Google Cloud – AI & ML

Every organization has unique requirements for building and scaling generative AI. At Google Cloud, we are committed to giving developers model choice and architectural flexibility to run workloads however they need. Today, we’re announcing the general availability of an Anthropic-managed deployment option for Claude Models on Google Cloud. This gives teams more choice in how they run their models, helping them consolidate AI workloads on Google Cloud alongside our existingGoogle-managed deployment option. Both options deliver the same frontier models on day zero, the same pricing, and the sam

Google Cloud has always been defined by choice, enterprise-grade governance, and market-leading price-performance. Key to the foundation of openness and choice is giving customers flexibility to run agentic workloads across their choice of models and infrastructure to achieve the right balance of cost, capability, and performance. Today, we are expanding that foundation by launching OpenAI models in Google Cloud’s model garden. Google Cloud is now the only hyperscale cloud provider offering access to all three leading closed source LLMs, alongside more than 200 first-party, third-party, and op

Whether you are iterating on a quick bug fix or planning a complex migration, different tasks call for different models. In keeping with Google Cloud's commitment to an open ecosystem and to give developers the right model for every job, Claude Opus 5.5 and Claude Sonnet 5.5 are now available in Google Antigravity for Google Cloud customers. Claude Opus 5.5 is built for complex work while Claude Sonnet 5.5 is a faster, lower-cost complement. Once enabled by an administrator, developers can select Claude Opus 5.5 and Sonnet 5.5 directly from the model picker. Administrators can also select Goog

Earlier this month at Gemini at Work, we introduced the Gemini agent: a single, universal agent for work. It answers your questions, handles your knowledge work, creates your images and media, and writes and runs code. You give it objectives, not instructions, and come back to finished work. It plans the work, uses skills and tools, connects to your systems, and has the security, administration, and governance your organization requires. Every industry runs on knowledge a general model does not have. Government work runs on statute, regulation, and agency guidance, across siloed legacy systems

Imagining traditional gearheads getting their hands on software-defined vehicles is an amusing thought experiment. With their socket wrenches and air compressors at the ready, they might be confused to encounter core functions more likely to run on 1s and 0s than oil and belts. But across the auto industry these days, more and more vehicles feature prominent systems built using cloud-native development, continuous integration/continuous delivery, fast digital iteration, and plenty of AI. But there’s still a big gap to be found in many automakers’ and Tier 1 suppliers’ cloud-based development p

Is your LLM-as-a-judge truly objective? There’s a common misconception in tech that humans are subjective, so we should rely on AI for objective evaluation. But the reality is that LLMs are also subjective. Just like human reviewers, they can carry biases: Verbosity bias: LLM judges usually favor longer, confident-sounding answers, even when the underlying facts are wrong. Position bias: Swapping the order of two options can cause an LLM judge to flip its verdict over 75% of the time. The halo effect: Because LLMs generate text piece by piece, clean grammar and formatting can trick an AI judge

As organizations work to leverage AI to transform their businesses, it is clear that the raw model intelligence is not the key challenge. Consistently, we hear that the challenges lie in how the agents can be deployed across the company while meeting key requirements for quality, integration, security, and governance. Gemini provides that connected foundation across the enterprise, and it’s why we believe Google was named a Leader in the inaugural IDC MarketScape: Worldwide Agentic AI Platforms 2026 Vendor Assessment1. IDC MarketScape vendor analysis model is designed to provide an overview of

The real challenge of building an agentic system isn't the construction, it's the measurement. Evaluation engineering is a critical foundation for building reliable AI systems. Setting up systematic, repeatable tests to measure your agent’s true behavior can be the difference between shipping a tool you can trust and one that silently breaks, fails to call APIs, or makes mistakes. I’ve run evaluation engineering initiatives for years at Google and developing the newly released Google Cloud Developer plugin served as a live testbed to stress-test these key principles across real-world developer

In production systems today, most AI agents operate as lightweight dispatchers. They parse a prompt, make an API call, retrieve a record, and hand back control. Their execution is fast, stateless and cheap. Biotechnology does not work this way. Folding a protein target, screening a 50,000 compound ligand library, or evaluating structure-activity relationships (SAR) are not quick API lookups. They are sustained, stateful, GPU-bound simulations that run for minutes or even hours across specialized silicon. When you ask a general-purpose large language model (LLM) endpoint to coordinate a complex

Editor's note: Generative AI startups are moving rapidly beyond static clip generation to build interactive, real-time worlds. In this technical deep dive, we explore how AI startup Visko collaborated with Google Cloud TPU engineers to deploy Orbis — its continuously streaming Live World Model—on Cloud TPUs, going from initial kickoff to production handover in just 30 days. What makes a live video world model so challenging to serve in production? The bottleneck isn't average throughput — it's beating a relentless real-time clock. Unlike offline video generators where a user submits a prompt a

Small teams, short runway, and constant pressure to ship faster — it’s the reality we see every day among the founders we work with at Google for Startups. When you are building a company from the ground up, every hour spent on operational overhead or infrastructure plumbing is an hour taken away from your core product. Last week at Gemini at Work 2026, we introduced the Gemini agent: a single, universal agent designed to work both as your personal assistant and as a dedicated member of your team. And this week, we made our new frontier model, Gemini 4 Argon, widely available — trained to main

The generative AI functions in BigQuery (AI.GENERATE, AI.IF, AI.CLASSIFY, AI.SCORE, and more) bring large language models (LLMs) directly into your SQL, so a single query can classify millions of support tickets or score each product review for sentiment. However, because these functions are billed by the token, scaling them across massive datasets highlights the importance of managing your budget proactively. We recently announced the general availability (GA) of token-based cost management for BigQuery generative AI functions: daily quotas that let administrators set self-serve guardrails on

Last week we announced that Gemini Enterprise has evolved to make it even simpler to get work done, and introduced the Gemini agent, your new universal agent for work. It's always on and ready to answer your questions. It will write code, complete longer and more complex tasks, or perform whatever you need to get the job done, all from a single prompt box. And model intelligence underpins it all. To enable a new frontier of work, we are making our new frontier model, Gemini 4 Argon, available on Google Cloud. We trained Argon to work with both depth and precision, which allows it to maintain c

Editor’s note: This article is adapted from Thomas Kurian’s keynote address at Gemini at Work 2026. It includes a number of announcements: Gemini, a single, universal agent for work that answers your questions, handles your knowledge work, creates your images and media, and writes and runs code. Inline in Workspace, Gemini works directly inside Gmail, Drive, Docs, Slides, Sheets, Chat, and Calendar, carrying the same memory, skills, and controls it has everywhere else. New data and analytics skills that allow both technical teams and everyday business users to use plain-language questions to g

In recent decades, Ireland has grown into a vibrant hub for global technology. As modernization accelerates, Irish organizations — spanning government agencies, established enterprise brands, and growing startups in AI and other areas — are all advancing their technical foundations. Today, leaders across the country are injecting agentic AI into every layer of their business and operational workflows. We are seeing organizations move from early prototypes to production-ready deployments using Gemini Enterprise. This progress supports Ireland's competitive position within the European market. A

We hereby declare September to be scalability month! As the world prepares for a surge of agentic fleets, we are shoring up our AI infrastructure and orchestration offerings to gracefully — and quickly — respond to that demand, all while maintaining workload isolation and security, and keeping costs in check. Read on to learn how these enhancements manifest across Google Cloud’s compute, network, storage, and orchestration offerings, plus new ways customers are using Google Cloud AI infrastructure, and third-party industry validation of our strategy. Product, technology, and tools updates Goog

Today, we’re expanding our ecosystem of managed remote MCP servers by introducing the Google Cloud CLI remote MCP server in preview. Powered by the popular gcloud and bq (BigQuery) command-line tools, this new server gives AI agents immediate, broad access to command-line operations for managing Google Cloud infrastructure and working with advanced BigQuery workflows securely and seamlessly. Why CLI matters for AI agents Agents are increasingly performing complex cloud operations, but standardizing how they interact with backend systems remains a challenge. The Google Cloud CLI remote MCP serv

Every week, I talk with founders who are building at an unbelievable pace. Teams are moving from inception to product-market fit faster than ever, with foundation models wired deeply into their core product workflows. Yet as startup architectures mature, a clear divide has emerged between teams struggling with margins and those scaling sustainably. The most effective engineering teams have abandoned the one-size-fits-all model strategy. In the early days of LLMs the default architecture was simple: send every interaction to the largest model available. But as applications move into production,

Reinforcement learning (RL) has been a keystone of modern LLM post-training, but it demands large training clusters and access to model internals that external customers can't have with proprietary models like Gemini. So here at Google Cloud, we packaged it into a managed RL fine-tuning service (RLFT service) — you bring prompts and a reward function; we handle the infrastructure and the proprietary model internals. Now, you can adapt Gemini with the service — teaching the model from a reward signal you define, rather than from a fixed set of labeled answers. This unlocks a class of problems t

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 interac