What Vertex AI is
Google Cloud Vertex AI is a managed platform that gives businesses access to powerful AI models without having to build or run the underlying infrastructure themselves. Think of it like a well-stocked marketplace: instead of training your own AI from scratch, you pick a model, plug it into your application, and Google Cloud handles the servers, scaling, and security behind the scenes.
What makes Vertex AI notable is who it hosts. It's not just Google's own models — it has become a major distribution point for third-party frontier AI, including Anthropic's Claude family, Mistral AI's models, and Meta's Llama 3.1 405B.
Why it matters to you
If your organization already runs on Google Cloud, Vertex AI means you can access the same cutting-edge AI models that power consumer apps — without moving your data to a new vendor or renegotiating your security setup. Your data stays inside your existing Google Cloud environment, with the compliance and governance controls you already have in place.
That's a big deal for regulated industries like financial services, healthcare, and telecommunications, where data residency and audit trails aren't optional.
What you can actually do with it
The models available on Vertex AI cover a wide range of tasks:
- Writing and reasoning — Claude models handle everything from drafting documents to answering complex questions.
- Coding — Claude Opus 4.7 and Claude Code support advanced software engineering and autonomous coding tasks.
- Computer use — Claude's "computer use" capability, which lets AI control a computer by viewing screens and clicking, launched on Vertex AI alongside the Anthropic API and Amazon Bedrock.
- Multilingual and multimodal work — Mistral Small 3.1 (available on Vertex AI under an open Apache 2.0 license) handles text, images, and over a dozen languages on modest hardware.
- Code generation — Mistral's Codestral model, also on Vertex AI, specializes in fill-in-the-middle coding tasks across 80+ programming languages.
The models available
From the events in this bundle, Vertex AI hosts:
- Anthropic Claude: from Claude 3 Haiku (the fast, affordable tier) through Claude 3.5 Sonnet, Claude Opus 4, and Claude Opus 4.7 — the latest release, which adds cybersecurity safeguards and improved vision capabilities.
- Mistral AI: Mistral Large 2 (123B parameters, 128k context), Mistral Small 3.1 (multimodal, open-weight), and Codestral 25.01 (coding specialist).
- Meta Llama 3.1 405B: available via a Hugging Face deployment guide for enterprises wanting to run the large open-weights model in a managed environment.
The infrastructure story
Vertex AI's depth as an AI platform is backed by serious infrastructure investment. Anthropic signed a multi-gigawatt TPU compute agreement with Google and Broadcom, with capacity expected to come online starting in 2027 — one of the largest compute commitments in the industry. This means the Google Cloud relationship isn't just a distribution deal; it's woven into how Anthropic trains and scales its models.
Who's using it
Early adopters cited in the events include Quora's Poe app, which reports millions of daily messages via Claude-based bots on Vertex AI. The Snowflake–Anthropic $200M partnership routes Claude model access through Vertex AI (alongside Amazon Bedrock and Microsoft Azure) to serve Snowflake's 12,600+ enterprise customers. Anthropic's Japan operations also list Google Cloud Vertex as a key platform partner.
Where it fits in the landscape
Vertex AI sits alongside Amazon Bedrock and Microsoft Azure AI Foundry as one of the three major cloud homes for enterprise AI. All three now offer access to Claude models. Amazon Bedrock remains Anthropic's primary cloud and training partner; Microsoft Azure has a $30B compute commitment with Anthropic. Vertex AI's differentiator is the Google Cloud ecosystem — TPU infrastructure, existing enterprise relationships, and Google's own model research — combined with the breadth of third-party models it hosts.
For practitioners choosing between platforms, the decision often comes down to where your data already lives and which cloud your organization has standardized on, rather than which models are available — since the major frontier models now appear on all three.
