AI & Machine Learning

Power your agents: Gemini 3.8 Live with Live Avatar is now generally available

Thu, 24 Sep 2026 15:00:00 +0000

Following our announcement of Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking last week, we are thrilled to share that Gemini 3.8 Live with Live Avatar is now generally available in Gemini Enterprise. First previewed at Google Cloud Next 2026, the technology is now officially ready for enterprise production.

As enterprise voice AI evolves beyond basic speed and cost metrics, our priority has shifted to making each interaction even higher quality. Gemini 3.8 Live already delivers a native speech-to-speech foundation for fluid, responsive dialogue. The Live Avatar feature brings an interactive visual presence to conversational video agents across web, mobile, and interactive kiosks. Together, you’ll have access to:

Video avatars: Conversational video with the Live Avatar feature can generate video avatars with synchronized lip-syncing. Note: Custom avatar feature is available via allowlist only.
Fluid dialogue: Native speech-to-speech means more natural interruption recovery without dropping conversation context or backend transactions.
Tool calling: It executes tools and API calls in the background while continuing the conversation, so the model can acknowledge requests and keep chatting while tasks finish in the background.
Breaks language barriers: Gemini 3.8 Live understands and speaks 97 languages, with automatic language detection.
Make it easy for agents to see what your user sees: Live visual understanding can process live camera feeds and screen shares alongside audio, all at the same time.

Though Gemini 3.8 Live Extended Thinking remains in private preview, Gemini 3.8 Live with Live Avatar is now available with US and EU endpoints, with provisioned throughput, enterprise compliance, and strict data governance. Try the model and its features in Gemini Enterprise, and start building now with API.

Trust and transparency at its core

To safeguard identity and prevent misuse, customers can deploy from a library of curated, pre-built avatars, while custom avatar creation is gated behind a strict enterprise allowlisting and verification process. Furthermore, all generated audio and video streams carry imperceptible SynthID watermarks, ensuring AI-generated content remains transparent and verifiable.

Three demos of Gemini 3.8 Live with Live Avatar in action

#1: Interactive custom avatar

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Watch how Gemini 3.8 Live makes it possible to build a custom avatar by adding system instructions, uploading a single reference photo and audio file sample.

Demo #2: Live video understanding in a voice-first claims intake

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In this demo, you’ll see a common use case come to life using Gemini 3.8 Live: an intake agent. In this scenario, we chose an insurance claims agent. You talk and show the damage on camera, and the claim notebook fills itself in as you go. In the background, an ADK agent team checks the policy, applies the intake rules, and builds the adjuster packet. To dive deeper, check out the open-source code.

#3: A real-time voice AI agent with Google ADK and Gemini Live API

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See how developers can use Agent Development Kit (ADK) to define agents, manage runners and session memory, and stream real-time audio directly to the Gemini Live API without a traditional speech-to-text pipeline.

How our customers are innovating with Gemini 3.8 Live with Live Avatar

Cox Automotive built an AI-powered shopping assistant for Autotrader that uses live screen-highlighting and tool-calling capabilities to guide car shoppers through vehicle search, comparison, and financing — in real time, through natural conversation.

“Shoppers increasingly expect to describe what they need in their own words rather than work through filters and menus. Autotrader’s new conversational AI Avatar brings that experience to vehicle discovery by matching natural conversation to the right inventory. It is another step toward our vision of connected intelligence, where every consumer interaction draws on the full depth of Cox Automotive data.” — Marianne Johnson, EVP and Chief Product Officer, Cox Automotive.

“We're building a personal AI that knows you, speaks your language, and is always on your side. Today, it handles over a million live calls daily across nine Indian languages. Gemini 3.8 Live improved interruption handling, multilingual conversations, and tool-call reliability. This AI doesn't just answer calls; it gets things done for you.” — Akhilesh Damaraju, CEO, Equal AI.

“We're excited that Gemini 3.8 Live and Agentforce are coming together to reimagine what's possible in intelligent service. This collaboration between Salesforce AI Research and Google combines real-time, multimodal capabilities with agentic AI to explore new ways to create richer, more intuitive customer experiences from first contact to resolution." — Bob Van Osten, VP of Product for Agentforce, Salesforce.

“Our team has been very impressed with Gemini 3.8 Live throughout testing and benchmarking! The updates made to Voice Activity Detection and the improvements to overall latency are huge steps forward and further our ability to deliver the highest quality AI Assistant on the SPECS platform.” — Eric Walsh, Software Engineer, Specs.

Start building today

Gemini 3.8 Live with Live Avatar are available now for enterprise customers:

Try the model in Gemini Enterprise
Access the Gemini Live API documentation for integration guides
Review pricing on our pricing page
Skill for building real-time, bidirectional streaming apps with the Gemini Live

If your application requires specialized, modular audio capabilities, explore our other audio models:

Reach out to your Google Cloud sales representative to activate provisioned throughput, discuss customized deployment architectures and allowlisting for custom avatar.

How growing Latin American midsize businesses are building in the AI era

Thu, 24 Sep 2026 14:30:00 +0000

Latin America’s small and medium-sized businesses are the heartbeat of the region's economy — accounting for more than 60% of total employment in the region, according to United Nations estimates. And just like their enterprise peers, everywhere you look, ambitious teams are moving fast to embrace AI.

Many have already transitioned from experimenting with generative tools and agentic workflows to using them every day to work smarter, save time, and deliver exceptional customer experiences. These growing businesses are particularly focused on maximizing the benefit they get from their investments in AI, whether that’s using a fast, low-cost model to summarize daily emails or deploying an advanced model for complex data analysis, teams can match the right AI capability to their exact task and budget.

It’s this range of options, and a familiarity with the broader suite of Google business, media, and advertising tools that has led many SMBs to choose Google Cloud, and Gemini Enterprise in particular, as their AI platform of choice. By doing so, they’re able to build custom AI agents, streamline daily tasks and paperwork, and offer customers instant support with the speed and reach needed to compete on a global scale.

With our unique front row seat, we’ve seen the benefit SMBs are getting from leveraging Gemini Enterprise, not only for generative AI, but as a catalyst for adopting other essential cloud tools like Google Kubernetes Engine and BigQuery for complete end-to-end modernization. The number of Latin American-based small and medium businesses using Google Cloud AI tools has grown 8x year-over-year and the number of Brazil based small and medium businesses using Google Cloud AI tools has grown 9x year-over-year. This rapid adoption spans our Gemini models, Gemini Enterprise, and core Cloud infrastructure, and are helping businesses to:

Roll out better customer support systems to help escalate and resolve customer support calls more quickly.
Automate repetitive actions in areas like payroll and accounting.
Help more employees understand and leverage data at work — even those not trained as data analysts.
Rapidly create and implement new designs for marketing collateral.
Help more people build their own AI agents to help them in their everyday jobs.

As we head into today’s Google Cloud Summit in Brazil, we were proud to showcase nearly 20 of our newest Latin American SMB customers using Google AI to reduce busywork, serve their customers faster, and grow their businesses.

Announcing new Latin American customers putting Google AI to work

AdGoat, an Argentina-based adtech company processing more than 10 billion annual ad requests across more than 100 global websites. It uses Cloud Run, the Gemini API, and Gemini Enterprise to automate content analysis, ad bidding, and audience targeting to help e-commerce brands drive higher campaign returns.
Angelus, a Brazilian dental and healthcare manufacturing company, uses Gemini Enterprise to streamline project management across its research and development department. This enables its teams to automatically pull technical project data into pre-approved templates aligned with the company’s brand identity and regulatory requirements.
BunkerDB, a marketing science company operating across Latin America, uses Gemini Enterprise, Cloud Run, and Cloud Storage to power an AI platform that organizes marketing assets, checks brand compliance, generates or adapts multimodal content, and predicts ad performance before launch. All of this helps it reduce creative turnaround times from weeks to hours and cut cost per lead by up to 25%.
Caffeine Army, a Brazilian wellness and high-performance company that connects people with solutions in nutrition, sports, and well-being, deployed BigQuery and Gemini Enterprise on Google Cloud to unify customer purchase insights, enabling faster creative campaign turnarounds and boosting team productivity across the organization.
Convert, a Brazilian marketing and analytics provider, uses Looker, BigQuery, and Cloud Run to power five specialized AI agents that answer complex business questions in natural language, speeding up report deliveries by 65% and reducing operational costs by 32%.
Growth Digital, a Google Ad sales rep operating across 13 Latin American countries, used BigQuery and Gemini Enterprise to build over 113 AI agents, enabling teams to build proposals 5x faster, cut campaign reporting time by 80%, and reduce financial error rates to under 0.01%.
GrupoTusMaquinas.com, an equipment management platform based in Chile, deployed Google Cloud AI tools and Gemini models to create digital tracking profiles for trucks and machinery, allowing businesses to query fleet status in plain language and manage vehicles regardless of brand or location.
HealthAtom, a healthcare technology company, uses the Gemini API, Firestore, and Cloud Functions to power AI assistants across its clinical platforms, automating appointment scheduling and medical record reviews while supporting 80 million annual patient interactions.
KLog.co, a Chilean logistics technology company digitizing freight forwarding across Latin America, uses Gemini Enterprise, BigQuery, and Google Workspace to automate cargo tracking and shipping paperwork, cutting manual data entry errors by over 90% and increasing document processing capacity tenfold.
NEEOH, a leading Brazilian out-of-home advertising communication platform, uses Gemini Enterprise to standardize secure AI usage across its organization, enabling teams to generate campaign copy and build pitch proposals faster while keeping corporate client data secure.
Luxia Agro, an Argentinian foreign trade supplier of crop protection products, uses Gemini 3.5 Flash and Gemini Enterprise to automatically pull key details from complicated shipping emails and update their central business systems. This allows it to automate 80% of foreign trade operations and cut manual processing errors in half.
Macal, a Chilean auction company, uses the Gemini Enterprise, Cloud Run, BigQuery, and Security Command Center to automatically verify property records and modernize its technology systems, cutting software development times from weeks to days and lowering infrastructure costs by up to 30%.
Ninecon, a Brazilian tech consulting firm, deployed Gemini Enterprise to integrate AI directly into employee workflows, allowing managers to track usage patterns and optimize project turnaround times with real-time insights.
Nova Gestões, a customer service and operations provider in Brazil, uses Cloud Speech-to-Text and Gemini Enterprise to translate and analyze 100% of customer calls in real time, reducing post-call manual data entry and boosting team productivity by 30%.
Romi, a Brazilian industrial machinery manufacturer, uses the Gemini API and Gemini Enterprise to power an interactive chat assistant directly on CNC machine HMI (human machine iInterface), giving factory operators instant answers grounded in official manuals and generating QR codes for step-by-step instructional videos.
Supermercados El Dorado, a leading supermarket chain in Uruguay, leverages Google Compute Engine and Gemini Enterprise to modernize legacy testing infrastructure and connect custom AI agents within their daily workflows, boosting team productivity across departments.
Tryvia, a Brazilian IT and business solutions provider, uses Google Cloud, Looker, and Gemini Enterprise to move off legacy physical servers, giving teams real-time reporting dashboards and AI tools that speed up software development and daily tasks.
Via Cristais, a major highway operator in Brazil, leverages Google Contact Center as a Service to speed up emergency routing for highway accidents, reducing caller wait times, improving driver satisfaction, and mitigating the impact of call center staff turnover.
WeSpeak, an AI conversational platform for the hospitality industry in Latin America, uses Cloud Run, Gemini Pro, and Gemini Flash to automate end-to-end guest interactions across messaging channels like WhatsApp and Instagram. This has helped it achieve an 85% resolution rate and a 2x increase in overall sales volume for hotel clients.

Helping your team build AI skills

To help growing teams get the absolute most out of AI, we’ve created easy, no-cost learning programs that anyone can use:

Programs for small and medium businesses: Explore beginner-friendly training paths or join specialized programs to learn how to build custom AI assistants for your day-to-day work.
Google skills for organizations: Access thousands of free, on-demand AI courses and hands-on practice labs designed by experts at Google Cloud and Google DeepMind.
Get certified: Help your staff gain industry-recognized AI certificates through guided courses, expert mentoring, and skill badges.

By offering easy-to-use tools and free training — from everyday office apps in Workspace to advanced AI on Google Cloud — Google is here to help Latin American businesses thrive today and in the future.

The three things today's hottest startups are looking for in their AI stack

Thu, 24 Sep 2026 13:00:00 +0000

Google Cloud has become the platform of choice for startups building AI.

Our uniquely complete stack — including a choice of first- and third-party compute and models; our platform for building and managing agents; and our products for securing AI workloads — has emerged as the single most important driver of this growth and it is powering AI development for many of the most exciting and innovative startups in the world.

As a result, startups are choosing to build and run on Google Cloud at a higher rate than they were three years ago, at the start of the AI era.

Given how quickly the technology industry moves in the AI era, the choices startups make can be notable. As we’ve worked together and watch many of these leaders scale, we’ve observed a few important trends emerging over the past several months. We expect these decisions will continue to shape the choices startups make about the platforms and technology they use:

Gemini Enterprise, which includes our tools for managing TPU and GPU clusters, services for building and managing agents, and APIs to access both first- and third-party models, is growing significantly with startups. And when startups use Gemini Enterprise, they also tend to use our “core cloud” services like Storage, BigQuery, or GKE.
Gemini models — as well as several of the third-party models available through Gemini Enterprise — are providing very strong price-performance for startups. These customers are increasingly deploying both our frontier models and “workhorse” models as their AI to power workloads as diverse as scientific research, generative media creation, and financial analysis.
Access to compute on GPUs and TPUs is critical for AI and the ability to choose one — or both — is unique to Google Cloud. But importantly, startups almost always use additional products from our stack alongside these chips, like models, tools for building agents, or services like BigQuery or GKE. These additional technologies illustrate how the needs of startups are rarely singular, and just how much value they find in having ready access to a strong suite of second-, third-, and fourth-level technologies beyond just compute.

We can see the demand for these technologies first-hand in some of the recent deals we have struck in the past 60 days with a number of leading startups across sectors:

Artificial Agency, a startup focused on generative behavior in games, is running critical AI workloads and research on Google Cloud, where it is using NVIDIA GPUs for model training and inference, as well as Gemini models and Cloud Storage.
Arya Health is building the AI workforce for healthcare, deploying agentic AI to perform the non-clinical administrative work that limits providers’ ability to deliver and expand care. Arya’s AI agents work across scheduling, intake, recruiting, onboarding, compliance, after-hours operations, and other critical workflows, interacting through voice, text, email, and providers’ existing systems. Arya uses a range of Gemini models across its agentic infrastructure, including Gemini 2.5 Pro, 3.1 Flash, and 3.5 Flash Lite, selecting models based on the reasoning, speed, and cost requirements of each workflow.
Casco is a cybersecurity startup whose autonomous agent swarms execute sophisticated, multi-step attacks to uncover vulnerabilities across enterprise applications, cloud environments, and infrastructure. Its architecture combines advanced reasoning models for complex, long-running tasks with fast models such as Gemini 3.5 Flash for focused subagent work. Google Cloud’s model portfolio and infrastructure, including Provisioned Throughput, help Casco match each workload with the right combination of intelligence, speed, and capacity.
CodeRabbit has been a pioneer in independent AI code review and has expanded that layer into Agentic Change Management, the control plane for agentic software development. They use our Cloud Run and Storage products to underpin their application, and are now beginning to leverage Gemini 3.1 Pro and other Gemini models to power use cases like analyzing how a single code change impacts an entire project, writing clear and contextual review comments to explain logic bugs, and instantly generating one-click fixes for developers.
Comfy offers a platform for creatives to build brand-consistent content and media with generative AI. They are utilizing a mix of NVIDIA systems and Google media generation models, like Veo and Nano Banana, in their platform.
MicroAGI, a German AI robotics startup, recently announced they would access NVIDIA Blackwell systems for model training through Google Cloud. They will also use Gemini Enterprise Agent Platform and AI models on Google Cloud to help robotics process multimodal information like video.
Ineffable Intelligence, the London-based superintelligence startup, recently announced a partnership with Google Cloud to access NVIDIA Vera Rubin systems as well as high-efficiency AI networking and storage.
PEAR Health Labs built and runs its AI health and fitness companion and agentic health platform entirely on Google Cloud, using our Gemini 3.5 Flash model as well as our data cloud products.
Roboforce is a physical AI company building scalable robots for industrial environments. They recently signed a new agreement with Google Cloud to utilize our GPU-powered VMs, which will be used to train and serve their custom and post-trained physical AI models.
xFigura offers a platform that gives architecture teams a single, secure canvas that brings generative models into one place for AI-driven design. Built on Google Cloud, xFigura uses models like Nano Banana and Omni to help designers generate and refine concepts in plain language, with authorship staying with the architect.
SciFin is a startup that helps revenue teams uncover "revenue reality" by converging fragmented sales context across CRM systems, documents, emails, and other sources. They are utilizing Gemini models, infrastructure, and Gemini Enterprise to build and run their platform.

These new and expanding customers represent some of the most exciting names in AI and are demonstrative of the type of successes that startups are having with Google Cloud’s uniquely complete stack for building AI.

To learn more about Google Cloud’s work with leading AI startups, or to get started building with us, visit here.

Changing the game: Using agentic AI to secure infrastructure code

Fri, 18 Sep 2026 16:00:00 +0000

AI is accelerating software development at an unprecedented pace. But as code generation scales, so do the challenges of securing the code, especially emerging AI-based vulnerability exploitations. To meet these challenges, the Google AI and Infrastructure team is transforming how we approach security. In this article, we discuss new AI-native agentic methods that we’ve developed that systematically embed high-precision, pervasive vulnerability scanning and patching directly into Google’s software development lifecycle. By continuously scanning every code change across hundreds of millions of lines of code that we deploy onto our infrastructure, we are preventing hundreds of vulnerabilities per month from ever reaching our code base or production, defending our global network, AI infrastructure and our users.

Solution architecture and implementation

Pervasive pre-submit agentic scanning: security as part of ongoing software development

Traditionally, the technology industry relies on large one-off security scans that are slow and lack sufficient context. As a result, they often find vulnerabilities too late. Our approach instead focuses on pre-submit scanning, where we evaluate each code check-in (across every layer of the stack) in real-time using AI agents. By integrating the pre-submit scan into the tools developers already use, security becomes a continuous routine process, similar to rule checkers, readability reviews or other software development tools. Also, from an AI perspective, scanning each individual code change requires much less context than performing a large one-off scan, significantly improving the scan’s effectiveness.

The importance of localized threat models

For this initiative, we evolved Mantis, our open-source multi-agent review harness, to increase the precision of our security agents by matching them with a cohort of robust localized threat models. Rather than relying on static decoupled documents, the threat models use live codebase metadata. The scanning agent improves its accuracy further using a dependence call graph across packages and libraries to expand and refine its threat model context. Making threat models part of our ongoing vulnerability scanning encourages developers to continuously update threats and dependencies, keeping the models up-to-date. Using localized and precise threat model data translates to dramatic accuracy improvements, bringing our false-positive rates down to 3% in some cases.

Specialized triage agents speed up development

Vulnerability scanning as part of code check-in requires it to respond quickly to the developer or agents generating the code, so as not to impede engineering productivity. To get responses with low latency, we run a two-step validation process. First, we run a quick lightweight scan that validates its findings against a specialized triage agent. This agent programmatically checks the actual structure of the code (using abstract syntax tree parsing, call-graph traversal, and pre-indexed domain safety rules) to prove that the vulnerable path is actually reachable by an attacker. This agent gets over 92% precision and completes its work in less than a minute. Then, a post-submit scan as part of nightly integration testing serves as a second layer of defense, using off-peak cycles to test for vulnerabilities that may have been introduced across multiple changes.

Bug fix agents close the loop

Finding vulnerabilities is only half the battle. The last component of our solution is an automated bug-fix agent that uses the scan results and generated proofs (snippet of code that demonstrates how the vulnerability is exercised) to autonomously construct precise fixes that are consistent with our internal coding standards. The agent submits the fixes for human review as part of the original change request’s review, further reducing the time between detection and resolution.

Learnings and call to action

Embedding continuous scanning directly into the software development lifecycle has been a game changer at Google; its suggestions are widely adopted, and it’s prevented a multitude of vulnerabilities from being introduced into the codebase. But any organization wishing to improve security can adopt a similar AI-native approach, following these principles:

Keep systems separate: To prevent bias, keep the harnesses, rules, and context for each of your development, scanning, triage agents separate. Pair lightweight AI scans with deterministic, structural validation to drive down latency and improve accuracy.
Use context wisely: Feed your agents your existing threat models. Precise context is the answer to reducing false positives, and up-to-date threat models set a high floor on a team's security posture by improving the rate of true positives in presubmit scanning.
Build a good harness: While the choice of the underlying model is important, using a multi-agent harness can have substantial impact, by helping compensate for variability in model choice.
Automate the fix: Use agents to also propose human-in-the-loop fixes, to further reduce time-to-resolution.

If you want to get started on your own AI-native security transformation, Mantis is now available as open source for you to use and benefit from. You can also learn more about the fundamentals of cybersecurity and the other platforms that power this agentic pipeline: Google Cloud, Gemini Enterprise and Gemini models running on Trillium and Ironwood TPUs. And you can get inspiration from how agentic vulnerability scanning and remediation defends Google Cloud customers as an integral part of Google Cloud’s secure software development lifecycle (SDLC) effort.

^{With special recognition to critical team members who made this delivery possible: Stella Voutsina (Lead Program Manager), Yulong Zhang (Senior Staff Security Engineer, Mantis), and Nick Galloway (Staff Security Engineer, Mantis).}

How Orange built FinOps accountability, and why agents are next

Wed, 16 Sep 2026 16:00:00 +0000

At Orange, the leading France-based multinational telecom provider, there are days when engineering teams set aside their delivery backlogs and spend the day cleaning up cloud spend together. There's a leaderboard. There are goodies on the line. Experienced practitioners guide the newcomers, so people learn the work while doing it. By the end of the day, sponsors can see the results.

Orange calls these FinOps Clean Days. Together with gamified hackathons, they've earned the company's 100-plus person FinOps community a Net Promoter Score within the organization that’s above 70.

Those numbers point at something the wider industry is wrestling with. Recent State of FinOps reports identify getting engineers to take action as one of the top challenges organizations face. Moving from awareness to action means finding ways to build FinOps accountability, and to get teams to genuinely care.

That makes FinOps a business change problem. And business change problems have known solutions. We spoke with Camille Marini, the FinOps lead at Orange, to get a deeper understanding of how the company overcame these hurdles to accelerate AI adoption and ROI, and how your organization might follow the same course.

Why the Clean Days work

Orange has held two principles since it set up its FinOps team. First, Cloud FinOps is a shared responsibility, with every stakeholder in a project involved in their own way. And the only path to that shared responsibility runs through communication and a deliberate change effort.

“We insisted on the concept of shared responsibility across the organization for our FinOps practices,” Marini told us. “It’s very similar to how we approach cloud security. We needed to make teams understand that every single stakeholder in a project is involved in FinOps, each in their own way, if we are going to achieve responsible and impactful AI spending and usage.”

Those principles led Orange to create a FinOps Community of Practice, with support from Google Cloud Consulting. The team ran it on standardized communication channels so the methodology reached well beyond the central group, and kept the meetings actionable, sharing optimizations and billing updates so every session provided value.

The Clean Days came from a clear-eyed reading of how agile teams actually operate. In agile environments with deployment running constantly, optimization work rarely wins against the sprint. Delivery priorities, backlogs, and daily operations take the available time first. So Orange created protected time, made it collaborative, and made it fun.

McKinsey's four building blocks of change explain why this approach lands. Any large organizational change, the framework holds, requires action across four areas:

Conviction and understanding: "I know what is expected of me and I agree with it."
Formal mechanisms: "The structures, processes, and systems reinforce the change."
Role modeling: "I see my leaders and colleagues behaving differently."
Talent and skills: "I have the skills and opportunities to behave in a new way."

Map Orange's practice onto those blocks and the pattern is visible. Gamification and rewards give engineers colleagues to emulate: The leaderboard makes different behavior visible, and sponsors see the quick wins for themselves. Experienced practitioners guiding novices builds talent and skills through the community itself. The regular sessions, sharing optimizations and billing updates, build the conviction that comes from knowing where the money goes.

FinOps activities mapped to the four building blocks of change, with the points where AI agents can reinforce them.

What happens beyond 100 people

A community of 100 engaged people is an achievement. But in an organization with thousands of engineers, no central FinOps team can reach everyone directly. The question for leaders is how to extend what a community like Orange's creates — the awareness, the shared ownership, the habit of acting — to people the FinOps team will never meet.

This is where AI agents extend the capabilities of a FinOps team with two core benefits. They take on complex, time-intensive activities that previously needed a human, and they reduce friction around FinOps for individuals across the business.

Getting teams to adopt them takes a strategy aimed at your own organization's pain points, which often come from high cognitive load, unclear accountability, or competing priorities. Start by finding where engagement drops off in your FinOps lifecycle:

An awareness gap: If teams are unsure of their spend impact, an insight agent can push real-time cost data into their daily tools.
A bandwidth gap: If engineers are too busy with backlogs, a remediation agent can identify quick wins and present them as ready-to-merge code changes.
A complexity gap: If reporting feels like a manual chore, an orchestration agent can gather the data and simplify the process.

Start with trust, then add autonomy

The sensible path runs in sequence. Establish the community practice, the way Orange did. Then introduce read-only agents that inform and suggest. Only once those are established across the community should you build agents that execute changes. Direct action carries operational risk, so manage it carefully. It's also where significant wins often sit.

How you build depends on who's building. For teams that want to deploy quickly with minimal code, the Gemini Enterprise App provides a no-code environment for creating agents. For developers who need granular control, the Gemini Enterprise Agent Platform (formerly Vertex AI) offers advanced tools for launching and governing agents built with frameworks like the Agent Development Kit (ADK).

Cloud FinOps is moving beyond centralized reporting toward action that happens where the work does. The organizations getting there start with the culture, then use agents to carry it further than any one team could reach.

Orange's numbers came out of the community work. Building that foundation is the part worth copying first. When you're ready to extend it, Google Cloud Consulting can help you shape the community practice, and the Gemini Enterprise App is a low-lift way to put your first read-only agent in front of your teams.

Cloud CISO Perspectives: How Google monitors AI threats and advances AI defenses

Wed, 16 Sep 2026 16:00:00 +0000

Welcome to the first Cloud CISO Perspectives for September 2026. Today, Sandra Joyce shares the latest details on Google’s visibility into how attackers are using AI, and how we’re using AI to stop them.

As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog. If you’re reading this on the website and you’d like to receive the email version, you can subscribe here.

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‘Spellcheck for cybersecurity’ and beyond: How Google monitors AI threats and advances AI defenses

By Sandra Joyce, VP, Google Threat Intelligence

Sandra Joyce, VP, Google Threat Intelligence

Anyone operating in security knows that speculation is a major liability during periods of technological disruption. While there is plenty of hype and understandable concern around how threats might use and target AI, a CISO’s AI security strategy has to be anchored in ground truth.

Google operates at a rare intersection as both a frontier AI lab and a security company with a frontline view of global incidents. This dual vantage point allows us to understand how AI is built, and exactly how AI is being targeted in the wild. To provide the operational realities that security and business leaders need in the AI era, Google Threat Intelligence Group (GTIG) recently released our latest AI Threat Tracker.

When we strip away the noise and look at the telemetry, the real threat landscape boils down to three structural shifts that CISOs must address:

AI is reshaping how software is built.
AI is expanding the attack surface.
AI is enhancing threat capabilities.

Today, we’re sharing details on Google’s visibility into these three challenges, and our approach for solving them.

Building securely in the AI era

AI has fundamentally altered software development velocity. Across the industry, autonomous agents and AI workflows now push code into production at unprecedented speed. This creates exciting opportunities for innovation, yet CISOs are faced with the difficult task of mitigating enterprise risk while maintaining business momentum.

We’re seeing threat actors turn our greatest engineering shortcut against us by contaminating upstream packages that AI assistants are trained to suggest and trust. GTIG believes that malicious contamination of AI-assisted coding practices has been contributing to the significant growth in large-scale, open-source software supply chain compromises we observed in 2025 and early 2026.

The solution to a machine-speed threat landscape isn't slowing developers down — it’s building security natively into the AI pipeline. Part of this process involves in-editor guardrails for developers that create a real-time 'spellcheck for cybersecurity.'

We’re also monitoring adversaries targeting agents. The financially-motivated threat actor TeamPCP (UNC6780) has implemented more than half a dozen methods to exploit AI tools and open-source software development practices, including hijacking AI toolkits, prompt injection, and blinding AI scanners with toxic prompts to obfuscate malicious payloads.

Just as word processors underline typos without forcing the writer to stop, security controls must sit natively inside the developer’s editor and agentic workflows, instantly flagging poisoned packages, toxic prompts, and misconfigured toolkits.

Crucially, this can’t stop at the editor. Traditional security suffers from context blindness: Code editors can’t see cloud configurations, delivery pipelines miss runtime exposure, and production teams can’t easily patch root-cause blueprints.

Bridging this gap requires an integrated code-to-cloud approach — the exact design principle behind platforms like Wiz Code. The underlying approach is to ensure code is continuously verified against live cloud realities before it ships.

When organizations think about AI-driven code analysis, the default assumption is to pick one frontier model and point it at their repository. However, our research and telemetry show that single-model security creates a dangerous monoculture: No single AI model can discover every vulnerability, and threat actors are already testing inputs that can blind specific LLM safety filters and scanners.

To secure this expanding attack surface, CISOs should avoid the trap of managing AI through disconnected silos... The future of cloud and AI defense needs to be built on a unified and dynamic graph that connects your code, your models, your data lineage, and your runtime identities into a single living map.

To solve this, Google takes a deliberate multi-model approach. By orchestrating several foundation models — including Gemini, commercial, and open-source — we cross-validate findings, strip out false positives, remediate code, and identify complex logic flaws that a single model misses. We’re smarter with more than one “brain.”

Securing AI

Securing the development lifecycle is only half the battle. We also need to prevent adversaries from exploiting AI attack surfaces and weaponizing over-privileged agents. Threat actors are targeting AI workloads with techniques that include:

LLMJacking: Cybercriminals and state-sponsored groups target GPU access to support running their AI models and agentic workflows. In one notable intrusion Mandiant investigated in April, a threat actor gained initial access to a victim’s cloud environment from an exposed personal access token, and used it to deploy unauthorized AI infrastructure and scale high-performance compute resources, leaving the victim to absorb the hardware and platform costs.
Targeting of AI data and access: Cybercriminals now recognize that your custom prompts, agent instructions, and fine-tuned models represent high-value crown jewels. In Q2 2026, Mandiant investigated multiple data theft extortion operations where threat actors stole proprietary AI data, including models, skills, prompts, source code, and related research. Demand is also surging for AI account credentials in underground marketplace forums, with some sellers offering steep discounts for consumer accounts at up to 99% off retail prices.

To secure this expanding attack surface, CISOs should avoid the trap of managing AI through disconnected silos. Don’t treat agent access policies, model inventories (AI-BOMs) and shadow AI as separate challenges because these risks are deeply connected. The future of cloud and AI defense needs to be built on a unified and dynamic graph that connects your code, your models, your data lineage, and your runtime identities into a single living map.

Pioneered by the Wiz Security Graph, this approach serves as the contextual engine for Google AI Threat Defense (AITD) — our broader autonomous security framework that fuses the reasoning power of Gemini and other frontier models, the contextual risk prioritization of Wiz, the code remediation capabilities of CodeMender, and the frontline expertise of Mandiant to stay ahead of AI-driven attacks. Crucially, this context is not siloed; it directly feeds Google Security Operations, ensuring that security operations teams can continuously identify, prioritize, and sever toxic attack paths at machine speed.

Defending against AI threats

Threat actors are rapidly moving beyond simple prompt generation toward fully-automated, multi-agent attack pipelines.

Cyber Defense Summit 2026: Security in the AI era

In one notable intrusion investigated by Mandiant, a financially-motivated actor compromised an organization's cloud infrastructure and deployed an autonomous agent framework. The threat actor used an AI coding chatbot, a prompt, and a set of agent instructions to plan, build, and execute a mass credential harvesting campaign in less than six hours.

We’re also tracking adversaries using AI as an intelligent orchestrator across the entire attack lifecycle. GTIG recently observed a PRC-nexus espionage group experimenting with a tool called CC Switch to cycle across multiple accounts and swap AI models — like Claude, Codex, and Gemini — picking the best model for specific tasks, such as writing exploit scripts and drafting lures. While the underlying hacking tools aren’t new, AI turned what had been a disjointed manual process into a smooth and automated workflow.

To take advantage of your deep context, it’s imperative to shift from manual, human-scale incident response to machine-speed security operations. We can no longer rely on human analysts manually triaging endless backlogs of static alerts.

While these machine-speed attacks sound daunting, defenders actually hold an asymmetric advantage. Even when armed with autonomous AI, an attacker operates from the outside with limited context — probing in the dark, guessing connections, and hoping a compromised credential leads to a useful asset.

Defenders, on the other hand, possess deep context that attackers don’t have. You know your code, cloud configurations, user identities, deployment realities, and internal architecture better than anyone. When you feed this rich, multi-dimensional internal observability into security models, AI defense becomes inherently faster and more accurate than AI offense.

By codifying our frontline threat intelligence directly into these AI models, these autonomous agents can continuously monitor for, investigate, prioritize, and remediate attacks.

How Google is helping defend the ecosystem

As adversaries adopt AI, we have a unique opportunity to disrupt them at the source. As a major security and AI provider, we take this responsibility seriously, using multiple levers to stay ahead.

Disabling malicious infrastructure. If you use Google tools to facilitate an attack, you lose access to those tools. We proactively disable the projects, accounts, and assets of known bad actors.
Hardening our AI models and classifiers. We operate a continuous feedback loop for our AI models. By feeding threat intelligence directly back into product development, our models learn to recognize and refuse malicious requests before an attack can even be generated.
Automating vulnerability hunting and patching also disrupt adversaries. We are moving from manual patching to AI-driven hunting. Tools like CodeMender automatically fix critical vulnerabilities in the code itself.
Developing advanced defenses and threat models. Our teams at Google DeepMind are building specialized defenses for generative AI — deploying active monitoring across our entire ecosystem to identify misuse in real-time.

Securing the AI era can’t be achieved with the disconnected, manual tools of the past, and you can only defend against an AI-powered threat with an AI-powered defense. To tip the scales back in favor of defenders, we must transition to a continuous, machine-speed model of protection — and at Google, we are committed to building that secure future alongside you.

To learn more about our approach to securing the AI era, please check out our new Mandiant AI Risk and Resilience report.

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In case you missed it

Here are the latest updates, products, services, and resources from our security teams so far this month:

A manufacturing blueprint for secure agentic AI: AI and agents have arrived on the factory floor. Today’s CISOs and business leaders must balance innovation with precision, physical safety, and operational resilience. Read more.
Proactive cyber defense for governments and enterprises: Our new Fairwind Program is a limited access program for governments and trusted partners to use our most advanced cyber defense capabilities. Read more.
Getting started with the Mantis harness to find and fix bugs: Mantis is part of how Google finds and fixes vulnerabilities at machine-speed. The open-source AI harness creates a more effective repository analysis. Read more.
Breaking into Google's GFile for $100,000: Learn about how a vulnerability — that was not exploited and has now been patched — could have allowed attackers to chain unauthenticated, undocumented internal APIs with overly-permissive shared file libraries to achieve unrestricted data access across core infrastructure. Read more.
Introducing new session management tools with native, granular controls: New Google Cloud session controls are deeply integrated and a granular feature of Context-Aware Access. Here’s what you need to know. Read more.
How Blackline prevents data exfiltration with VPC Service Controls: We’re excited to share new policy intelligence capabilities in VPC-SC that help drive operational simplicity: Violation analyzer and violation dashboard. Read more.
Introducing Continuous Vulnerability Assessment: You can detect exposure to new vulnerabilities the moment they’re published with Wiz CVA. Read more.
How developers prevent production risk at the source: Fixing security vulnerabilities in code takes seconds, while patching in production creates high operational costs and risk. Discover how empowering developers as your first line of defense eliminates exposure across every phase of your software pipeline. Read more.
Wiz achieves GovRAMP High authorization: Delivering unified cloud security and accelerating secure modernization to protect citizen data and critical infrastructure. Read more.

Please visit the Google Cloud blog for more security stories published this month.

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Threat Intelligence news

AI Threat Tracker: From prompting to autonomy: In the newest Google Threat Intelligence Group (GTIG) report on the adversarial misuse of AI, we’ve observed adversaries transition from basic prompting to agentic AI workflows and AI-enabled automation, including threat actors compromise a cloud resource, then plan, build, and execute an agent-enabled mass credential harvesting campaign in under six hours. Read more.
Financially-motivated threat actor BREEZE COMET targets Brazil: Learn about BREEZE COMET’s tactics and toolkit, and our mitigation recommendations and detections to support organizations in defending against this active and developing threat. Read more.
JFrog Artifactory under attack: Wiz Research has identified active, in-the-wild exploitation of three critical and high-severity vulnerabilities impacting JFrog Artifactory. Attackers are chaining these vulnerabilities to bypass authentication and gain administrative control. Read more.

Please visit the Google Cloud blog for more threat intelligence stories published this month.

Now hear this: Podcasts from Google Cloud

Cloud Security Podcast: Patching browsers with AI, agents, Rust, and your tabs: Jasika Bawa and Doug Turner of Chrome Security explore how Google Chrome now uses AI agents to autonomously identify and patch security vulnerabilities at an unprecedented scale, significantly accelerating the browser's update cadence. Listen here.
Cloud Security Podcast: All about Project Atlas, Wiz's AI vulnerability research: Nir Orfeld, head of vulnerability research, Wiz, discusses how his team uses multi-agent AI systems for discovering high-impact zero-day vulnerabilities in cloud infrastructure. Listen here.
Cloud Security Podcast: How Google eliminates classes of vulnerabilities at scale: How do you build the foundations for a secure Google-scale enterprise that stays secure even if an AI is writing the code and nobody has time to review it? Christoph Kern, principal security engineer, Google, explores what secure-by-design really means in the AI era. Listen here.

To have our Cloud CISO Perspectives post delivered twice a month to your inbox, sign up for our newsletter. We’ll be back in a few weeks with more security-related updates from Google Cloud.

Google is a Leader in the 2026 Gartner® Magic Quadrant™ for Enterprise AI Assistants

Wed, 09 Sep 2026 18:00:00 +0000

We are excited to share that Gartner has named Google a Leader in its inaugural 2026 Magic Quadrant for Enterprise AI Assistants. In this comprehensive evaluation of top enterprise AI assistant vendors, Gartner placed Google in the Leaders quadrant for its evaluation across both Completeness of Vision and Ability to Execute.

Gemini Enterprise helps organizations bring helpful, secure AI directly into the daily work of their employees. It moves teams past basic chat interactions to automating multi-step, end-to-end workflows with AI agents. It connects with the tools and infrastructure companies already use and scales easily and cost-effectively, all with security and governance in place.

We see this recognition from Gartner as validation of our goal: creating a unified platform where everyone — from business users to developers — can work alongside AI agents to accomplish more together.

Our take on Google as a Leader

Amid a complex landscape of standalone AI tools and emerging platforms, Gemini Enterprise emerges as a unified, open agentic platform backed by Google’s full AI stack, with strengths mentioned in the report such as:

Unified “AI front door”: Gemini Enterprise unifies enterprise chat and search, first and third-party agents, a no-code agent designer, Google Workspace integration, and third-party connectors all in one platform, eliminating the need for organizations to piece together disparate AI tools.
Open connectivity: Gemini Enterprise offers extensive connectivity beyond Google's ecosystem — extending to Microsoft 365, other third-party software, and internal enterprise data sources. This open connectivity lets organizations adopt Gemini Enterprise alongside their existing infrastructure without costly system overhauls.
Simple economics: Gemini Enterprise offers a straightforward pricing model, with actions like chat and search included in the base SKU. Organizations can select per-user seat subscriptions, as well as a pay-as-you-go option that lets users run agent workloads without hitting quota limits mid-task.
Built-in governance: Gemini Enterprise provides robust agent governance out-of-the- box at no extra cost, enabling enterprises to seamlessly manage users, agents, and data permissions while curbing security risks and agent sprawl.
Full-stack depth and scale: Google’s vertically-integrated stack provides global infrastructure, custom silicon, world-class models, and a secure, enterprise-ready foundation — all optimized for security, interoperability, and cost.

To read the full report, download it here.

Accelerating our vision with the latest Gemini Enterprise advancements

Over the past months, we have accelerated Gemini Enterprise with significant product advancements:

Tailored industry solutions: We introduced specialized solutions for legal and financial services last month. These tailored solutions bring pre-built agents, domain-specific skills, secure data connectors, and an open partner ecosystem, allowing for rapid deployment. They are also built on top of Gemini Enterprise’s governed control plane so you can create and manage workflows in these highly regulated industries.
Google Antigravity in Gemini Enterprise: With the introduction of AI developer tools in Gemini Enterprise, enterprises can now easily enable agentic dev tools like Antigravity and Android Studio for their developer teams with eligible Gemini Enterprise licenses, and maintain full governance and observability inside the admin console.
FinOps and cost controls: New FinOps and cost-control capabilities were introduced last month, allowing organizations to optimize AI spend through more pricing options, Flexible Savings Plans, and granular spend management.

Gemini Enterprise customers are also seeing the value

Hearing this recognition from Gartner is great, but it's not just them—our customers are seeing this real-world value too, and it's driving a positive impact across their organizations every day.

Check out what our customers are saying about the recent product advancements.

“Deploying Antigravity in Gemini Enterprise allows Accenture to arm our engineers with Google DeepMind’s premier technology on the secure, trusted foundation of Google Cloud. Abstracting away operational complexity ensures our teams don't have to choose between developer speed and enterprise-grade governance — freeing them to deliver high-velocity engineering and transformative value for our clients.” — Chetna Sehgal, Global Practice Lead, Accenture Google Business Group. Learn more here.

“Cleary is committed to embedding AI into our workflows in strategic and competitive ways. Using Google’s Gemini Enterprise, which can slot in seamlessly with other daily work tools, we can unlock greater efficiencies for our teams and help them deliver even higher quality work for our clients.” — Jeff Karpf, Managing Partner, Cleary Gottlieb. Learn more here.

“We’re thrilled to partner with Google Cloud in the early adoption of Gemini Enterprise for Legal. We look forward to integrating Google’s technology to streamline workflow and further support our litigators in shaping outcomes critical to our clients’ futures.” — Joe Petrosinelli, Chairman, Williams & Connolly. Learn more here.

Get started today

Read the report: Download your complimentary copy of the 2026 Gartner Magic Quadrant for Enterprise AI Assistants to explore the full analysis
Explore Gemini Enterprise: Discover how your organization can deploy governed, connected agents across every team at cloud.google.com/gemini-enterprise

How KDDI built Buffmee, a faster, reliable consumer RAG app

Tue, 08 Sep 2026 16:00:00 +0000

When building consumer-facing generative AI applications, balancing high generation quality with fast response times across diverse media types, can be challenging. KDDI, a major telecommunications carrier in Japan, tackled this challenge head-on when they developed Buffmee, their consumer Retrieval-Augmented Generation (RAG) app.

Buffmee is an interactive AI service built on the concept of 'AI that helps you grow.' By grounding responses in over 100 sources — including books, magazines, and web media — it helps users search for information, summarize key points, and explore personalized learning and hobby interests. By citing sources, Buffmee alleviates concerns about information reliability, allowing users to safely deepen their knowledge. To achieve this, KDDI collaborated closely with their development partner KDDI iret, Google Cloud Consulting and our specialized AI engineers.

As part of their app launch, the engineer team needed to ground a massive variety of proprietary content, including books and magazines. However, they struggled with latency issues that prevented them from meeting their target response times, and they needed a reliable way to ensure hallucination-free results.

Buffmee App Description and Images

To meet these performance targets, organizations need a systematic approach to AI evaluation and real-time bottleneck identification. That is why we are sharing the automated evaluation framework and performance optimization techniques that helped KDDI successfully launch their application.

The results were inspiring: KDDI reduced total application response latency by 38%, successfully hitting their target response performance. They also achieved a nearly 18% improvement in TTFT.

"Our vision hinged on a platform where content, once ingested, would instantly function as a working RAG system. Google's careful, hands-on guidance made that a reality — we're sincerely grateful for their support." — Shunya Onoda, AI Product Department, KDDI.

With these performance and accuracy improvements, Buffmee now empowers users to safely explore their favorite media through interactive Q&A and deep-dive analysis, delivering a highly personalized experience while maintaining strict trust and compliance for content providers.

Let’s deep dive into how they achieved these results.

Establish automated evaluation for diverse content

Traditional manual testing requires immense effort and cannot scale to accommodate a large content library. To solve this, the development team designed a systematic AI evaluation process using Gemini Enterprise Agent Platform Evaluation Service.

By implementing automated evaluation frameworks like LLM-as-a-Judge and the Rule of Hundreds, the team replaced labor-intensive manual testing with a data-driven process. They ingested their extensive document corpus, constructed hundreds of automated evaluation tests, and built a comprehensive benchmark dataset to measure the reliability of answers for each use case. As a result, the team improved their groundedness scores by 25%, helping deliver highly accurate and reliable outputs.

KDDI's automated evaluation loop: AI generates questions and scores answers, while humans calibrate thresholds and analyze edge-case failures.

Identify bottlenecks and optimize performance with an agentic loop

To improve response speeds, the team implemented BigQuery Agent Analytics and the Agent Development Kit (ADK) log analysis agent. By analyzing actual production logs, they visualized how skill division and prompt bloat—especially with highly complex, multi-page system prompts — impacted the Time To First Token (TTFT).

The team optimized the system prompt, including the inline integration of skills, and reviewed the sub-agent routing. This allowed them to identify and resolve deep-stack bottlenecks in real time without sacrificing response accuracy.

Four core principles for reliable evaluation

To achieve these results, the team implemented four core technical practices:

Transitioning to binary evaluation: By selectively moving away from ambiguous 1–5 ratings to a binary "pass (1) / fail (0)" system for critical metrics, the team minimized variance and noise, helping improve automation accuracy.
Strategic content sampling: Rather than attempting to evaluate every single document, the team classified their entire corpus along a two-dimensional grid: File Format (Web articles, EPUBs, PDFs, structured data) and Media Composition (Text-heavy, image-heavy, or mixed). By selecting representative samples from each cell of this difficulty grid, they reduced the evaluation workload by 75% while maintaining comprehensive test coverage.
Thresholds grounded in product judgment: Instead of relying solely on default tool parameters, the product owner reviewed randomly sampled answers alongside their automated scores to calibrate and establish what "good enough to ship" actually meant for the user experience.
Modular splitting of massive prompts into ADK Skills: Because massive system prompts exceeding 800 lines can cause LLM attention drift and latency degradation, the team split prompts by function into Agent Development Kit (ADK) Skills, dynamically loading only the required logic to optimize response times.

Get started

Building scalable, reliable generative AI applications requires both automated evaluation and deep performance analytics. To apply these techniques to your own applications:

Measure quality systematically with the Gen AI evaluation service
Structure your agents with Agent Development Kit and apply progressive disclosure deliberately
Ground your agents with Agent Search and inspect your retrieval queries

Spanner migrations: Automating dual-write with Antigravity CLI for minimal disruption

Fri, 04 Sep 2026 16:00:00 +0000

When Google's Finance Engineering team needed to modernize their legacy data layer, they chose Spanner, a globally distributed, strongly consistent, multi-model database with high availability capabilities. But migrating to Spanner without taking production services offline was a daunting engineering challenge: As the internal team responsible for the application, we needed to manually rewrite dual-write logic across dozens of Data Access Objects (DAOs), a process that is slow and prone to human error. Further, doing so without disruption would have required implementing multi-phase dual-write architectures across every DAO in our codebase.

To solve this, we took an alternative approach: We built an automated refactoring pipeline powered by Antigravity CLI in headless mode. This helped us accelerate our migration velocity significantly while maintaining strict data parity in our staging environments as we prepare for production.

The challenge: Anatomy of a dual-write migration

When migrating high-throughput production services where financial accuracy is essential, simple cutover scripts do not work. You must verify that both the legacy datastore and Spanner receive identical writes simultaneously until all the historical data backfills and verifications are complete.

We structured our migration across three distinct phases:

Historical backfill: Copying existing historical records to Spanner while maintaining referential integrity.
Dual-write / dual-read implementation: Modifying every DAO to write mutations to both the primary store and Cloud Spanner in parallel during the migration window.
Automated API verification and parity checking: Intercepting RPC traffic and verifying end-to-end that every write lands with byte-for-byte equivalence across both stores.

The architectural pattern is clean, but at our scale, we began to encounter friction. That’s because each DAO requires:

A dedicated MutationConverter class mapping complex domain models to Spanner schema columns
Dual-write branch handling and rollback or error-reporting logic
A suite of unit tests verifying both primary and Spanner writes using fake time sources and test doubles (FakeTimeSource)

Performing these identical, high-precision code changes across 30+ DAOs by hand would have taken months of engineering time.

The solution: Standardized mutation converter patterns

To verify that our automation pipeline could reliably generate clean code, we first standardized our DAO refactoring pattern around a decoupled MutationConverter interface.

Instead of embedding raw Spanner table names and column assignments directly inside core DAO business logic, we isolate Spanner schema translation into dedicated converter units:

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By establishing a rigid, deterministic contract between the DAO and the Spanner SDK (spanner.Mutation), we created an exact target specification that an AI coding agent could reason about and generate reliably.

Why use Antigravity CLI in headless mode?

Interactive AI chat interfaces in IDEs work well for exploratory coding, but they are poorly suited for systematic, multi-file code updates across an entire codebase. When you need to apply repeatable refactoring to dozens of targets without missing edge cases, you need automated workflows.

We addressed this by building an orchestration script (migration_ui.py) that runs Antigravity CLI in headless mode (-p).

Headless mode lets Antigravity run directly inside shell scripts, continuous integration pipelines, and background automation jobs without requiring manual terminal prompts. This approach helped us scale our work in three key ways:

Deterministic prompt architectures: We treated our prompts as version-controlled engineering artifacts. We codified precise rules handling common Spanner edge cases — such as timestamp serialization, nullability conversions, mutation ambiguity, and FakeTimeSource test injection — directly into reusable prompt templates.
Batch execution and automated verification: Our orchestration script takes a target DAO name as input, retrieves the existing single-write source code and schema, and feeds it to headless Antigravity alongside our structural conventions. Antigravity generates the new converter, the refactored dual-write DAO, and corresponding unit tests. The script then runs blaze test. If a linter error or test assertion fails, the error log feeds directly back into Antigravity for self-correction.
Overnight execution at scale: Because the loop runs unattended, engineers can queue up 10 DAOs at the end of the day. By morning, the pipeline generates, tests, and validates 10 clean changelists ready for human code review.

Results and key takeaways for cloud engineers

Combining Spanner's distributed database primitives with Antigravity CLI's headless automation produced clear benefits across our engineering organization:

Significant reduction in migration effort: DAO dual-write migrations that previously required extensive manual coding and testing were completed and reviewed in a fraction of the time
Highly reliable data migration: Because every generated DAO adhered to the exact same tested MutationConverter pattern and underwent automated unit testing against Spanner test doubles, we sustained high data fidelity during our extensive migration testing.
Focus on higher-value engineering: Engineers avoided repetitive boilerplate refactoring, giving them time to focus on data modeling, architectural resilience, and performance optimization.

Three tips for your next database migration

Decouple schema translation first: Before writing migration scripts, define a strict interface (like our MutationConverter) that isolates your new cloud database SDK requirements from your existing business logic. AI agents work best when given clear, bounded design patterns.
Move from interactive chat to headless automation: When executing repetitive refactoring across more than three or four files, invest in scripted, headless workflows. Treating prompt inputs and test verifications as automated build steps help maintain quality and consistency.
Let the build system act as your guardrail: Connect your AI generation loop directly to your build and test harness (bazel test or go test). This lets the model fix compile and assertion errors before a developer reviews the code.

Get started

Whether you’re migrating financial systems or building cloud-native applications from scratch, Spanner and Antigravity provide a foundation for scalable software development.

Explore Cloud Spanner: Learn more about Spanner's distributed architecture Google Cloud Spanner documentation.
Discover Gemini for Developers: See how AI-assisted coding and headless CLI automation can assist your engineering workflows at Google Cloud AI for Developers.

Getting started with Mantis, our open-source bug finding-and-fixing harness

Wed, 02 Sep 2026 16:00:00 +0000

AI models have clearly proven their ability to discover and exploit vulnerabilities without much, if any, human assistance. To help defenders gain the advantage with AI, we built the Mantis harness to automate the discovery, triage, reproduction, and patching of software vulnerabilities.

Available to all as an open-source framework, Mantis is part of Google’s internal approach to find and fix vulnerabilities at machine-speed. It creates a more effective scalable, context-aware repository analysis.

While sloppiness in AI code scanning frequently leads to hallucinated bugs and weak true-positive rates under 7%, we designed Mantis to be effective by combining industry-standard agentic techniques like critic and review agents with sandboxed reproduction of vulnerabilities for grounding.

As we detailed in June, it examines the history of the repository to learn from past security fixes and automatically builds up architectural and threat model documentation, even if these are not provided.

It constructs a hierarchical security summary tree, condensing individual files into directory and root-level summaries. This technique reduced token overhead by over 85%, while preserving critical structural context across massive repositories.

Mantis distills decades of cybersecurity expertise across a wide spectrum of codebases, and is available on GitHub. Here’s how you can get started using Mantis.

First, clone the Mantis repo locally using:

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Second, open your favorite coding agent and use the prompt, “I would like to use Mantis framework in path/to/mantis to review my code in path/to/your/code, can you help me get started?”

Internally at Google, this exact prompt has been used to find real vulnerabilities across our many code repositories. As part of the Mantis repository on GitHub, we’ve included sample sandboxing options. You can also implement your own sandbox to match your own workflow.

Mantis is intended to be an easy place to start with vulnerability discovery, true positive filtering, and patching. Once you've got a handle on AI-discovered vulnerabilities, you can use the new mantis-advise skill to make use of the accumulated knowledge and get your coding agents to write secure code the first time.

To get the most out of AI-driven vulnerability discovery and modernize your development practices, we strongly recommend two essential practices:

Feed your tools the right context: While Mantis automatically analyzes commit history and code to build documentation for itself, human-curated knowledge often can dramatically improve the quality of your results. For example, if you would never waste time fixing bugs where the user can crash their own program, this is critical information for a scanning pipeline to ensure that those types of bugs are never surfaced.
Build a cyber sandbox with vulnerability acceptance criteria. Safe, sandboxed environments where you can reproduce vulnerabilities with clear vulnerability-reproduction criteria will give you better results for surfacing only the things you need to know and also for ensuring that your fixes are correct.

You can learn more about Mantis here.

What Google Cloud announced in AI this month

Tue, 01 Sep 2026 16:00:00 +0000

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

This month, we focused on making AI highly practical for your business, including its associated costs. This meant tailoring our models for specialized industries – starting with Financial Services and Legal – and helping you keep your budgets under control. Let’s dive in!

Top announcements

FinOps for the AI era: New flexible billing and cost controls for agents: To help you get better return on AI, we announced expanded billing flexibility and new cost management tools for agent workloads across Gemini Enterprise and developer tools like Google Antigravity in Gemini Enterprise and Android Studio.
Gemini Enterprise for Financial Services: We’re bringing Google’s agentic AI directly into the workflows of capital markets and corporate banking.
Gemini Enterprise for Legal: Gemini Enterprise for Legal provides an integrated, fully governed environment configured for rapid deployment across firms and corporate legal departments.
Expanding Google Antigravity for enterprise customers: Antigravity is available now as part of eligible Gemini Enterprise app subscriptions, including out-of-the-box administrative and spend controls.

Thought leadership (editor’s pick):

Tokenomics: Why smart teams spend more on AI, on purpose: Hear from Eric Lam, Head of Value, Delta, Google Cloud Consulting about how disciplined organizations are moving past reactive sticker shock over AI bills and embracing tokenomics.
Meet the researcher fighting AI hallucinations at Google Cloud: Cyrus is a senior research scientist at Google. Lately, his focus has shifted to large language models, specifically a persistent issue known as hallucination, which is when an artificial intelligence model lacks the correct facts but confidently invents an answer anyway.
What sports cars can teach us about optimizing AI spend: More tokens doesn't always mean better AI. Read our conversation with Mike Clark, Director of Product Management for Gemini Enterprise Agent Platform, on how to balance horsepower with efficiency and get the highest return out of every dollar you spend on AI.

News you can use:

Looking for a steer on your foundation or inspiration for your next project? Take a look at some of our favorite how-to guides from August:

10 questions every startup should answer before moving to production with their AI prototype: These ten are scoped to the prototype-to-production transition itself. Each question ends with a short, runnable snippet you can copy into your own project today. Adjacent decisions that matter just as much but aren't specific to that move, your data layer and RAG architecture, CI/CD, network design, are deliberately out of frame here.
Your chance to start building AI agents from the absolute basics: Agent Valley is a free, 5-week live learning series designed to take you from scratch to building your very own hands-on agent systems. And instead of staring at boring terminal lines, you’ll be building and playing inside a tiny, low-poly virtual world!

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

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July

Since launching the Gemini Enterprise Agent Platform a few months ago, we’ve watched businesses move from basic experiments to serious, production-grade builds. We want to make it even easier — and more secure — for you to scale those systems.

Along with a batch of new platform updates, this month we’ve put together 13 practical demos and 20 diagnostic questions to help your engineering teams align on a strong architectural blueprint. Let’s dive in!

Top announcements

What’s new in Gemini Enterprise Agent Platform: In this helpful recap, we announced some of our most popular capabilities are available for everyone, from Agent Runtime to Agent Identity.
Now in preview: Find and fix software vulnerabilities with CodeMender: As adversarial AI threats accelerate attacks on code, security teams must counter them with machine-speed defenses that can automate code remediation and fight AI with AI. You can learn more about CodeMender and review the documentation here.
Solve harder problems with AlphaEvolve, now available to everyone on Google Cloud: AlphaEvolve is a code optimization and discovery agent built on top of Gemini that helps solve the hardest algorithmic problems and achieve breakthroughs for your business and research.

Thought leadership (editor’s pick):

If automation requires delegation, then delegation requires trust. But letting an AI agent run on its own is a big leap for any business. While the productivity gains are clear, the fear of losing control is very real. This month, we sat down with our experts to discuss how leaders can navigate this shift by focusing on transparency, predictability, and setting clear boundaries for how agents handle weird data exceptions.

Here’s our picks for the month to learn more.

How leaders can scale AI by trading control for trust (Q&A): We sat down with Michael Gerstenhaber, VP of Product Management for Gemini Enterprise, to discuss why the future of AI is about defining safe boundaries.
What makes an AI agent trustworthy: Context is fast becoming one of the most valuable assets a company owns. Prajakta Damle, Senior Director, Product Management, shares what it takes to get trustworthy AI right.

News you can use:

What if you’re looking for a steer on your basic foundation, inspiration for recipes, or some inspiration? Take a look at some of our favorite how-to guides from July:

Automate your agent development lifecycle using any coding agent: Stuck prototyping? With Agents CLI skills, you can go through the different phases of the entire agent lifecycle without ever leaving your coding agent.
Why AI apps fail in production (And how Google solved it): Only 5% of AI prototypes make it to production, and the other 95% fall into the validation abyss. How can you move confidently into production?
13 hands-on demos to build on Gemini Enterprise Agent Platform: Not sure where to start with Agent Platform? Here’s 13 ways you can stir up some creativity.

June

Our main focus in June was helping your teams build, scale, and secure AI. Today, we’re sharing a fresh roundup of updates designed to help you run smarter, more secure applications while keeping everything under your control.

We even shared a cool virtual shopping demo at Cannes to show how retailers can make product discovery more exciting. Let’s dive in!

Top announcements

Introducing the Open Knowledge Format: We introduced the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. This is a vendor-neutral, agent- and human-friendly standard for representing the metadata, context, and curated knowledge that modern AI systems need.
Collaboration with Apple on its expanded Private Cloud Compute (PCC) systems: Our collaboration with Apple is built on a foundation of deep commitment to privacy that leverages Google Cloud's security and privacy technologies. At the heart of this collaboration is our Confidential Computing portfolio and our Titanium security architecture.
Claude Fable 5: Available on Google Cloud: Claude Fable 5, Anthropic’s latest frontier model, is now generally available on Google Cloud. This launch is the latest proof point of our ongoing commitment to bring the industry's latest models straight to our Agent Platform.
Cloud Atelier: How Gemini Enterprise is helping restyle the retail playbook: This year at Cannes, we showcased Cloud Atelier — a destination-based, virtual shopping experience that highlights how retail brands can turn this classic dilemma into an exciting moment of product discovery.

Thought leadership (editor’s pick):

How Google Cloud Security uses AI internally: To counter machine-speed, AI-driven threats, we’ve worked hard to transition Google Cloud’s security posture to an autonomous, proactive model. By embedding specialized AI agents directly into our software development lifecycle (SDLC), we’ve created automated guardrails that protect code at a scale and speed unreachable by human teams — and we’re taking steps to make those same guardrails widely available.
The 4 lessons that guided AI Threat Defense: We introduced Chris Betz as the new CISO of Google Cloud. For his first Cloud CISO Perspectives, Chris shares four key lessons we learned about using AI to the defender’s advantage while building AI Threat Defense.

News you can use:

5 lessons from red teaming AI applications: To help you build AI securely, Mandiant has developed a proactive, risk-based approach centered on the Good AI Assessment (GAIA) Top 10, outlined in our new report, Secure Development of Generative AI Applications: A Proactive Approach.
How to unlock true ROI in software development – a deep dive into the latest DORA research: To help you evaluate the costs and business benefits of AI, we recently shared the DORA: ROI of AI-assisted software development report. This research offers a practical approach to help your team work through early adoption challenges, align engineering plans, and drive business growth.
Agent Factory Recap: 100X engineering with AI agents in Google Antigravity 2.0: In this episode of the Agent Factory, Shir Meir Lador, Head of AI Engineering, Google Cloud Developer Relations, sat down with Rody Davis, one of Google’s top agentic engineers. They dive into the massive shift from traditional IDEs to agent-first platforms, the reality of code reviews in an AI-driven world, and how to use "skills" to perform at a 100X level.

May

We’ve had a busy month! Between announcing Gemini Spark and Gemini 3.5 at Google I/O – and unveiling Google AI Threat Defense, our latest AI-powered cybersecurity solution, we had a lot to share with Google Cloud customers. Keeping up with the latest news takes time, so we gathered the most important announcements, thought leadership, and technical guides in one place to help you quickly catch up.

To learn more about our I/O announcements, here’s everything you need to know for Google Cloud customers, and top news for startups.

Top announcements

Introducing Google AI Threat Defense to help you outpace the adversary: Google Cloud is introducing a comprehensive AI-powered cybersecurity solution — Google AI Threat Defense — an always-on autonomous security platform. Learn more here.

Gemini 3.5: Our latest family of models combines frontier intelligence with action – starting with Gemini 3.5 Flash.
Gemini Omni: Our new model is a leap forward in world understanding, multimodality, and editing, letting you generate any output from any input, starting with video.
Google Antigravity: Google Antigravity’s expanded capabilities and new integration with Agent Platform bring agentic development to your entire organization.
Gemini Spark: For Gemini Enterprise and Workspace customers, Gemini Spark is your 24/7 personal AI agent that helps you work more efficiently by autonomously taking action on your behalf, under your direction.
Google Workspace: Google Pics, our new image generation and editing tool, and new voice features in Gmail, Docs and Keep, help reimagine how you work.
Managed Agents API on Agent Platform: Allows developers to build and run custom agents inside secure, Google-hosted environments that seamlessly integrate with Agent Platform.
CodeMender: A powerful AI security agent provided through Agent Platform, CodeMender can help find and fix vulnerabilities in your code.

Nano Banana 2 and Nano Banana Pro are generally available: Available today via Gemini Enterprise Agent Platform, organizations are already putting the models to work. Learn more here.

Thought leadership (editor’s pick):

Cloud CISO Perspectives: How Google + Wiz changes multicloud strategy for CISOs: Vinod D’Souza, director, Office of the CISO, shares highlights from his RSA Conference fireside chat with Anthony Belfiore, chief strategy officer, Wiz. While threat actors have seen gains from the adversarial misuse of AI, Google and Wiz are tackling these challenges head-on by combining Wiz's deep cloud telemetry with Google's world-class AI and quantum research to help CISOs and their organizations meet the needs of the agentic enterprise era. Read more here.

News you can use:

What Google I/O '26 means for developing agents on Google Cloud: Dig deep into how Gemini Enterprise Agent Platform and the new developer tools shared at I/O fit together, unpack the spectrum of choice for building, and share what we’d actually try first. Learn more here.

Five must-have guides to move agents into production with Gemini Enterprise Agent Platform: Here is a look back at our five-part series covering the architecture patterns and best practices you need to move your agents into production. Learn more here.
How to build an AI-ready security program for the public sector: From industrial control systems to decades-old municipal databases, here’s our CISO guidance to prep AI-ready security programs for the public sector. Learn more here.

April

We hosted Google Cloud Next in Las Vegas on April 22, announcing incredible innovations from Gemini Enterprise Agent Platform to our eight-generation TPUs. We also expanded the Gemini Enterprise app in collaborative ways – now, with new features like Projects, you can work side-by-side with your agents and colleagues.

If you missed the livestream, take a look at our Day 1 recap. It’s been incredible to see how customers have been applying AI in thousands of ways — so far, we’ve counted more than 1,300 examples.

Top announcements

1. Gemini Enterprise Agent Platform: Our new, comprehensive platform to build, scale, govern, and optimize agents. Moving forward, all Vertex AI services and roadmap evolutions will be delivered exclusively through the Agent Platform, rather than as a standalone service, to power the next generation of agent development.

The platform is designed around four core pillars — build, scale, govern, and optimize — that allow teams to collaborate seamlessly. Learn more about Agent Platform here.

2. Gemini Enterprise app has all the key components to let teams discover, create, share, and run AI agents in a single environment. At Next ‘26, we introduced several new capabilities in the Gemini Enterprise app:

Agent Designer uses the same no-code agent designer experience of Agent Platform and lets employees build sophisticated schedule- and trigger-based agents using any enterprise connector. It gives you a virtual flowchart of your agent, allowing you to inspect, test, and approve workflows, ensuring total transparency for executing critical business processes.
Long-running agents are designed to execute complex business processes. They can work autonomously in secure cloud sandboxes, giving agents the ability to orchestrate business logic, write code to build custom tools, and complete multi-step work like reconciliation activities or sales prospect sequencing — without needing constant prompting.
Inbox in Gemini Enterprise provides a central location to monitor, guide, and help manage all of your agent activity, including your long-running agents. Notifications are intuitively categorized into actionable groups like "Needs your input," "Errors," and "Completed.”
Projects create a dedicated space where the agent’s memory is confined to the files and conversations your team adds. By connecting it to data sources including Google Drive, NotebookLM, and Google Group Chats, the agent becomes an expert on a specific topic and can provide team members daily briefings or status updates without digging through months of documents.
Skills create simple shortcuts using an “@” mention for repetitive tasks such as applying brand guidelines, formatting a report, and accessing specific data.
Canvas gives our customers an interactive editor directly within Gemini Enterprise. It allows teams to easily create and edit Docs and Slides, and even export to Microsoft 365 files, within the same experience.
Agent Gallery provides access to third-party agents from partners like Adobe, Atlassian, Lovable, and ServiceNow, and is adding more third-party connectors for Asana, Mailchimp, Workday, and more. These integrations enable your agents to retrieve data and execute tasks with your systems-of-record.

3. AI Hypercomputer: Designed specifically for demanding AI workloads, our AI Hypercomputer is an advanced, purpose-built architecture that unites performance-optimized hardware for compute, storage, networking, open software and machine learning frameworks — as well as flexible consumption models — into a single, integrated system. We are announcing innovations at every layer of the AI Hypercomputer:

TPU 8t, optimized for training, uses breakthrough Inter-Chip Interconnect (ICI) technology to scale up to 9,600 TPUs and 2 PB of shared, high-bandwidth memory in a single superpod. It achieves 3x the processing power of Ironwood and delivers up to 2x more performance/Watt.
TPU 8i, optimized for inference, uses our new Boardfly topology to directly connect 1,152 TPUs in a single pod. It features 3x more on-chip SRAM compared to previous versions to host larger KV caches entirely on-silicon and integrates a specialized Collectives Acceleration Engine. Taken together, TPU 8i delivers 80% better performance per dollar for inference than the prior generation, enabling millions of concurrent agents to run cost-effectively.

4. The Agentic Data Cloud: A new data architecture built for the speed and scale of agentic AI. The Agentic Data Cloud delivers an AI-native architecture, allowing agents to perceive, reason, and act on your behalf in real-time, including:

Cross-Cloud Lakehouse, standardized on Apache Iceberg, is our Lakehouse that enables you to leave your data in AWS or Azure (coming later this year) while querying it instantly — without the friction of vendor lock-in or the cost of data movement
Knowledge Catalog constructs a unified, dynamic context graph of your entire business enabling you to ground agents in all of your business data and semantics. With Smart Storage and the Object Context API, files in Google Cloud Storage are instantly tagged and enriched with metadata before an agent touches them. Then our Knowledge Engine uses Gemini to autonomously tag, define logic and instantly map complex relationships across your entire enterprise, providing the semantic definition your agents have been missing.

5. Protecting the agentic enterprise: Security built for the AI era. Our full-stack AI approach, from the chips to the models, gives you a competitive advantage with better integration and velocity to help protect customers. Not only can Google action insights from the world’s largest threat observatory and Mandiant frontline experts, but we also bring cutting-edge insights and breakthroughs from Google DeepMind, to help make your platforms more secure.

Agentic defense: Three new agents in Google Security Operations can help hunt threats, engineer detections, and provide context on third parties. You can build your own security agents with remote Google Cloud model context protocol (MCP) server support for Google Security Operations, now generally available. You can also access the MCP server client directly from the Google Security Operations chat interface, available in preview.
Protecting AI and cloud apps across any infrastructure with Wiz: Newly expanded AI coverage helps build secure agents across clouds and AI studios. New AI-Bill of Materials in development tools can help secure AI-generated code and mitigate the risk of shadow AI. Learn more.
Securing agents and the agentic web: Model Armor can integrate with Agent Gateway, and new Agent Identities provide more layers of defense against shadow AI. Google Cloud Fraud Defense, the next evolution of reCAPTCHA, offers agent-specific capabilities that can help secure the agentic web as well as the entire user and customer journey.
Trusted Cloud: We’re simplifying permissions with modern IAM, and advancing Google Cloud security with new capabilities in Security Command Center plus new innovations in data and network security.
New partner-supported workflows for Google Security Operations: This new robust cohort of partner integrations includes partners developing their own agentic security operations centers (SOCs).

You can catch up on all our security announcements from Next ‘26 here.

News you can use

Guide to prompting Gemini 3.1 Flash TTS (text-to-speech): The new TTS model introduces a high level of controllability by allowing you to steer the delivery using more than 200 audio tags. We'll share how to get strong results from the model, whether you are building accessible gaming soundtracks, banking systems, or audiobooks. Learn more about the model here.
Ultimate prompting guide for Lyria 3 models: Lyria 3, Google's family of music-generation models, is designed to give you granular control over vocals, instrumentation, and arrangement. So we spent weeks testing against every musical genre and use case we could imagine. We put together this guide to share exactly what we learned and how you can get the best results.
How to find the sweet spot between cost and performance: This guide will walk you through Google Cloud's flexible gen AI infrastructure options, showing you how to find that sweet spot on the efficient frontier between cost and performance. We'll start with the foundational pay-as-you-go (PayGo) models and then explore how to layer on more specialized options to build a robust and cost-effective gen AI strategy.
Essential AI and cloud security now on by default: To support the next generation of AI innovators, we are offering on by default essential AI security and cloud security in Security Command Center Standard.
Securing AI inference on GKE with Model Armor: Here’s how to secure AI inference on Google Kubernetes Engine with Model Armor and high-performance storage.
Cloud CISO Perspectives: AI, security, and the workforce of the future: You can’t bring traditional security to an AI fight, so how do we defend against AI-powered attacks, boost defenders with AI, and secure AI use? Drop in on this RSA Conference fireside chat between Francis deSouza, Google Cloud COO and President, Security Products, and Nick Godfrey, senior director, Office of the CISO.

March

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

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

Top hits:

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

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

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

News you can use:

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

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

February

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

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

Top hits

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

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

News you can use

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

Janurary

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

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

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

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

Let’s dive in.

Top hits

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

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

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

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

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

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

News you can use

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

What’s new in AI infrastructure and orchestration in August

Mon, 31 Aug 2026 16:00:00 +0000

Welcome back to What’s new in AI infrastructure and orchestration this month, a collection of product updates, how-tos, customer stories, research and other resources about all the AI compute, networks, storage, frameworks, and orchestration software that you can find at Google Cloud. To be honest, we thought August would be a slow month, but nothing could be further from the truth. Read on and you’ll see what we mean.

August 2026

Product, technology, and tools updates

Product update: Filestore, Google Cloud’s first-party, secure, scalable NFS file service, has emerged as a popular storage platform for AI and agentic workflows, and now, it’s even better suited to the task, with a new backend storage layer built directly on Colossus, Google’s foundational distributed storage system. This new backend lets you provision IOPS independently from storage capacity, and is deeply integrated with GKE. In AI environments, this can help you service so-called agentic swarms — large groups of agents that need to read and write to a common dataset — without a drop off in performance. For more, check out the blog post.
New feature: gVisor sandboxes are now available in distributed Ray clusters on GKE. In partnership with Anyscale, we introduced an experimental library for Ray that brings gVisor, Google’s open-source application kernel, directly into distributed Ray clusters. gVisor provides lightweight environments with stronger isolation than ordinary containers, plus fast startup times and low memory overhead. To try out these sandboxing capabilities on GKE, head over to the Ray sandboxing User Guide.
Product update: Looking for high-performance, easy-to-use infrastructure on which to run a personal AI agent, but don’t want to spend a lot of money? New Cloud Run instances are dedicated, singleton compute runtimes on Cloud Run that won’t shut down when the agent is idle. Better yet, the cost to run a Cloud Run instance with 1 vCPU and 1 GiB of memory continuously for 30 days is just $5.70.

Practitioner guides, documentation and how-tos

How-to guide: Big news in Model Context Protocol (MCP) land: As of the 2026-07-28 specification, the protocol core is “completely stateless. The handshake is gone. The initialize / initialized handshake (SEP-2575) and the logical Mcp-Session-Id header (SEP-2567) have been removed entirely. Instead, every request is now self-describing and independent.” Whoa. Learn more about the changes that the latest MCP specification brings, and more importantly, how to implement them, in this Google Developers blog.
Guide: Real-time AI systems make a mess of traditional network load balancing techniques. “Instead of handling isolated requests, the backend has to manage a continuous, live bidirectional stream. You’re dealing with a constant stream of audio chunks, transcripts, model outputs, and synthesized speech flowing back and forth simultaneously.” Things only get worse when the user gets involved. “The server has to immediately halt its current speech generation, pivot to update the context, maybe trigger a new tool, and start drafting a different response; this must be done without dropping the connection.” For a new approach to managing load in the AI era, read Scaling real-time AI agents with session-aware load balancing.
How-to: Learn how to build an elastic, scalable LLM inference platform on GKE, even with a mix of different GPU accelerators. The proposed architecture combines Capacity Advisor and Compute Advisor, plus high-performance storage like RunAI:model streamer or GCPFuse with parallel downloads. Get all the details here.
Documentation: The thing about hosts with GPUs or TPUs is that you can’t use live migration to update them, setting up a maintenance challenge. In this new docs page, learn how to update accelerator-equipped hosts according to your tolerance for downtime for your training and inference workloads.
Documentation: Advanced Compute Images, or ACIs, are standardized image stacks for AI/ML and HPC infrastructure, so you don’t need to manually build your own custom images. In this new docs page, learn how to create an ACI image using the Google Cloud CLI, console, or SchedMD's Slurm workload manager.
Guide: AI workloads are notoriously difficult to architect, resource-intensive, and bursty, which can also lead to scaling bottlenecks and large pools of underutilized — or misutilized — compute resources. A new blog outlines the three main ways to achieve dynamic capacity management in Google Cloud: 1) scheduling capacity for planned downtime; 2) maintaining automated fallback capacity for unplanned downtime; and 3) relying on GKE’s core orchestration capabilities to automate resource allocation.

Customer and partner updates

Business orchestration software provider UiPath was dealing with spiky workloads, and wanted more predictable costs. To get there, it re-architected its infrastructure, moving from isolated clusters to a shared Google Cloud GPU fleet that included both A3 VM instances (NVIDIA H100 GPUs) for training with G4 VM instances (NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs) for inference. You can read more about their architecture here.
Mirendil, an frontier AI lab focused on accelerating AI development, announced that it is using AI Hypercomputer with both TPUs and NVIDIA GPUs to support its model pre-training and post-training applications.
Replenit, a retail CRM provider, built its AI decision engine in Google Cloud, using BigQuery, Gemini Enterprise Agent Platform, and open-source Gemma models that it runs on Cloud TPUs. This latter combination provided Replenit with 90% lower pipeline costs than their previous cloud provider, the company reports. Read the full case study for more.
Malachyte architected its AI-powered e-commerce recommendation platform on top of Bigtable, Managed Service for Apache Kafka, Pub/Sub, Compute Engine, and last but not least, GKE. See how it all comes together in this blog.

July 2026

Product, technology, and tools updates

Product update: Google Cloud Managed Lustre is now GA, and available in four distinct performance tiers that deliver throughput ranging from 125 MB/s, 250 MB/s, 500 MB/s, to 1000 MB/s per TiB of capacity — with the ability to scale up to 8 PB of storage capacity. The Managed Lustre solution is powered by DDN’s EXAScaler, combining DDN's decades of leadership in high-performance storage with Google Cloud's expertise in cloud infrastructure.
Product update: C4N network and storage optimized VMs are now GA. C4N is our first network- and block-storage-optimized VM series built to eliminate data-transfer bottlenecks. Powered by 5th Gen Intel Xeon Scalable processors and built on Google's Titanium offloading hardware, it achieves 400 Gbps network bandwidth, 95 million packets per second (MPPS), and up to 25 GiB/s of block storage throughput when paired with Hyperdisk Extreme.
New feature: GKE Dataplane V2 up to 15K Nodes with Network Policies (GA). This capability enables standard GKE clusters to scale up to 15,000 nodes while maintaining full active Network Policy enforcement, supporting the massive infrastructure needs of large enterprise and AI/ML customers.
New feature: Co-operative time-slicing in llm-d. If you’re running reinforcement learning (RL) workloads, you can now interleave independent RL jobs onto shared physical hardware, increasing aggregate accelerator duty cycles from a ~40% baseline up to 70% without impacting model convergence or accuracy.
New AI security tool: Looking to secure your AI supply chain on GKE, deploy AI workloads safely, and cut down on shadow AI? We open-sourced k8s-aibom, a lightweight, unprivileged Kubernetes controller that continuously monitors container clusters to automatically detect running AI runtimes (like vLLM and Triton) and generate standard CycloneDX Machine Learning Bill of Materials (ML-BOMs). Check out the k8s-aibom project and get involved.

Practitioner guides and how-tos

How-to guide: On July 27, Google announced Day 0 support for Moonshot AI’s Kimi K3 2.8-trillion-parameter open-weight model, the day weights were released. Whichever your preferred deployment path — via Model Garden, custom orchestration, or GKE with llm-d recipes — this guide offers detailed step-by-step instructions to help you evaluate and pilot Kimi K3 in Google Cloud.
How-to guide: Google Kubernetes Engine (GKE) managed DRANET supports both GPUs and TPUs. There are several configurations to use this implementation, including standard cluster (where you have full control) and autopilot cluster (where Google does the heavy configs for you). Take a deeper dive in the hands-on lab, GKE Autopilot clusters with TPUs, GKE managed DRANET and Gemma 4.
How-to guide: Learn to run Ray on TPUs, not GPUs. In Part 1 of this two-part series, we discuss TPU slices (hint: Ray thinks of them as just another accelerator on which to schedule), then walk through Ray’s various AI libraries (Part 2).
How-to guide: Evaluate TPUs for sample workloads using a new microbenchmark suite that helps you accurately assess whether a device is achieving its theoretical performance specifications, and to identify specific performance gaps or architecture-specific bottlenecks. Dive in here.
How-to guide: Scale your agents without killing your budget. Learn how GKE orchestration can help you safely pack more agents onto a fixed compute footprint with GKE Agent Sandbox and Pod snapshots. Whether your goal is performance or cost optimization, we teach you how to turn the right dials for optimal agent efficiency.
Technical blueprint: Inside the optimization of Mistral 3 large inference on Ironwood. This blog outlines how one Google team optimized Mistral 3 large MoE model inference on Google’s Ironwood (TPU v7x), achieving a 1.5x performance gain. They did so with hybrid sharding, replacing linear VPU summations with tree reductions, optimizing GMM/MLA kernels, and adopting asynchronous scheduling. As a result, they boosted throughput by up to 48% while maintaining benchmark accuracy neutrality. Read the full blog here.

Research, reports and deep-dives

Report: Google was named a Leader in the inaugural GartnerⓇ Magic Quadrant™ for AI Infrastructure, positioned highest for ‘Ability to Execute’ and furthest for ‘Completeness of Vision’. Gartner called out Google’s proprietary scalable compute, integrated AI Hypercomputer architecture, and the scale of our AI compute capacity as key strengths. Download a copy here.
Report: We recently surveyed more than 1,400 senior IT leaders for our State of AI Infrastructure report, and a resounding pattern emerged: The gap between AI ambition and infrastructure reality is widening. In fact, 83% of organizations say they require infrastructure upgrades to support production-grade agentic AI. Read the accompanying blog to understand how adapting your infrastructure to meet the demands that agentic applications place on your systems will help you move from pilot to production.

June 2026

Product, technology and tool updates

Product update: Protecting sensitive data used with AI is a critical part of advanced and secure cloud infrastructure. Confidential Computing cryptographically protects data in use in hardware-based Trusted Execution Environments (TEEs) with verifiable data integrity, and is now available on the accelerator-optimized G4 machine series, featuring NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. Get started with Confidential G4 VMs and Confidential G4 GKE Nodes.
Developer resource: The new TPU Developer Hub is the place to go for model builders, optimizers, and developers to learn to unlock the full performance of Google Cloud TPUs. Read more in this blog.
New product: Scale your AI workloads with the new OpenTelemetry-Based TPU AI Telemetry Collector Agent. For the first time, you can route high-fidelity TPU hardware telemetry to Google Cloud Monitoring, Google Managed Prometheus, or your own self-hosted Grafana stack.

Practitioner guides and how-tos

How-to guide: Learn how to build high availability into an AI inference workload running on GKE Inference Gateway with TPUs, Cloud Storage FUSE and Dynamic Resource Allocation (DRA). This blog provides an overview, or you can get all the technical details in the hands-on codelab.
How-to guide: Did you know you can connect your AI agents to unstructured data in Cloud Storage via Model Context Protocol (MCP)? In this blog, learn about why would want to do that from three customer examples, then how to do it, choosing either a fully managed service, or a self-managed local server for more customization and control.

Research, reports and deep-dives

Report: According to an independent benchmark report, GKE Inference Gateway outperforms the next leading managed Kubernetes service with 15.7% higher throughput, 92.8% shorter wait times, and 62.6% lower inter-token latency. This performance can be attributed to its use of prefix caching, which optimizes LLM performance by storing the KV cache (activation states) of long, repetitive prompt prefixes. Learn more in the blog.
Architecture deep dive: A closer look at the cold start problem, this time for TPUs and GKE, and how the Run:ai Model Streamer can help change the dynamic.

Customer and partner updates

Customer win: Leveraging GKE, BigQuery, Cloud SQL, and Gemini Enterprise Agent Platform, Pager Health is eliminating operational fragmentation to deliver a simplified, personalized U.S. healthcare experience that transforms lives.
Customer win: Trustpilot, the customer review platform, built a high-volume streaming pipeline using fine-tuned Gemma models with Dataflow and Gemini Enterprise Agent Platform running on cost-optimized A2 VMs using A100 GPUs, as well as optimized version of vLLM maintained by Gemini Enterprise Agent Platform.

May 2026

Product, technology and tool updates

Product update: GKE Agent Sandbox is now generally available.
New open-source project: Agent Substrate is a new open-source project aimed at continuing to push the limits of agentic infrastructure density
New feature: Google AI Edge Portal, a solution for testing and benchmarking on-device machine learning (ML) at scale, now supports benchmarking and debugging on-device LLMs. Read more here.
Product deep dive: We went into depth about Cloud Storage Rapid, a new family of high-performance storage offerings for AI workloads. At launch, offerings include Rapid Bucket (formerly Rapid Storage), a high-performance zonal object storage offering, and Rapid Cache (formerly Anywhere Cache), which accelerates reads on-demand and colocates compute and data for workloads in existing buckets.

Research, reports and deep dives

Architecture deep dive: Google Global Infrastructure VP Bikash Koley and Engineering Fellow Arjun Singh provide a high-level overview of the challenges that AI workloads pose to network infrastructure, and discuss the deep enhancements we’ve made to our data center fabrics, WAN, and global networks to better support them.
Architecture deep dive: We unveiled a new cluster-level reliability model for developing frontier AI models on TPUs, ditching instance-level reliability

Customer and partner updates

Customer win: Visual media provider Imgix serves more than 8 billion images and videos from AI Hypercomputer equipped with G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell GPUs.

Reimagining work: How Pythian’s internal AI playbook delivers customer ROI

Thu, 27 Aug 2026 16:00:00 +0000

When Pythian rolled out Google Cloud’s Gemini Enterprise across our 500-person company in 27 countries, the goal was simple: use our own company as a proving ground to discover how enterprise AI actually delivers ROI.

What we found changed our strategy entirely.

Since the rollout of Gemini Enterprise and our previous enterprise AI deployments, Pythian observed firsthand why so many enterprise AI initiatives stall out or fail.

Most organizations trap themselves in a tool-centric mindset — buying licenses, making tools broadly available, and assuming value will naturally follow. They get stuck chasing "nickel and dime" micro-efficiencies (like saving 5 minutes per user) while missing structural, high-ROI workflow transformations. Compounding the problem, even when custom agents are built, they frequently stall in pilot mode or break down in production because teams lack the operational capability to manage AI model drift, agent lifecycles, and ongoing observability.

To solve this, we engineered the Pythian AI Operating Model — a multifaceted, end-to-end framework designed to take enterprise AI from high-level strategy all the way into sustained production. While our dual center of excellence (COE) serves as the core execution muscle, it is the application of the entire framework, from Field CTO strategy and tooling deployment to the dual COE and XOps, that consistently unlocks million-dollar outcomes.

By proving this complete model internally first, Pythian drove a 3x surge in active user engagement and cut our database incident resolution times by 80%.

The four pillars of the Pythian AI operating model

To move past the common failure points of enterprise AI, our framework consolidates strategy, execution, and operations into a single continuous loop:

Field CTO strategy ──> tooling deployment ──> dual COE execution ──> production XOps

Field CTO strategy and governance: Generative AI is arguably the most academically challenging architectural shift in IT history. Led by former C-suite tech leaders, our Field CTO practice provides executive advisory to establish steering committees and clear value metrics. The team audits operations using 16 horizontal agentic patterns (like automated document processing and runbook creation) to build a prioritized backlog of high-ROI use cases before development starts.
Tooling and platform deployment: The team establishes a secure, production-grade foundation on platforms like Gemini Enterprise and connects AI directly into CRMs, ERPs, and database estates to ground models in real corporate context.
The dualCOE: This execution muscle is split into two specialized engines:

People productivity COE: This group handles adoption and change management. Instead of expecting non-technical teams (like HR or Procurement) to build its own agents, this COE builds no-code agents for them, focusing entirely on enablement.
Process productivity COE: This team engineers deep, custom-coded AI agents and complex agentic workflows that integrate into core data platforms for autonomous operations.

XOps (AI production management): While deploying an agent is 20% of the journey, maintaining accuracy in production is 80%. Because AI models and prompt structures naturally drift over time, this XOps practice provides the continuous monitoring, prompt tuning, and model observability needed to keep agents performing without breaking core workflows.

The difference between chasing minor, scattered efficiencies and driving structural enterprise ROI comes down to how you align your operating strategy:

Alignment element	Tool-centric approach	Pythian AI operating model
Primary metric	Individual minutes saved per user	High-impact workflow reimagination and ROI
Operational focus	Broad, unguided tool availability	Prioritized backlog via 16 agentic patterns
Execution muscle	Ad-hoc user experimentation	Dual COE (people and process productivity)
Production lifecycle	Unmonitored static deployments	Active XOps (Continuous accuracy and drift management)

Real-world impact: from database ops to global supply chains

Whether managing 70 manufacturing plants or 30,000 enterprise databases, AI succeeds when tied to structural, high-value workflows:

Pythian “as a customer:” Across 15,000 monthly database tickets, our Process COE deployed an agentic workflow that reads tickets, searches knowledge bases, and auto-generates mini runbooks before an engineer touches them. The result was slashed mean time to resolution by 80% and tripled active user engagement.
Knowledge management customer: We deployed autonomous IT support agents across 10,000 consultants. As a result, we were able to automate 10% of 20,000 annual IT tickets into "no-touch" resolutions, saving 1,000,000+ operational hours.
Supply chain customer: By building custom agentic supply chain tools on Gemini Enterprise, we compressed forecast-matching cycles from weeks down to 2–3 days across 70 global manufacturing sites.
Retail customer: We combined Gemini Agentic AI and computer vision to automate store product onboarding. As a result, we transformed a 20-minute manual task into a multi-second flow.

Ready to build your AI operating model?

Scaling AI demands more than tool-level experimentation. It also requires an end-to-end AI operating model. Learn how Pythian pairs with Google Cloud to operationalize strategy, streamline XOps, and fast-track your Gemini Enterprise journey.

FinOps for the AI era: New flexible billing and cost controls for agents

Wed, 26 Aug 2026 13:30:00 +0000

Editor's note: A product image was updated after initial publication.

As AI takes on more complex work, business leaders face a new challenge: enabling rapid innovation using agents while protecting their margins and budgets. To get a real return on AI, financial operations (FinOps) and cost management must evolve alongside technology, giving you clear visibility, proactive cost controls, and flexible payment models that fit your needs.

That’s why today we’re introducing expanded billing flexibility and new cost management tools for agent workloads across Gemini Enterprise and developer tools like Google Antigravity in Gemini Enterprise and Android Studio.

Flexible payment options: You can mix our existing, predictable per-user seat subscriptions with a new pay-as-you-go option in Gemini Enterprise app that lets you run agent workloads without hitting quota limits mid-task.
Developer access, one place to manage your AI: Google Antigravity and Android Studio AI use is now included in your Gemini Enterprise subscription (available for select customers and rolling out broadly soon), giving your developers more without giving you more to manage. Usage across Antigravity, the platform, and the app rolls up into a single view instead of separate licenses and billing silos.
Pay less as your usage grows: If your AI workloads are steady or climbing, Flexible Savings Plans let you commit to a monthly spend you're comfortable with and take 10–20% off your token costs — no minimums, no maximums, and no new billing silo to manage.
Consolidated spend guardrails: You can now set hard monthly caps on AI spend and projects, estimate agent runtime costs, and catch sudden budget spikes before they hit your invoice.

Give your teams flexibility without losing control over spend in Gemini Enterprise

Every organization operates differently. Even within the same business, no two teams consume AI in the same way. Your business users might rely on steady, everyday productivity tools. Meanwhile, your technical teams might run AI agent workloads in bursts.

To help align costs with how work actually gets done, you can combine these payment and licensing choices and features across Gemini Enterprise:

Option	How it works	Why it helps optimize spend
Gemini Enterprise app per-user seat subscription	You pay a fixed monthly fee per user, which includes daily quota pools that are shared across your entire project.	Predictable budgeting. It provides finance teams with a clear, steady monthly baseline for teams with consistent daily productivity needs.
[New] Gemini Enterprise app pay-as-you-go consumption edition *available for select customers and rolling out broadly soon	There is no upfront commitment or base subscription fee, meaning you pay strictly for the compute and tokens your teams consume at standard model API rates.	Only pay for what you use. Your spend scales up and down automatically with real usage, ensuring you never pay for empty seats when project demand dips.
[New for Antigravity in Gemini Enterprise] Consolidated pooled quotas	Daily usage allowances are pooled project-wide, letting business apps, developer tools, and custom agents draw from the same shared quota. Pooled quota is always exhausted first, and admins can control if overages are allowed, at which point it’s charged at pay-as-you-go rates.	Maximized resource usage: Unused daily allowances from business users automatically absorb heavy developer or custom API agent demands, so no quota allowance goes to waste.
[Coming soon] Deferred execution pricing *available for select workloads soon	Mark eligible agent workloads as deferred, and our intelligent scheduler in the Gemini Enterprise Agent Platform runs them during off-peak capacity windows.	Substantial discounts for work that can wait: AI workloads can run on separate, off-peak capacity, you pay up to half the inference cost and bypass standard quota limits entirely – letting you run substantially more agentic volume under the same budget.

Equip developers with advanced agentic tooling under a single Gemini Enterprise subscription

We’re rolling out access to Google Antigravity in Gemini Enterprise, an agent-first developer platform that brings powerful agentic coding and agent-building capabilities to technical teams, included with Gemini Enterprise subscriptions for eligible customers. In addition, Android developers can leverage the Google Antigravity quota included in their Gemini Enterprise subscriptions natively in Android Studio, the agentic IDE for professional Android development.

To be more efficient with agentic coding costs, we are pooling developer tools quota included in each Gemini Enterprise subscription and making it available across the whole Google Cloud project so your teams can benefit from the capacity you’re already purchasing. Your developers get access to advanced agentic tools, while you maintain centralized governance and control.

For a closer look into what’s new with Antigravity in Gemini Enterprise and how customers are putting it to work in production, take a look at our deep-dive.

Budget smarter with Gemini Enterprise Flexible Savings Plans (FSPs)

If your organization has steady or growing AI workloads, Gemini Enterprise Flexible Savings Plans offer a simple, spend-based commitment model across Gemini Enterprise usage. FSPs are designed to lower token costs while keeping budgets flexible:

Programmatic savings: Receive 10% off for 1-year or 20% off for 3-year commitments for monthly spending across Gemini Enterprise.
Tailored to your pace: With no minimum or maximum spend requirements, you can determine a monthly commitment that fits your current traffic and make adjustments as your usage increases over time.
Enterprise Agreement (EA) friendly: FSP spend seamlessly draws down against your existing Google Cloud EA, giving lines of business dedicated budget control without fragmenting your broader cloud commitments.

Gemini Enterprise Flexible Savings Plans are already available for self-serve customers and customers on enterprise agreements.

Give your teams the freedom to build while maintaining financial discipline

As a leader, your goal isn't to restrict the potential value of AI – it's to remove the financial and operational risk that you face without managed AI costs. You should be able to give engineering, marketing, and operational teams the freedom to innovate with agents, but you should also have the visibility to trust what those agents are doing and the safety nets to protect your budget.

To bridge this gap, we've built robust, native governance tooling directly into the Google Cloud Billing Console around three simple goals:

1. Plan before you scale: The Google Cloud Pricing Calculator lets you estimate anticipated costs in Gemini Enterprise across per-user licenses, developer tools, and background agent runtimes. It gives you the numbers you need to build clear business cases upfront before project work begins.

2. Enforce boundaries without micromanaging spend: Instead of spending time tracking daily usage variations across project teams, let these tools do the monitoring for you:

Early anomaly detection: If a project’s AI spending trends higher than normal, the system flags the deviation with root cause analysis and pinpoints the top 3 SKUs driving the increase so you can see exactly what changed.

Billing Console showing an Early Anomaly alert with the Root Cause Analysis (RCA) breakdown highlighting the driving SKUs

- Project-level spend caps: When a project needs defined financial boundaries, you can set a firm monthly spend limit directly in the Google Cloud Billing Console. If a project hits its limit, the agent's API calls temporarily pause – protecting your budget without affecting the rest of your production infrastructure. Automated email alerts at 50%, 80% and 100% of the budget keep you informed of your progress against the spend limit.

- Overage controls: If a spend cap triggers, you can choose to resume work with a single click in the console. Alternatively, if your priority is continuous operation, you can turn on overages so excess usage smoothly transitions to consumption rates, which can draw directly against your FSP to keep overage unit costs heavily discounted.

Enabling overage pay-as-you-go for a project.

3. Get visibility into business value: Use centralized billing reports paired with the FinOps agent to generate natural-language cost insight summaries of where your budget went, making it simple to show ROI to leadership.

AI spending reporting in Google Cloud Console

Go deeper with AI cost optimization

To build a full-stack FinOps strategy that optimizes the cost, latency, and performance of your models and infrastructure, explore our detailed architecture specifications and frameworks:

How to outsmart infrastructure constraints with dynamic capacity management: Discover how to optimize your compute investments with capabilities in Google Kubernetes Engine and Google Compute Engine that automatically schedule and reallocate resources to avoid interruptions, over-provisioning, and over-reliance on any one hardware configuration.
Expanding Google Antigravity for Enterprise Customers: Read our developer tooling deep-dive to see how technical teams are accelerating software delivery with agent-first workflows.
What sports cars can teach us about optimizing AI spend: More tokens doesn't always mean better AI. Read our conversation with Mike Clark, Director of Product Management for Gemini Enterprise Agent Platform, on how to balance horsepower with efficiency and get the highest return out of every dollar you spend on AI.
Protection during usage spikes: Your heavy workloads can surge during peak hours without forcing you to pay for expensive, dedicated infrastructure that sits idle the rest of the time. As your AI usage grows, Gemini models can automatically scale on demand without hitting artificial rate limits – processing up to 50 million tokens per minute.

Now introducing Gemini Enterprise for Legal

Tue, 25 Aug 2026 12:00:00 +0000

Few professions are as exacting as the practice of law. A team reviewing a contract or building a case works inside strictly privileged information, firm-specific playbooks, and a body of law that changes constantly. The work thrives on nuanced, professional judgment — and the systems supporting it inherit real obligations: ethical walls that cannot be crossed, matter permissions that cannot be flattened, and a duty of confidentiality that does not bend for convenience.

General-purpose AI, however capable, does not meet that standard on its own. Foundational model intelligence is necessary. For legal work, it is nowhere near sufficient.

What makes the difference is the system built around the model: skills that enhance a firm's own expertise, connections into the systems where matters actually live, agents that complete work rather than return suggestions, and an open ecosystem to extend all of it — with governance running underneath all four. Each is valuable alone. Only in combination do they produce something a firm or a legal department can put into production and actually rely on.

Today we're bringing that to legal practice with Gemini Enterprise for Legal, part of our new suite of purpose-built industry solutions.

Bringing Gemini Enterprise to your legal practice

Four components of Gemini Enterprise for Legal

Developed alongside industry leaders, Gemini Enterprise for Legal provides an integrated, fully governed environment configured for rapid deployment across firms and corporate legal departments:

1. Purpose-built skills for legal work. Skills are reusable packages of instructions and context, designed by domain experts, that teach an agent to run a specialized task while enforcing your firm's playbooks, citation rules, and house style. They cover contract review and redlining, playbook creation, regulatory horizon scanning, legal research, DSAR fulfillment, and more — and they are where a firm's institutional knowledge becomes something the platform can execute rather than something a partner has to re-explain.

2. Connections to trusted systems and data. Secure MCP connectors link agents to the document management systems, case repositories, research services, and industry applications legal teams already rely on — inheriting each platform's existing user permissions and access controls rather than working around them.

3. Agents that act within the data. Skills and connections come together in agents that carry work through: pre-built agents from Google and leading legal software providers deploy out of the box. Specialized agents handle legal and policy research, regulatory screening, and contract drafting — bringing deep legal expertise onto a platform with centralized governance.

4. An open partner ecosystem. Every firm and legal department practices differently. Partnerships with global systems integrators and legal-tech specialists — Accenture, Deloitte, Devoteam, Factor Law, KPMG, Tribe.ai, Valtech, Zazmic, Zencore, and 66degrees — let organizations customize, integrate, and scale across complex enterprise architectures without vendor lock-in.

Running underneath: a governed control plane. A single dashboard for legal IT and risk teams that natively enforces security policies (VPC, CMEK), maintains private data isolation, and holds every output to verifiable grounding with traceable citations.

Unlocking high-value workflows with domain-specific skills

Gemini Enterprise for Legal shifts AI from passive querying to agentic execution, automating high-volume, precision-critical workflows such as:

Proactive regulatory horizon scanning: Keeps legal and compliance teams ahead of global mandates by autonomously tracking legislative updates, court dockets, and supervisory bodies. It cross-references emerging changes against enterprise policies to flag exposure gaps and generate updated policy drafts for immediate practitioner review.
Automating data discovery and DSAR response: Modernizes privacy workflows by compiling personal data across fragmented enterprise systems in seconds. It eliminates the manual toil of Data Subject Access Requests (DSARs), and allows for adherence to regulatory timelines while minimizing operational risk.
Accelerating contract review and negotiation: Compresses turnaround times for inbound vendor agreements, NDAs, and complex M&A documentation by benchmarking terms against enterprise playbooks. It surfaces high-risk clauses and potential exposure, enabling attorneys to focus on strategic negotiation and high-value judgment.
Building and updating contracting playbooks: Transforms legacy agreement archives into dynamic, actionable playbooks instantly. It automatically extracts key terms, fallback positions, and institutional knowledge to maintain portfolio-wide term consistency and lower negotiation variance across the enterprise.
Redacting documents for motions to seal: Eliminates the manual burden of preparing court filings and redacting legal documents. It intelligently identifies sensitive terms and PII for rapid practitioner confirmation, dramatically accelerating filing timelines while safeguarding confidentiality.
Drafting NDA documents: Elevates contract creation through structural fidelity validation that enforces firm standards and logical document hierarchies. It allows legal teams to rapidly generate and evolve non-disclosure agreements with complete formatting confidence and minimal review overhead.

Open ecosystem of connectors across the legal technology stack

Legal work is only as good as its sources, and legal data carries permissions that have to travel with it. Gemini Enterprise for Legal connects directly to core legal systems via secure MCP connectors. Crucially, access is bound by existing role-based access controls, document-level permissions, and trusted data controls inherited from document management and ediscovery systems.

Productivity and collaboration:

Google Workspace: Connects seamlessly with Google Docs, Gmail, Drive, and Sheets to analyze matter communications, correspondence, and surface internal files while enforcing enterprise access controls.
Microsoft 365: Integrates directly with Word, Outlook, and SharePoint to triage inquiries, redlines, and securely ground work product across emails and matter folders without breaking workflow context.

Document management:

iManage: Gives Gemini Enterprise for Legal permission-bound, auditable access to governed iManage content, including matter history, documents, and institutional knowledge, eliminating the need for bulk exports or custom integrations.
NetDocuments: Enables Gemini Enterprise to search and analyze an organization's knowledge and expertise while preserving each user's existing permissions and ethical walls. Source documents never leave the governed NetDocuments environment.

Contract lifecycle and execution:

Docusign: Integrates agreement metadata, active approval workflows, and contract repositories to surface obligations, track renewal dates, and streamline drafting-to-execution lifecycles.

E-discovery and litigation intelligence:

Everlaw: Connects Gemini Enterprise to litigation and investigations evidence in Everlaw, allowing legal teams to search and analyze their data, uncover case insights, and build timelines directly in Gemini Enterprise, with responses grounded in the underlying documents and access governed by each user’s existing Everlaw permissions.
RelativityOne: Allows legal teams to stand up workspaces, organize case data, and manage operations within a secure perimeter.

Primary law, research, and public dockets:

Thomson Reuters HighQ: Connects Gemini Enterprise for Legal with HighQ, helping legal teams securely access and reference relevant HighQ content within their workflows.
Free Law Project’s CourtListener.com: Provides access to millions of federal and state court opinions, PACER dockets, judicial profiles, and oral arguments.
Courtroom5: Delivers jurisdiction-aware civil litigation datasets, procedural rules, and deadline calculation logic.

Specialized legal AI and intellectual property:

Harvey: Bridges Harvey’s legal reasoning intelligence into Gemini Enterprise, supporting complex legal reasoning and research across Vault projects.
Solve Intelligence: Links Gemini Enterprise to worldwide patent and non-patent literature, SEP technical standards, and prior art databases for patent drafting and claim charting.
Legora: Agentic operating system for legal work, supporting lawyers in research, review, and drafting across complex matters

Third-party agents and implementation partners

Every firm and legal department practices differently. Through our open platform, organizations can deploy pre-built partner agents or collaborate with systems integrators to scale custom capabilities:

Deloitte: Contract Summarize Pro Agent that synthesizes complex contracts into clear summaries for rapid insight and informed decision-making. Clause Guard contract redlining agent to accelerate turnaround times, and minimize risk in contract management.
Eudia Knowledge agent: Accelerates high-stakes legal and contracting work by combining institutional intelligence with a suite of agents that execute deep legal research, high-volume document analysis, and regulatory compliance screening.
Global systems integrators & tech partners: Strategic partnerships with Accenture, Deloitte, Devoteam, Factor Law, KPMG, Tribe.ai, Valtech, Zazmic, Zencore, and 66degrees ensure legal teams can customize, integrate, and scale these capabilities across complex enterprise architectures without vendor lock-in.

Gemini Enterprise for Legal offers leading firms a way to manage modern legal work with a secure agentic platform

Developed alongside leading global law firms

We are working closely with leading law firms, including Cleary Gottlieb, Freshfields, Weil, and Williams & Connolly, to ensure these capabilities address the realities of sophisticated legal practice.

“The legal sector is entering a period of accelerated change and transformation. For Freshfields the opportunity lies in how effectively we combine frontier technology like Gemini Enterprise for Legal with our expertise, robust governance and institutional knowledge to create value for our clients. Our strategic, multi-year partnership with Google Cloud is helping us accelerate that work and enhance how we deliver legal services.” — Alan Mason, Global Managing Partner, Freshfields.

“We’re thrilled to partner with Google Cloud in the early adoption of Gemini Enterprise for Legal. We look forward to integrating Google’s technology to streamline workflow and further support our litigators in shaping outcomes critical to our clients’ futures.” — Joe Petrosinelli, Chairman, Williams & Connolly.

"Our collaboration with Google gives us early access to emerging capabilities while allowing us to help shape the platform based on the realities of sophisticated legal practice. The result is technology that helps us continue to deliver the innovative, high-quality service our clients expect from Weil. We are looking forward to working with Google Cloud engineers and the Gemini Enterprise product team as we further innovate and evolve our AI capabilities." — Ramona Nee, Incoming Executive Partner, Weil.

Weil scales judicial insights with Benchmark, built on Gemini Enterprise

Built on an enterprise-grade foundation

Confidentiality is not a feature of legal technology; it is the precondition for using any at all. Because Gemini Enterprise for Legal runs on Google Cloud infrastructure and the Gemini Enterprise platform, the permissions and access controls your firm already maintains are the boundaries the platform operates within — not settings it asks you to reconstruct.

Client data, firm-specific playbooks, intellectual property, custom agents, and model outputs stay private to your organization, and are never used to train or fine-tune Google's foundation models. Because we operate the full stack, from infrastructure and models through the application layer, we can tune performance and cost together — so expanding what your teams can take on does not mean expanding spend at the same rate.

This is just the beginning

The launch of Gemini Enterprise for Legal represents another defining step in delivering on the promise of Gemini Enterprise: bringing the best of Google AI to every professional, for every workflow, natively tailored to the way they work.

Gemini Enterprise for Legal is available in preview today, launching alongside our new purpose-built solution for Financial Services. We invite law firms and legal teams to explore how Gemini Enterprise can transform their most critical workflows, with solutions for Healthcare, Life Sciences, and other Professional Services on the horizon.

Now introducing Gemini Enterprise for Financial Services

Tue, 25 Aug 2026 12:00:00 +0000

Protecting capital in today's markets requires immense speed and precision. A financial analyst preparing a deal memo works across licensed market data, internal models, and confidential client files. General-purpose AI lacks the real-time accuracy, verifiable data lineage, and strict security that financial institutions demand. While model intelligence is necessary, without deep integration into trusted financial systems, it is not sufficient.

Making AI genuinely useful inside an industry requires four things, together: domain expertise encoded into reusable skills, secure connections to the systems and data the work depends on, agents that can act inside real workflows, and an open ecosystem that extends and scales all of it — with governance running underneath all four. Each is valuable alone. Only together do they produce something an institution can actually put into production and see true return on investment.

Today, we are delivering on this vision with Gemini Enterprise for Financial Services, bringing Google’s agentic AI directly into the workflows of capital markets and corporate banking.

Bringing Gemini Enterprise into the workflows of capital markets and corporate banking

Four components, built for financial work

Gemini Enterprise for Financial Services delivers an integrated, secure environment configured for rapid deployment with four core components:

1. Purpose-built financial skills. Skills are reusable packages of instructions and context that teach an agent to run a specialized task the way your institution runs it — applying custom formatting to a report, pulling a specific data cut, following a defined research methodology. They are available inside the Financial Research agent and to any agent your teams build.

2. Secure Model Context Protocol (MCP) connectors. Direct integrations, using MCP, into essential financial platforms and licensed data sources, configured inside your own environment. Access stays bound by the entitlements you already maintain — licensed data stays licensed, and permissioned data stays permissioned.

3. Agents that act. At its core is the Financial Research agent which is a Google-built, Google-managed agent that runs end-to-end research with full explainability. It ships with more than 50 foundational skills and exposes its reasoning through confidence scores, explicit methodologies, data snapshots for auditing, and precise source citations. Analysts can use it directly in the Gemini Enterprise app or wire it into existing agent workflows through Agent-to-Agent (A2A) APIs, and it connects to enterprise data sources over MCP to produce reports and documents in the formats your teams already use. Alongside it, out-of-the-box partner agents cover other workflows and extend the capabilities further.

4. An open partner ecosystem. Scale with global systems integrators and specialized fintech providers including 66degrees, Accenture, Artefact, Capgemini, Cognizant, Deloitte, Genpact, GFT Technologies, Infosys, KPMG, PwC, Quantiphi, Slalom, Tribe AI, and Zencore to customize and integrate the platform into your own architecture, without vendor lock-in.

Running underneath: a governed control plane. A single dashboard for IT and risk teams that natively enforces security policies (VPC, CMEK), maintains private data isolation, and holds every output to verifiable grounding with traceable citations.

Unlocking high-value workflows with domain-specific skills

Whether used by private equity specialists, wealth managers, or compliance teams, the solution adapts to diverse workflows like credit risk assessment, portfolio monitoring, market news synthesis, and investigative financial research:

Elevate advisor insights: Equips relationship managers and advisors with AI-generated insights, personalized recommendations, and tailored artifacts, enabling higher-quality conversations and fostering loyalty.
Deepen Know Your Customer (KYC) research and analysis: Modernizes onboarding and Know Your Customer (KYC) workflows across private banking and prime brokerage by using multi-format ingestion (PDFs, Excel, SEC filings) to map complex corporate hierarchies, evaluate risk personas, and resolve ultimate beneficial owners (UBOs).
Enhance portfolio resilience: Helps trading desks deal with sudden macroeconomic shocks. It reduces complex bond portfolio risk exposure analysis to a sub-5-minute execution, complete with automated duration-hedging strategy suggestions.
Uncover credit market opportunities: Transforms credit data into actionable trade ideas by identifying and isolating potential mispricings. This enables teams to expand trading volumes while lowering back-office risk and underwriting latency.
Accelerate bond issuance: Compresses client pitch presentation timelines from days to minutes so that fixed-income and underwriting teams can proactively target prospects, increase deal capacity, and secure a crucial first-mover advantage to help win more business.

Open ecosystem of connectors across the financial technology stack

Gemini Enterprise connects directly to core financial systems via secure MCP connectors. Access is bound by existing role-based controls, ensuring verifiable grounding and precise source citations:

Productivity and collaboration:

Google Workspace: Enables seamless analysis and live artifact generation across Docs, Sheets, and Slides while adhering to enterprise DLP policies.
Microsoft 365: Integrates directly with Excel, Word, and PowerPoint to populate financial models, research memos, and client pitch decks.

Market data and financial fundamentals:

Daloopa: Provides the structured, source-linked financial data layer that enables finance professionals and AI tools to produce accurate and auditable results.
FactSet: Enables secure, authorized access to FactSet's multi-asset class financial and non-financial datasets, powering reliable AI-driven workflows with fully auditable, compliant insights.
Finnhub: Provides real-time financial APIs, global fundamentals, and earnings call transcripts for in-depth financial research.
Fiscal.ai: Delivers institutional-grade financial data within minutes of earnings, covering financials, news, ownership, segments & KPIs, filings, and earnings call transcripts.
Guidepoint: Connects to primary research insights and expert network transcripts to inform and validate investment theses.
LSEG: Provides access to a broad range of trusted financial data, analytical models, indices and news. Enabling customers turnkey access to trusted financial intelligence across every stage of the investment lifecycle.
S&P Global: Integrates cited, verifiable S&P Global data for a range of workflows, from financial analysis, to peer benchmarking, industry research, and more.

Risk, ratings, and private markets:

Moody’s: Brings ratings, default risk models, and real time news fused into one lens for counterparty risk assessment.
MSCI: Connects to proprietary indexes, data and models spanning public and private assets and also provides risk analytics and factor exposures.
PitchBook: Provides comprehensive data and research on private equity, venture capital, credit, M&A, and public markets, including, company financials, deal terms, valuations, and fund performance.

Regulatory and corporate records:

SEC Edgar: Delivers instant, verifiable retrieval of statutory filings, 10-Ks, 10-Qs, and 8-Ks with precise citation mapping.
Dun & Bradstreet: Accelerates commercial onboarding and KYB verification through direct access to global corporate hierarchy records.

Digital assets and indices:

CoinDesk Data and Indices: Supplies institutional-grade digital asset pricing, benchmark indices, and crypto market intelligence for multi-asset strategies.

Introducing Gemini Enterprise for Financial Services

Third-party agents and implementation partners

Organizations can deploy out-of-the-box partner agents or collaborate with global systems integrators to scale custom capabilities without vendor lock-in:

D&B Business Verification agent: Accelerates commercial onboarding and strengthens KYC compliance.
FlowX agents: Automate loan pack completeness check, document reconciliation and many other mission critical processes for financial institutions.
Obin Financial agent: Helps asset management, commercial lending, and insurance teams accelerate complex financial analyses.

S&P Global agents: Data Retrieval Agent for multi-step analysis, report generation, research workflows, and the Horizons Agents that help turn complex energy and sustainability data into fast insights for finance workflows.
Global systems integrators and tech partners: Strategic partnerships connect firms with specialist FinTech and leading global systems integrators, including 66degrees, Accenture, Artefact, Capgemini, Cognizant, Deloitte, Genpact, GFT Technologies, Infosys, KPMG, PwC, Quantiphi, Slalom, Tribe AI, and Zencore to manage custom configurations and deploy specialized capabilities at a global scale.

Developed alongside leading global financial institutions

We are developing these capabilities in close collaboration with financial institutions, including Deutsche Bank and CME Group, to ensure they reflect the operational realities of the industry.

“As a design partner for the Financial Research agent, Deutsche Bank has helped shape this capability in view of the realities of a highly regulated industry – from data protection and governance to the workflows our teams use every day,” said Marie-Jeanne Deverdun, Chief Technology, Data and Innovation Officer, and Member of the Deutsche Bank Management Board. “Starting in the Corporate Bank, we see significant potential to reduce manual research effort, improve the consistency and auditability of outputs, and give our teams more time for client conversations. This is an important step in applying AI where it can make a practical difference: safely, responsibly and at scale.”

This launch builds on the rapidly growing momentum of Gemini Enterprise, with many leading financial institutions like BNY, Citi Wealth, Lloyds Banking Group, Macquarie Bank, and Signal Iduna using it to equip their workforce with advanced, agentic workflow tools to drive growth and efficiency.

Built on an enterprise-grade foundation

Because Gemini Enterprise for Financial Services runs on Google Cloud infrastructure and the Gemini Enterprise platform, organizations get the security, governance, compliance, and cost-management capabilities they expect from an enterprise platform.

Customer data, business rules, intellectual property, custom agents, and model outputs remain private to their organization. Your data is never used to train or fine-tune Google’s foundation models. Furthermore, our full-stack approach - from infrastructure and models to the application layer - allows us to optimize performance and cost, helping organizations maximize the value of their AI investments.

This is just the beginning

Gemini Enterprise for Financial Services is available in preview today, launching alongside our new purpose-built solution for Legal. We invite enterprise leaders in financial institutions to explore how Gemini Enterprise can transform their most critical workflows, with solutions for Healthcare, Life Sciences, and other Professional Services on the horizon.

Cloud CISO Perspectives: Sticking to security fundamentals in the AI era

Fri, 21 Aug 2026 16:00:00 +0000

Welcome to the first Cloud CISO Perspectives for August 2026. Today, Chris Betz explains why the AI era makes it more important than ever to lean into security fundamentals.

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How to stay strong with security fundamentals in the AI era

By Chris Betz, CISO, Google Cloud

Chris Betz, CISO, Google Cloud

As AI accelerates the capabilities of adversaries, foundational strength becomes the primary differentiator between resilience and vulnerability. It’s a dangerous and unfortunately common misconception that traditional security fundamentals are becoming obsolete. For CISOs, the challenge is to adopt new AI technology securely while scaling essential, effective defensive practices to move at the speed of the adversary.

For both attackers and defenders, AI has been a catalyst for optimization and innovation. While traditional automation has allowed us to perform repetitive tasks at scale, AI enables both sides to execute highly-specific, customized actions at massive scale and unprecedented speed.

Collectively, these technologies reduce the attack surface and contribute to the deep context that defensive AI needs to be a business enabler — and create the necessary conditions for successful AI-powered defenses.

We can see the threat developing almost in real-time. Adversaries are deploying new malware with just-in-time AI that dynamically generates malicious scripts and obfuscates code mid-execution to evade detection. They use sophisticated vishing and deepfakes for identity theft and business email compromise. We even see unauthorized AI tools lead to the rise of shadow agents.

Defending against AI powered security threats requires more than accelerating current security practices; it means stepping back and beginning with the security foundation and layered defenses. It’s critically important to build and use a layered defense with the right guardrails — foundational cybersecurity building blocks that we’ve been investing in for years.

Doubling down on this foundation: technologies like multi-factor authentication (MFA), Zero Trust frameworks, consistent system patching, and comprehensive detection and response. Collectively, these technologies reduce the attack surface and contribute to the deep context that defensive AI needs to be a business enabler — and create the necessary conditions for successful AI-powered defenses.

Revolutionizing vulnerability management

In just a few short years, identifying and fixing vulnerabilities has evolved from a mostly laborious, manual process to one driven by AI tools discovering vulnerabilities at volumes never seen before. Further, the time to exploit window has essentially been eliminated.

However, it’s not enough to merely discover vulnerabilities, especially at today’s volumes. You still need to prioritize fixing those that have the most critical impact on your systems and networks first, and that necessitates an equally-rapid response in smart mitigation.

Organizations use multiple models to scan for flaws and then suggest high-quality code fixes that engineers can quickly move into production, leveraging capabilities like AI Threat Defense. AI allows us to automate the entire software development lifecycle, from discovery to testing and deployment, ensuring that our defensive posture evolves faster than the threats targeting us.

Enhancing threat modeling

We’re also seeing the fundamental concept of threat modeling have an outsized impact. Doing threat modeling well requires bringing context together from your code, your cloud architecture, system design, and network pathways.

While it isn’t easy, using AI can scale our ability to bring that data together into a coherent picture. Teams have been experimenting with multi-AI models to collect system information and enumerate threats.

As I noted in June, engineering teams at Google Cloud now route product launches through an agent-based security review pipeline. High-risk indicators automatically get flagged for human review, while we’ve replaced static threat models with dynamic product dossiers that update in real-time.

The CISO as a strategic business leader

The most effective security leaders that I know today are more than just technologists: They are strategic business leaders. The intense global focus on AI vulnerabilities has brought cybersecurity to the forefront of boardroom and executive attention like never before.

This visibility is an opportunity to lead. We CISOs are expected to communicate with clarity, from the board to the C-suite to the security teams who look to them on a daily basis, demonstrating their ability as capable strategists who can navigate the complexities of AI while safeguarding the organization's growth.

By aligning security fundamentals with business objectives and using AI to enhance defense, we can lead our organizations securely into the future.

To learn more about building and maintaining strong security foundations in the AI era, read our newest Defender’s Advantage: Cyber Snapshot Report.

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In case you missed it

Here are the latest updates, products, services, and resources from our security teams so far this month:

Driving AI threat readiness with Wiz: Announcing new Wiz capabilities that can help organizations prepare for the AI era by expanding visibility and accelerating response, so your security teams can defend at machine speed. Read more.
PQC in Plaintext: Google Cloud’s post-quantum cryptography roadmap: We’ve long been actively working on and rolling out post-quantum cryptography in our infrastructure. Here’s our updated Google Cloud roadmap to migrate to PQC by 2029. Read more.
How Google Cloud detects, contains, and protects against emerging threats: Learn more about how Google Cloud empowers you with the tools, governance, and infrastructure you need to securely deploy workloads and maintain long-term trust. Read more.
Privacy-first medical AI with MedPerf and Google Cloud: Discover how Google Cloud and MedPerf use Confidential Computing to enable secure, privacy-first collaborative medical AI evaluation. Read more.
More cryptanalysis makes us all safer: Recent advances in frontier AI models do not signal the downfall of cryptography. Here’s why they’re best viewed as additional cryptanalysts. Read more.
How layered defenses harden Chrome against abusive notifications: Learn how Chrome Security has collaborated with Firebase Cloud Messaging (FCM) and Safe Browsing to significantly reduce notification abuse, and improve the security and quality of the web ecosystem for everyone. Read more.

Please visit the Google Cloud blog for more security stories published this month.

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Threat Intelligence news

Staying ahead of adversarial AI through agentic source code review: To help defenders implement agentic approaches similar to our approach at Google Cloud, we are sharing the details of our Agentic Vulnerability Discovery Harness architecture for the first time. AVDH can also be used alongside CodeMender’s ongoing scanning to create a two-layered defense strategy. Read more.
Cloud threat highlights from the first half of 2026: In the first half of 2026, Wiz's Research and CIRT teams tracked threats affecting thousands of cloud environments. We saw a notable increase in the volume of activity, with supply-chain attacks running at a previously unseen scale and developer toolchains and AI infrastructure drawing serious attention. Read more.
Batten down your packages: Mitigation guidance for supply chain compromise: GTIG and Mandiant have tracked ongoing and increasing open source software supply chain compromise campaigns over the past several years. Here are our mitigation and hardening recommendations to secure software supply chains, including insights we have developed as a result of supporting customers. Read more.
Multi-brand vishing extortion targets financial services and enterprise cloud environments: Telemetry and infrastructure analysis reveal that UNC6671 has not disbanded. Instead, the threat group has diversified its operations across multiple extortion fronts including Redact, Pink, Helix, and Falcon and continues to rely on voice phishing to target enterprise employees. Read more.
Keyv and cacheable npm package hijacked in supply chain attack: Wiz Research is actively investigating an ongoing software supply chain attack affecting multiple keyv/cacheable npm packages. Read more.
Inside the Metabase SQLi: Exploited in the wild: Wiz has reverse engineered Metabase CVE-2026-72898 with AI to accelerate defense. Here’s what we learned. Read more.

Please visit the Google Cloud blog for more threat intelligence stories published this month.

Now hear this: Podcasts from Google Cloud

Cloud Security Podcast: All about Project Atlas, Wiz's AI vulnerability research: Near Orfeld, head of vulnerability research, Wiz, discusses how his team uses multi-agent AI systems for discovering high-impact zero-day vulnerabilities in cloud infrastructure. Listen here.
Cloud Security Podcast: How Google eliminates classes of vulnerabilities at scale: How do you build the foundations for a secure Google-scale enterprise that stays secure even if an AI is writing the code and nobody has time to review it? Christoph Kern, principal security engineer, Google, explores what secure-by-design really means in the AI era. Listen here.

To have our Cloud CISO Perspectives post delivered twice a month to your inbox, sign up for our newsletter. We’ll be back in a few weeks with more security-related updates from Google Cloud.

How agents can delegate better

Fri, 21 Aug 2026 16:00:00 +0000

In any organizational behavior class, students will learn that effective delegation is among the most important skills for a seasoned leader. Getting meaningful work done involves careful coordination, starting with a subdivision of projects into manageable tasks, mapped onto the skills of the team, and assigned to the right people.

At Google Cloud, we’re learning a similar lesson when it comes to building and deploying AI agents in enterprise workflows. These workflows are best approached by multi-agent systems that can break apart and execute complex tasks. To do so, AI agents need to become good delegators.

To learn how, we turned to research from Google DeepMind. In their recent study titled Intelligent AI Delegation, they prove how delegation itself involves intelligence: adaptive negotiations, aligning on formal contracts, and security guardrails.

This work opens up new opportunities for customers building AI agents that can communicate, share tasks, and coordinate towards set objectives. Today, we’ll share four principles that emerged from that work, and how you might apply them to your own workflows.

Principle 1: Verify delegated work

If we are to permit AI to delegate tasks, we want it to do more than arbitrarily assign work. Agents should intelligently break down work into tasks that can be reliably verified. In our research, we call this "contract-first decomposition."

Like human delegation, this takes thoughtful deliberation. With people, this might mean a leader understanding their team’s strengths, and perhaps checking their work before it’s completed. For AI, there’s a similar learning curve. The orchestrating AI (the manager that sits atop a multi-agentic system) may consider multiple plans for how best to decompose and assign work, and keep decomposing sub-goals into smaller and smaller chunks until they become sufficiently simple to monitor and verify. Ideally, this should result in a plan where everything can be reliably graded. In reality, however, this may not always be possible to achieve.

Sometimes, it may be necessary to involve subjective assessment of whether work has been completed successfully, in line with expectations. Rather than being a problem, identifying such components helps us determine where human time is best spent, and how best to involve human expert judgement in oversight of agentic systems.

Principle 2: Be smart about cost

The research framework helps us answer a question that keeps coming up with customers: Can this particular task be handled by a smaller, cheaper model? Enterprises are increasingly attentive to cost, and rightly so.

Finding the right balance between performance and budget is tricky. Taking a complex problem, like payroll, and handing it off to a lightweight model, might not be powerful enough for the results you want. On the other hand, it’s unnecessary to route simple tasks, like reformatting a spreadsheet, to a strong reasoning model.

According to the research, an agent that is intelligent about delegation would learn to recognize these scenarios, and match each task to the right tool or endpoint, to achieve the desired result and maximum reliability at a minimum cost. Use of model routing capabilities within API gateways is becoming a popular choice among customers, in addition to the alternative for using client-side proxies (such as LiteLLM).

You can learn more about model routing here.

Principle #3: Respect sensitive data

Many workflows handle private, sensitive data, and AI agents need to respect those boundaries and permissions. For example, if you’re deploying your orchestrator agent for payroll data, you know that agent should never pass along its full set of information to a sub-agent. This not only compromises security, but also bloats the context window for agents and degrades performance. An agent should grant the absolute minimum permissions required to complete that specific assignment, and nothing more.

The challenging part arises when needing to demonstrate, according to our first principle, that work has been reliably completed, without revealing private information. According to the research , advanced cryptography can help address this, via techniques such as zero-knowledge proofs. Zero-knowledge proofs enable one AI agent to prove to the other AI agent that a planned computation was performed correctly, without revealing the data itself. For example, an agent tasked with analyzing a sensitive dataset can generate a succinct non-interactive argument of knowledge that proves a specific property of the result. This enables the delegator to instantly verify the validity of the proof.

Principles #4: Beware the zone of indifference

The zone of indifference is a term coined by Chester Barnard, an American business executive, in his 1938 book called The Function of the Executive. The zone is the space in which an employee will accept a task without questioning it. The task usually falls within their scope, so they unconsciously accept it. For example, if you’re a sales rep and your manager asks you to attend an upcoming pitch with a valued client, you probably wouldn’t push back or think too deeply about it.

As expressed in the research, current AI systems are defined by post-training safety filters and system instructions. As long as a request does not trigger a hard violation, the model complies. But when considering the emerging agentic web, this compliance might actually create a systemic risk. As mentioned in the research, “As delegation chains lengthen (? → ? → ?), a broad zone of indifference allows subtle intent mismatches or context-dependent harms to propagate rapidly downstream, with each agent acting as an unthinking router rather than a responsible actor.”

This has serious implications, because it means intelligent delegation requires “dynamic cognitive friction.” This means validating the information provided to agents to ensure that they are accurate, relevant, controlled and efficient.

This way, an agent can recognize when a request is ambiguous enough to warrant stepping outside their zone of indifference to challenge the delegator, or request human verification. Human participation and oversight similarly presume a degree of cognitive friction and active engagement, though this must be carefully managed, so as not to over-burden the users of the system. Human time is valuable and should only be invoked when necessary.

Looking ahead

At Google Cloud, our long-term goal is to integrate agents naturally and efficiently into organizations, which will mean delegating to and from human experts and respecting boundaries. Together, we believe this will deliver business value beyond what individual agents can handle.

Ready to navigate the agentic web? Read Google DeepMind’s report paper, Intelligent AI Delegation, on arXiv.

_{Note: A special thanks to Matija Franklin, Simon Osindero from Google DeepMind, and Vishal Agarwal, Andrea Morange from Google Cloud, for their contributions.}

Expanding Google Antigravity for enterprise customers

Thu, 20 Aug 2026 17:30:00 +0000

Since announcing Google Antigravity in Gemini Enterprise Agent Platform at I/O in May, we’ve heard helpful feedback from our customers. Your developers want easy access to coding agents across surfaces. Your enterprise governance team wants security controls and license management. And your finance team wants pooled usage so that no prepaid token ever goes unused. Now, everybody finally gets what they want:

Antigravity is available now as part of eligible Gemini Enterprise app subscriptions, including out-of-the-box administrative and spend controls.
New IDE extensions let developers use Antigravity in the IDEs of their choice, including VS Code.

Unify AI developer tools and enterprise-grade controls in one subscription

Equipping your developers with advanced agentic tools shouldn't mean managing separate add-on licenses, invoices, billing consoles or security settings. With AI developer tools included in Gemini Enterprise subscriptions, administrators can easily enable Antigravity and Android Studio for users with eligible Gemini Enterprise Standard, Plus, and Standard Emerging Market licenses, and maintain full governance with spend, security, observability and usage metrics consolidated in the Gemini Enterprise admin console.

Unblock your developers while controlling spend

With billing flexibility and cost management tools in Gemini Enterprise, you can ensure your developers have the resources they need while managing costs:

Granular spend thresholds: Administrators can set monthly project-level budget caps directly in the Billing console, with additional per-user and team controls rolling out later this year.
Pooled quotas: Shared token pools provide flexibility to high-demand teams, preventing purchased quota from sitting idle across the organization.
Overage enablement: To maintain continuous developer workflows when pooled quotas are met, administrators can opt into overages with monthly spend caps, smoothly transitioning excess usage to standard consumption-based rates.
Usage metrics: Centralized usage tracking provides visibility into token consumption, API calls, and developer activity, enabling organizations to continuously optimize their AI investments.

Safeguard your organization’s code and data with built-in privacy and security

Gemini Enterprise subscriptions bring Google Antigravity under Google Cloud’s standard security and compliance protections. Administrators and IT teams can set clear boundaries around workspace access, enable full audit logging, and enforce data privacy from a single console:

Configurable security policies: Enforce security and compliance controls, such as workspace sandboxing, and browser and MCP server access, to help ensure AI agents operate safely within authorized enterprise environments.
Central audit logging: Enable comprehensive audit logging with a single toggle, capturing prompts, agent responses, and metadata for compliance reporting.
Data privacy: Maintain data ownership under Google Cloud’s Terms of Service, ensuring all agent activity executes strictly within your secure cloud boundary.

Bring agentic coding directly into your team’s preferred development environments

Starting today your developers can use Antigravity across the surfaces they already know and use — including Visual Studio Code, Visual Studio (preview), Jetbrains (preview) and Zed IDEs (preview) via the new IDE extensions as well as the Antigravity 2.0 desktop app, and the Antigravity CLI.

Throughout all surfaces, administrators can enforce corporate identity standards while removing setup friction for technical teams via native support for Workforce Identity Federation (WIF) and Application Default Credentials (ADC).

AGY IDE extension demo

What our customers are saying

From rapid code generation to end-to-end task automation, Google Antigravity is giving engineering teams the momentum of cutting edge AI development backed by the stability, governance, and scale of Google Cloud. Here is how leading enterprise customers and partners are driving measurable outcomes in production:

“At AirAsia and across the Group, we’re all about empowering our people. Bringing highly capable Gemini models directly into our daily workflows with Antigravity 2.0 does exactly that. We are putting the most advanced AI capabilities into the hands of our entire workforce, from software engineering to finance, marketing, legal, HR and much more. This empowers both our developers and critical back-office teams to innovate at an unprecedented pace and drive proven time-savings across the board.” — Nikunj Shanti, CTO, AirAsia Next

“Enterprises are moving beyond AI experimentation and expecting measurable business outcomes. With Gemini Enterprise and next-generation developer tools like Antigravity 2.0 and Antigravity CLI, we see significant opportunities to further embed agentic AI, particularly the advanced reasoning capabilities of Gemini models, directly into software delivery workflows. This goes beyond productivity as it enables faster decision-making, higher code quality, and reduced technical debt at scale. What stands out is how these capabilities are helping our teams evolve from writing code to orchestrating outcomes, strengthening every phase of the software development lifecycle while scaling innovation securely and responsibly.” — Rakesh Aerath, President, Asia Pacific Global Delivery Centers of Excellence, CGI

“Every developer workflow is unique, and agentic AI should adapt to the engineer, not the other way around. With Google Antigravity supported across developers' preferred IDEs, the desktop app, and the CLI, Cognizant can seamlessly embed agentic engineering across our global delivery centers. It gives our teams the freedom to choose their preferred surface while delivering high-velocity, secure software for our clients.”— Rajesh Varrier, President, Global Operations and Chairman & Managing Director, Cognizant India

“Antigravity has played a key role in advancing our AI-first strategy at Datamatics. Over the last few months, our teams have used it to rapidly build and deploy multiple applications, accelerate solution development, and embed AI into core business processes. From AI Impact Hub to analytics and sales enablement solutions, it has helped us move beyond experimentation to real execution, delivering measurable business outcomes while enabling teams to innovate faster and at scale.”— Vijay Venkatachalam, Vice President, Information Systems Group (ISG), Datamatics

"Embedding Google Antigravity's autonomous capabilities directly into Gemini Enterprise development environments allows our internal and Forward Deployed Engineering teams to automate complex tasks. Supported by the platform’s new FinOps and governance controls, our teams can confidently focus on orchestrating high-value, secure and cost-efficient outcomes for our clients at scale." — Faruk Muratovic, US AI & Engineering Strategy and Services Leader, Deloitte

“Adopting Antigravity places Wipro at the leading edge of the AI-driven software development lifecycle. Combining Antigravity 2.0, CLI, and the new IDE extensions with a seamless developer experience and superior code accuracy fits naturally into our AI-first engineering strategy. Working with Google Cloud allows us to accelerate software delivery and bring next-generation value to our global enterprise clients.” — Debashish Ghosh, Vice President and Global Head, Google Partnership, Wipro

Get started with Antigravity in Gemini Enterprise

Google Antigravity in Gemini Enterprise is available today for eligible Gemini Enterprise Standard, Plus, and Standard Emerging Market licenses, with broader support coming soon.

For administrators: Visit the enterprise setup guide to enable AI Developer tools for Google Antigravity and Android Studio.
For developers: Start building with Antigravity 2.0, the Antigravity CLI, or your preferred IDEs via Antigravity IDE extensions.

How AlloyDB ScaNN scales vector search to 10 billion vectors

Thu, 20 Aug 2026 16:00:00 +0000

To satisfy the demands of enterprise-grade agentic AI applications, underlying vector databases often struggle to scale effectively as modern use cases can scale to billions of vectors.

As a fully managed PostgreSQL-compatible database service, AlloyDB is engineered to handle demanding enterprise workloads. Combining Google's infrastructure with the reliability of commercial databases, it delivers high availability, scalability, and includes a cutting-edge analytical engine, optimal for agentic AI use cases. A key part of this is its ScaNN index, which now operates efficiently at a scale of 10 billion vectors. This was achieved through a major architectural enhancement: an innovative four-level tree (preview) paired with efficient memory usage.

The 10 billion vector scale challenge

Scaling to a 10 billion vector workload presents significant memory and computational challenges. Previous AlloyDB ScaNN tree-based index was limited to two- or three-level tree configurations, and attempting to scale those structures led to several bottlenecks:

Increased compute intensity: Larger tree structures demand significantly more operations for both index construction and query traversal.
Memory constraints: The sampling processes required for 10 billion vectors can easily exceed the system's available memory capacity.

Solution: Four-level architecture

The introduction of a four-level tree (preview) is the primary innovation in the recent AlloyDB ScaNN release. This architecture, illustrated in Figure 1, employs a top-down strategy to optimize the balance between accuracy and build efficiency. To maintain high performance and mitigate recall loss, the system integrates key enhancements such as Top-K branch, SOAR, centroid adjustment and balanced tree shape.

Figure 1. AlloyDB ScaNN four-level tree architecture

This design has two primary benefits:

1. Reduced compute intensity via hierarchical partitioning

The four-level architecture drastically reduces compute intensity by using hierarchical partitioning to restrict the volume of vectors scanned during a query. Instead of traversing a flat or poorly segmented space, the multi-layered hierarchy narrows down the search path exponentially. Figure 2 illustrates the search spaces across different tree levels, demonstrating how structural layering optimizes traversal efficiency:

Figure 2. Search space for two-, three- and four-level trees

Two-level: Utilizes coarse partitioning to guide queries, resulting in a basic search complexity of O(N^1/2).
Three-level: Introduces an intermediate layer to further subdivide clusters, narrowing exploration to O(N^1/3).
Four-level: Implements refined, highly granular partitions that optimize traversal efficiency down to O(N^1/4), sufficiently allowing for more than 10-billion vectors.

By dynamically expanding hierarchical layers as the dataset expands, AlloyDB ScaNN maintains ultra-low query latency and avoids computational scale walls from impacting performance.

2. Efficient memory usage

Achieving a 10 billion vector scale requires high memory efficiency. AlloyDB ScaNN uses these strategies to maximize memory management performance:

Balanced tree shape construction: The four-level tree utilizes a balanced configuration to circumvent memory limitations that restrict the size of training datasets. This balanced architecture effectively leverages reduced sampling sizes to construct high-fidelity tree partitions.
Sampling optimization: When the system encounters memory limitations, it generates a condensed sampling set that considers performance and accuracy.

Performance test results

By leveraging the innovative four-level tree architecture in our internal tests, we are able to achieve the following performance results:

AlloyDB can scale to over 10 billion vectors with its ScaNN index.
AlloyDB can deliver <= 51 ms p95 latency and 95% recall at 10 billion vectors with its ScaNN index.

Get started today

Experience AlloyDB ScaNN's four-level tree (preview) architecture today. You can deploy ScaNN for AlloyDB by following our quickstart guide to set up an instance. For optimized, high-speed vector search, refer to the official ScaNN documentation. New users can also explore AlloyDB through our 30-day free trial program. We can’t wait to hear about what you build!

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