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
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