is 30x more energy-efficient than our first publicly-available TPU and far more power-efficient than general-purpose CPUs for inference.
Optimized idling: Our serving stack makes highly efficient use of CPUs and minimizes TPU idling by dynamically moving models based on demand in near-real-time, rather than using a “set it and forget” approach.
ML software stack: Our XLA ML compiler, Pallas kernels, and Pathways systems enable model computations expressed in higher-level systems like JAX to run efficiently on our TPU serving hardware.
Ultra-efficient data centers: Google’s data centers are among the industry’s most efficient, operating at a fleet-wide average PUE of 1.09.
Responsible data center operations: We continue to add clean energy generation in pursuit of our 24/7 carbon-free ambition, while advancing our aim to replenish 120% of the freshwater we consume on average across our offices and data centers. We also optimize our cooling systems, balancing the local trade-off between energy, water, and emissions, by conducting science-backed watershed health assessments, to guide cooling type selection and limit water use in high-stress locations.

Our commitment to efficient AI

Gemini’s efficiency gains are the result of years of work, but this is just the beginning. Recognizing that AI demand is growing, we're heavily investing in reducing the power provisioning costs and water required per prompt. By sharing our findings and methodology, we aim to drive industry-wide progress toward more efficient AI. This is essential for responsible AI development.

^{1. A point-in-time analysis quantified the energy consumed per median Gemini App text-generation prompt, considering data from May 2025. Emissions per prompt was estimated based on energy per prompt, and applying Google’s 2024 average fleetwide grid carbon intensity. Water consumption per prompt was estimated based on energy per prompt, and applying Google’s 2024 average fleetwide water usage effectiveness. These findings do not represent the specific environmental impact for all Gemini App text-generation prompts nor are they indicative of future performance.
2. The results of the above analysis from May 2025 were compared to baseline data from the median Gemini App text-generation prompt in May 2024. Energy per median prompt is subject to change as new models are added, AI model architecture evolves, and AI chatbot user behavior develops. The data and claims have not been verified by an independent third-party.}

Strengthening network resilience with the Sol transatlantic cable

Wed, 09 Jul 2025 10:00:00 +0000

Today, we’re announcing Sol, a new transatlantic subsea cable system to connect the U.S., Bermuda, the Azores, and Spain. Sol translates to “sun” in Spanish and Portuguese, a nod to the cable system’s landing points in warmer climates. Alongside the Nuvem subsea cable, Sol completes our unique investment in transatlantic resiliency — with the two systems interconnecting terrestrially in the U.S. and in Iberia, as well as in Bermuda and the Azores.

The Sol cable will be manufactured in the U.S. and, once operational, bolster capacity and reliability for our growing network of 42 Google Cloud regions around the world, helping meet growing customer demand for Google Cloud and AI services across the U.S., Europe, and beyond.

Palm Coast, Florida, will anchor the Sol cable in the U.S. We’ll partner with DC BLOX to land the cable and establish a new connectivity hub in the Sunshine State. We’ll also develop a terrestrial route linking Palm Coast to our South Carolina cloud region.

When it’s complete, Sol will be the only in-service fiber-optic cable between Florida and Europe. In Spain, we’ll partner with Telxius to provide the necessary infrastructure to land the Sol cable in Santander, further integrating the Google Cloud region in Madrid into our global network.

"Florida is once again at the forefront of strengthening our nation's digital infrastructure. The anchoring of the Sol subsea cable in our state is a testament to our state's commitment to fostering an environment that is ripe for technological investment and innovation. We are proud to partner with companies like Google and DC BLOX on strategic infrastructure projects that will support the future of AI innovation and bring tangible benefits to our communities and the entire Sunshine State." – J. Alex Kelly, Florida Secretary of Commerce

"This is a landmark moment for Palm Coast, Flagler Beach, and Flagler County, and it's a clear signal that we are a community of the future, investing in our economic development and vitality. The Sol subsea cable is more than just infrastructure; it's a gateway to unprecedented global connectivity that will attract further high-caliber industries that our residents deserve. We are not just putting Palm Coast and our community on the map; we are building a direct route to the world's digital economy, ensuring a prosperous and dynamic future for our community." – Vice Mayor Theresa Carli Pontieri, Palm Coast, Florida

“DC BLOX is proud and honored to expand the foundational digital infrastructure that is vital to Florida’s growing economy. Supporting Google’s Sol cable, along with the capacity for additional cables, the new Palm Coast Cable Landing Station campus enhances Florida’s position as a nexus for global communications.” - Chris Gatch, Chief Revenue Officer, DC BLOX

“Digital connectivity is today a strategic factor for the economic and social development of any city. At Santander City Council, we are committed to facilitating this type of infrastructure because its implementation attracts investment, talent, and technology companies, in addition to generating employment and positioning us as a city prepared for remote work, advanced services, and the Smart City model. That's why we appreciate the interest that Google has shown in our coast and for choosing Santander as a transatlantic connection point!” - City Council Mayor Gema Igual, Santander, Spain,

“Telxius is delighted to support Google in bringing the Sol cable to Spain, a key milestone that will boost transatlantic connectivity with enhanced capacity, reliability, and resilience. Through our submarine landing services, we accelerate project implementation and help expand global networks for our customers.” - Mario Martín, CEO, Telxius

“The Government of the Azores welcomes this important investment in the region with great enthusiasm and congratulates Google on its visionary, pioneering, and strategic approach, which fully aligns with our own vision: that the Azores are an important and strategic hub for digital connectivity in Europe and the North Atlantic.” - Artur Lima, Vice President, Government of the Azores

“Promoting the expansion and modernization of submarine cable infrastructure, with a view to strengthening the country's international connectivity and digital competitiveness, is an essential goal for the government to the development of our country. The project for Google's new "Sol" cable, with 16 fiber optic cable pairs and the potential to provide redundancy to the "Nuvem" cable, also from that company, will increase resilience and respond to the growing demand for digital infrastructure in Portugal and Europe.”- Miguel Pinto Luz, Minister of Infrastructure and Housing, Portugal

“Bermuda welcomes Google’s continued investment with the announcement of a second cable ‘Sol,’ further establishing Bermuda as a digital hub in the Atlantic. Bermuda greatly values its positive relationship with Google. This second cable highlights the benefits of this Government’s strategy to develop Bermuda as a progressive world leading jurisdiction for technology companies.” - Alexa Lightbourne, Minister of Home Affairs, Bermuda

Sol will add capacity, increase reliability, and decrease latency for Google users and Google Cloud customers around the world. Alongside cable systems like Nuvem, Firmina, Equiano, and Grace Hopper, Sol further establishes key locations across the Atlantic as connectivity hubs, strengthening local economies and bringing AI’s benefits to people and businesses around the world.

Just say no: Build defense in depth with IAM Deny and Org Policies

Fri, 27 Jun 2025 16:00:00 +0000

In today's cloud landscape, safeguarding your cloud environment requires bolstering your Identity and Access Management (IAM) approach with more than allow policies and the principle of least privilege. To bolster your defenses, we offer a powerful tool: IAM Deny Policies.

Relying only on IAM Allow policies leaves room for potential over-permissioning, and can make it challenging for security teams to consistently enforce permission-level restrictions at scale. This is where IAM Deny comes in.

IAM Deny provides a vital, scalable layer of security that allows you to explicitly define which actions principals can not take, regardless of the roles they have been assigned. This proactive approach can help prevent unauthorized access, and strengthens your overall security posture, providing admin teams overriding guardrail policies throughout their environment.

Understanding IAM Deny

The foundation of IAM Deny is built on IAM Allow policies. Allow policies define who can do what and where in a Google Cloud organization, binding principals (users, groups, service accounts) to roles that grant access to resources at various levels (organization, folder, project, resource).

IAM Deny, conversely, defines restrictions. While it also targets principals, the binding occurs at the organization, folder, or project level — not at the resource level.

Key differences between Allow and Deny Policies:

IAM Allow: Focuses on granting permissions through role bindings to principals.
IAM Deny: Focuses on restricting permissions by overriding role bindings given by IAM Allow, at a hierarchical level.

IAM Deny acts as a guardrail for your Google Cloud environment, helping to centralize the management of administrative privileges, reduce the need for numerous custom roles, and ultimately enhance the security of your organization.

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How IAM Deny works

IAM Deny policies use several key components to build restrictions.

Denied Principals (Who): The users, groups, or service accounts you want to restrict. This can even be "everyone" in your organization, or even any principal regardless of organization (noted by the allUsers identifier).
Denied Permissions (What): The specific actions or permissions that the denied principals cannot use. Most Google Cloud services support IAM Deny, but it's important to verify support for new services.
Attachment Points (Where): The organization, folder, or project where the deny policy is applied. Deny policies can not be attached directly to individual resources.
Conditions (How): While optional, these allow for more granular control over when a deny policy is enforced. Conditions are set with Resource Tags using Common Expression Language (CEL) expressions, enabling you to apply deny policies conditionally (such as only in specific environments or unless a certain tag is present).

IAM Deny core components.

Start with IAM Deny

A crucial aspect of IAM Deny is its evaluation order. Deny policies are evaluated first, before any Allow policies. If a Deny policy applies to a principal's action, the request is explicitly denied, regardless of any roles the principal might have. Only if no Deny policy applies does the system then evaluate Allow policies to determine if the action is permitted.

There are built-in ways you can configure exceptions to this rule, however. Deny policies can specify principals who are exempt from certain restrictions. This can provide flexibility to allow necessary actions for specific administrative or break-glass accounts.

Deny policies always evaluate before IAM Allow policies.

When you can use IAM Deny

IAM Deny policies can be used to implement common security guardrails. These include:

Restricting high-privilege permissions: Prevent developers from creating or managing IAM roles, modifying organization policies, or accessing sensitive billing information in development environments.
Enforcing organizational standards: By limiting a set of permissions no roles can use, you can do things like prevent the misuse of overly-permissive Basic Roles, or restrict the ability to enable Google Cloud services in certain folders.
Implementing security profiles: Define sets of denied permissions for different teams (including billing, networking, and security) to enforce separation of duties.
Securing tagged resources: Apply organization-level deny policies to resources with specific tags (such as iam_deny=enabled).
Creating folder-level restrictions: Deny broad categories of permissions (including billing, networking, and security) on resources within a specific folder, unless they have any tag applied.

Complementary security layers

IAM Deny is most effective when used in conjunction with other security controls. Google Cloud provides several tools that complement IAM Deny:

Organization Policies: Allow you to centrally configure and manage organizational constraints across your Google Cloud hierarchy, such as restricting which APIs are available in your organization with Resource Usage Restriction policies. You can even define IAM Custom Constraints to limit which roles can be granted.
Policy troubleshooter: Can help you understand why a principal has access or has been denied access to a resource. It allows you to analyze both Allow and Deny policies to pinpoint the exact reason for an access outcome.
Policy Simulator: Enables you to simulate the impact of changes to your deny policies before applying them in your live environment. It can help you identify potential disruptions and refine your policies. Our Deny Simulator is now available in preview.
IAM Recommender: Uses machine learning to analyze how you’ve applied IAM permissions, and provide recommendations for reducing overly permissive role assignments. It can help you move towards true least privilege.
Privileged Access Management (PAM): Can manage temporary, just-in-time elevated access for principals who might need exceptions to deny policies. PAM solutions provide auditing and control over break-glass accounts and other privileged access scenarios.
Principal Access Boundaries: Lets you define the resources that principals in your organization can access. For example, you can use these to prevent your principals from accessing resources in other organizations, which can help prevent phishing attacks or data exfiltration.

Implementing IAM Deny with Terraform

The provided GitHub repository offers a Terraform configuration to help you get started with implementing IAM Deny and Organization Policies. This configuration includes:

An organization-level IAM Deny Policy targeting specific administrative permissions on tagged resources.
A folder-level IAM Deny Policy restricting Billing, Networking, and Security permissions on untagged resources.
A Custom Organization Policy Constraint to prevent the use of the roles/owner role.
An Organization Policy restricting the usage of specific Google Cloud services within a designated folder.

Key steps for using the Terraform configuration:

Clone the repository:

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2. Navigate to the examples/iam-deny folder, then switch to the Terraform directory:

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3. Prepare terraform.tfvars: Copy terraform.tfvars.example to terraform.tfvars and edit it to include your Organization ID, Target Folder ID, and principal group emails for exceptions.

4. Create a tag key and tag value in your organization to enable these policies

1. You can name these whatever you want, but for our example you can use tag key (iamdeny) and tag value (enabled).

5. Update `main.tf` Tag IDs: Replace placeholder tag key and value IDs with your actual tag IDs in the denial_condition section for each policy.

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a. NOTE: This is optional, you can also use this expression to deny all resources when the policy is applied

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Remember to review the predefined denied permissions in files like `billing.json`, `networking.json`, and `securitycenter.json` (located in the `/terraform/profiles/` directory) and the `denied_perms.tf` file to align them with your organization's security requirements.

6. Initialize, review and apply Terraform:

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Always consult with your security team before deploying these policies.

Embrace the power of no

IAM is a critical component for enhancing security and its strategic implementation is key to a comprehensive defense-in-depth strategy. (Please see our IAM guides — I hate IAM but I need it desperately and Scaling the IAM mountain, an in-depth guide — for more information on getting started with IAM.)

Implementing IAM Deny policies is a crucial step in enhancing your Google Cloud security posture. By explicitly defining what principals cannot do, you add a powerful layer of defense against both accidental misconfigurations and malicious actors.

When combined with Organization Policies, Policy Troubleshooter, Policy Simulator, and IAM Recommender, IAM Deny empowers you to enforce least privilege more effectively and build a more secure cloud environment. Start exploring the provided Terraform example and discover the Power of No in your Google Cloud security strategy.

This content was created from learnings gathered from work by Google Cloud Consulting with enterprise Google Cloud Customers. If you would like to accelerate your Google Cloud journey with our best experts and innovators, contact us at Google Cloud Consulting to get started.

Hej Sverige! Google Cloud launches new region in Sweden

Thu, 06 Mar 2025 02:00:00 +0000

We're thrilled to launch our cloud 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.

One of Sweden’s most globally recognized companies, IKEA, worked closely with Google Cloud on this new region:

"IKEA is delighted to collaborate with Google Cloud in celebrating the new region in Sweden, underscoring our shared commitment to fostering innovation in the country. Google Cloud's scalable and reliable infrastructure helps us to deliver a seamless shopping experience for our customers, helping us make interior design more accessible to everyone.” - Francesco Marzoni, Chief Data & Analytics Officer, IKEA Retail (Ingka Group)

Swedish audio-streaming service Spotify is one of Google Cloud’s earliest customers, is also excited to welcome a Google Cloud region in its home country:

“Over the past decade, we've forged a valuable partnership with Google Cloud, growing and innovating together. In a space where speed is paramount and even milliseconds matter, the new Google Cloud region in Sweden will be a catalyst for accelerating innovation for Swedish businesses and digital unicorns. We’re excited to be part of this evolution and growing cloud community.” - Tyson Singer, VP of Technology & Platforms, Spotify

Fueling Swedish innovation and growth

This new region provides Swedish businesses, organizations, and individuals with a powerful new platform for growth, powered by Google Cloud technologies like AI, machine learning, and data analytics. By offering high-performance, low-latency cloud services in Sweden, we're enabling faster application development, richer user experiences, and enhanced business agility for customers such as Tradera, an online marketplace:

“Google Cloud’s technology has empowered Tradera to enhance customers' selling experience, enabling them to sell more and faster. The launch of Google Cloud’s new Swedish region will empower a wider range of businesses to reap the benefits we've experienced and innovate at the pace we have, and that’s exciting.” - Linus Sjöberg, CTO, Tradera

This region also directly addresses data residency requirements and digital sovereignty concerns, removing key barriers to cloud adoption for many Swedish organizations. For the first time, these organizations can harness the full potential of Google Cloud's services while maintaining control over their data's location. We see this as a pivotal moment, unlocking possibilities and empowering Swedish ingenuity to flourish. From startups disrupting traditional industries to established enterprises undergoing digital transformation, the new region in Sweden will provide the infrastructure and tools needed to thrive in the AI era.

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In fact, the Swedish government's AI Commission recently launched its AI roadmap proposing an AI factory for the public sector to collaborate on a common AI infrastructure. This new region highlights our partnership in the Swedish AI-innovation ecosystem by strengthening the country’s AI infrastructure capabilities:

“Swedish AI infrastructure investments, such as a Swedish cloud region, strengthens Swedish AI development and enables AI innovations with data stored in Sweden.” - Martin Svensson, Director of the Swedish National Center for Applied AI called AI Sweden

Google Cloud also offers organizations in Sweden a new way to build resiliently, reduce costs, and accelerate sustainability impact through the smarter use of data and AI. Current projections indicate this region will operate at or above 99% carbon-free energy (CFE) in its first full year of operation in 2026, due to the Swedish grid’s electricity mix. We estimate our Swedish operations will have one of the highest Google CFE¹ scores among all electricity grid regions where Google operates. Google also announced its first Swedish power purchase agreement (PPA) in 2013, and has since signed additional long-term agreements with clean energy developers that enabled more than 700 megawatts (MW) of onshore wind projects in the country. These sustainability credentials are particularly important for Telenor Sweden:

“We at Telenor Sweden are very pleased about Google Cloud’s new, sustainability-focused cloud region launch in Sweden. This expansion directly supports our ambitious digital transformation, enabling us to scale our AI, data analytics, and cloud initiatives more efficiently while meeting our customers’ ever-growing demands for high-performance services. By leveraging Google Cloud’s local presence and green infrastructure, we can strengthen our commitment to innovation and continue delivering value-driven, secure and fast connectivity.” - Kristin Lindmark, CIO, Telenor Sweden

Technical advantages

Beyond its local impact, the region in Sweden offers a host of technical benefits. Digital bank Nordnet looks forward to taking advantage of them:

“By partnering with Google Cloud, Nordnet has built a new, cloud-native platform that takes advantage of faster time-to-market, improved scalability, and enhanced security. Google Cloud’s new Swedish region gives us the possibility to further strengthen these benefits, enabling Nordnet to enhance its platform for savings and investments, offer exceptional customer experience, and accelerate our growth.” - Elias Lindholm, CTO, Nordnet

The new region’s technical benefits include:

High performance and low latency: Experience just milliseconds of latency to Stockholm and significantly reduced latency for users across Sweden and neighboring countries. This translates to faster application response times, smoother streaming, and enhanced online experiences, boosting productivity and user satisfaction. One of our customers, Bonnier News, exemplifies this technological edge:

“In today's fast-paced media landscape, timely news delivery and rapid adaptation are crucial. The new Google Cloud region in Sweden offers Bonnier News the agility and speed we need to innovate and stay ahead. With faster data processing and lower latency, we can ensure our readers get the latest news and insights, whenever and wherever they need it.” - Lina Hallmer, CTO, Bonnier News

Uncompromising data sovereignty and security: This new region in Sweden benefits from our robust infrastructure, including data encryption at rest and in transit, granular data access controls, data residency, and sophisticated threat detection systems. We adhere to the highest international security and data protection standards to help ensure the confidentiality, integrity, and sovereignty of your data.
Scalability and flexibility on demand: Google Cloud’s infrastructure is designed to scale easily with your business. Whether you’re a small startup or a large corporation, you can easily adjust your resources to meet your evolving needs.

Investing in Sweden's digital future

Google's commitment to Sweden extends beyond this new cloud region. We're making significant investments in the country's digital ecosystem to foster talent development and support local communities. Initiatives include:

Exclusive launch partnerships: We're thrilled to announce the launch of our new cloud region in collaboration with our exclusive launch partners: Devoteam and Tietoevry Tech Services. The deep engineering and consulting expertise of our launch partners helps customers quickly realize the benefits of the new region.
Local collaboration: We're working with Swedish businesses, educational institutions, and government organizations to create a thriving cloud ecosystem. These collaborations focus on skills development, knowledge sharing, and supporting local innovation.

Looking ahead: A partnership for progress

The launch of the new cloud region in Sweden is just the first step in our journey together. We're dedicated to ongoing investment in Sweden, partnering with local businesses and organizations to build a thriving digital future. This region will be a powerful engine for innovation and growth, empowering Swedish organizations to transform their industries, unlock new opportunities, and shape the world of tomorrow. We can’t wait to see what you create, look for Stockholm (europe-north2) in the console to get started today! Välkommen till Google Cloud, Sweden!

Want to learn more? Check out our other global region locations and how Google Cloud builds infrastructure.

^{1. Carbon-free energy is any type of electricity generation that doesn’t directly emit carbon dioxide, including (but not limited to) solar, wind, geothermal, hydropower, and nuclear. Sustainable biomass and carbon capture and storage (CCS) are special cases considered on a case-by-case basis, but are often also considered carbon-free energy sources. For more information on our carbon-free energy strategy and plans, see Google’s 2024 Environmental Report.}

How we got to 100 million cells in our global Li-ion rack battery fleet

Tue, 25 Feb 2025 16:00:00 +0000

When it comes to data center power systems, batteries play an important role. The applications that run in our data centers require nearly continuous uptime. And while utility power is highly reliable, power outages are unavoidable.

When an outage happens, batteries can supply short-duration power, allowing servers to operate continuously when the facility switches between AC power sources, or to ride through transient power disturbances. Or, if a facility loses both primary and alternate power sources for an extended period of time, batteries can supply sufficient power to allow machines to execute a clean shutdown procedure. This is helpful in expediting machine restarts after the power outage. More importantly, it helps ensure that critical user data is safely stored to disk and not lost in the power disruption.

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At Google, we rely on a 48Vdc rack power system with integrated battery backup units (BBUs), and in 2015, we became one of the first hyperscale data center providers to deploy Lithium-ion BBUs. These Li-ion batteries had twice the life, twice the power and half the volume of previous-generation lead-acid batteries. Switching from lead-acid batteries to Li-ion means we deploy only one-quarter the number of batteries, greatly reducing the battery waste generated by our data centers.

We recently reached an important milestone: Google has more than 100 million cells deployed in battery packs across our global data center fleet. This is remarkable, and only possible thanks to the safety-first approach we take to deploy Li-ion batteries at scale.

The main safety risk associated with Li-ion batteries is the battery going into thermal runaway if it’s accidentally mishandled or exposed to excessive temperatures or overcharging. While a rare event, the resulting fire is extremely difficult to extinguish due to the large amount of heat generated, driving a thermal runaway chain reaction to nearby cells.

To deploy this large fleet of Li-ion cells, we have had to make safety a core principle of our battery design. Specifically, as an early adopter of the UL9540A thermal runaway test method, we subject our Li-ion BBU designs to rigorous flame safety testing that demonstrates their ability to limit thermal runaway. As a result, Google has successfully been granted permits to deploy BBUs in some of the world’s most stringent jurisdictions, in the APAC region.

In addition, our Li-ion BBUs benefit from our distributed UPS architecture that offers significant availability and TCO benefits compared to traditional monolithic UPS systems. The distributed UPS architecture improves machine availability by: 1) reducing the failure-domain blast radius to a single rack, and 2) locating the batteries in the rack to eliminate intermediate points of failure between the UPS and machines. This architecture also provides TCO benefits by scaling the UPS with the deployment, i.e., reducing day-1 UPS cost. Additionally, locating the batteries in the rack on the same DC bus as the machines eliminates intermediate AC/DC power conversion steps that cause efficiency losses. In 2016 we shared the 48V rack power system spec with the Open Compute Project, including specs for the Li-ion BBUs.

Li-ion batteries have been crucial to ensuring the uninterrupted operation of Google Cloud data centers. By transitioning from lead-acid to Li-ion BBUs, we’ve significantly improved power availability, efficiency, and lifespan, even as we simultaneously address their critical safety risks. Our commitment to rigorous safety testing and adherence to standards and test methods like UL9540A has enabled us to deploy millions of Li-ion BBUs globally, providing our customers with the high level of reliability they expect from Google Cloud.

Getting to 100 million Li-ion batteries is just one of many examples of how we are building a reliable cloud and power-efficient AI. As data center power systems evolve to include new technologies including large battery energy storage systems (BESS) and new workload requirements (AI workloads), we remain dedicated to exploring and implementing innovative solutions to build the most efficient and safest cloud data centers.

^{The authors would like to acknowledge Vijay Boovaragavan, Matt Tamashiro, Sandeep Sebastian, Thibault Pelloux-Gervais, Ken Wong, Mike Meakins, Stanley Fung, and Scott Sharp for their contributions.}