What AI models are available on Google Vertex AI?

The platform bundles the Gemini model family (2.5 Pro, Flash, Nano), Imagen for images, Veo for video, and Chirp for speech. It also offers a Model Garden with over 200 first-party and open-source models, including Llama, Mistral, and Claude via partner endpoints.

How does Vertex AI support enterprise security and compliance?

Everything runs inside a customer's Google Cloud project, applying VPC-SC perimeters, CMEK encryption, private endpoints, and data residency in over 30 regions. Audit logging to Cloud Logging is also automatically applied.

What tools are provided for building AI agents?

Vertex AI Agent Builder includes a no-code Conversational Agents console, a code-first Agent Development Kit (ADK) with Python and Java SDKs, and an Agent Engine runtime for long-running stateful sessions. It also supports native tool-use with function calling and code execution.

What are the main drawbacks of using Google Vertex AI?

The platform has a steep learning curve regarding IAM, service accounts, and quotas. Pricing can be opaque until fully built out, and the solution locks users into Google Cloud, requiring abstraction for multi-cloud portability.

Google Vertex AI

✓ Editorially verified

Google Cloud's unified platform for building, deploying, and scaling enterprise AI agents and models.

Paid· Image Data - Training (Classification): $3.465 / 1 hour · Image Data - Training (Object Detection): $3.465 / 1 hour · Image Data - Deployment and Online Prediction: $1.375 / 1 hour · Image Data - Batch Prediction: $2.222 / 1 hour · Tabular Data - Training (Classification/Regression): $21.252 / 1 hourAgentsGemini 2.5 (Pro/Flash/Nano), Imagen, Veo, Chirp, plus Model Garden (Llama, Mistral, Claude via partner)8.6 / 10

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In short

Google Vertex AI provides an end-to-end managed environment for developing production-grade AI agents and models. It is best suited for enterprise engineering teams on Google Cloud requiring strict compliance, data residency, and deep integration with BigQuery and Workspace.

Best for

Enterprise engineering teams already on Google Cloud who need production-grade agents, RAG, or fine-tuned Gemini with strict security, compliance, and data-residency requirements.

Skip if

Solo developers, hobbyists, or early-stage startups who want the fastest path from prompt to prototype — the consumer Gemini API, OpenAI, or Anthropic are lighter weight.

Google Vertex AI is Google Cloud's end-to-end managed platform for building, deploying, and scaling AI applications, and in 2026 it is the anchor product behind the Gemini Enterprise Agent Platform. It bundles the Gemini model family (2.5 Pro, Flash, Nano), Imagen for images, Veo for video, Chirp for speech, and a Model Garden of 200+ first-party and open-source models (Llama, Mistral, Claude via partner endpoints, Stable Diffusion) behind one API, IAM boundary, and billing surface. For teams building agents, Vertex AI Agent Builder provides a full stack: a no-code Conversational Agents console, the code-first Agent Development Kit (ADK) with Python/Java SDKs, an Agent Engine runtime for long-running stateful sessions, Vertex AI Search for enterprise-grade RAG over Cloud Storage, BigQuery, and third-party connectors, and native tool-use with function calling, code execution, and Google Search grounding. Data scientists get the classic Vertex AI Workbench, Pipelines, Feature Store, Model Registry, and TPU/GPU training for custom models and supervised or reinforcement fine-tuning of Gemini. Everything lives inside a customer's Google Cloud project, so VPC-SC perimeters, CMEK encryption, private endpoints, data residency in 30+ regions, and audit logging to Cloud Logging apply automatically — the reason regulated enterprises pick it over the consumer Gemini API. Common workflows include RAG chatbots grounded on internal docs, customer-service agents that call CRM tools, batch document extraction, code-generation copilots deployed inside Workspace, and multi-agent orchestration where a supervisor agent dispatches to specialist sub-agents via ADK.

Editor's take

Vertex AI is the most enterprise-serious of the hyperscaler AI platforms — if your data already lives in BigQuery and you need auditable agents behind a VPC-SC perimeter, nothing else comes close. Just budget real engineering time for the GCP learning curve; the platform rewards teams that commit to it and punishes tourists.

— The AI Tool Bible editorial team

Pros

✅ Deep integration with BigQuery, Cloud Storage, and Google Workspace makes enterprise RAG straightforward
✅ Model Garden gives one API surface for Gemini, open-source Llama/Mistral, and partner models like Claude
✅ Agent Development Kit (ADK) is a genuinely capable code-first framework with multi-agent orchestration
✅ Enterprise controls (VPC-SC, CMEK, data residency, private endpoints, audit logs) are best-in-class
✅ Gemini 2.5 models offer very long context windows (1M+ tokens) at competitive per-token pricing
✅ Vertex AI Search handles chunking, embeddings, and hybrid retrieval as a managed service
✅ TPU access for training and fine-tuning is a real cost advantage over GPU-only clouds

Cons

⚠️ Steep learning curve — IAM, service accounts, quotas, and regional endpoints trip up newcomers
⚠️ Console UX is fragmented across Vertex AI Studio, Agent Builder, and legacy AI Platform screens
⚠️ Pricing is opaque until you build it out; egress and vector-search costs surprise teams
⚠️ Locks you into Google Cloud; multi-cloud portability requires wrapping everything in your own abstraction
⚠️ Third-party models (Claude, Llama) often lag the vendor's own API on latest versions and features

Use cases

Enterprise RAG chatbotMulti-agent customer serviceDocument extraction at scaleFine-tuning Gemini on proprietary dataCode generation copilotBigQuery natural-language analyticsVector search over Cloud StorageBatch content moderationLong-context legal reviewVoice agents with Chirp

Frequently asked

What AI models are available on Google Vertex AI?: The platform bundles the Gemini model family (2.5 Pro, Flash, Nano), Imagen for images, Veo for video, and Chirp for speech. It also offers a Model Garden with over 200 first-party and open-source models, including Llama, Mistral, and Claude via partner endpoints.
How does Vertex AI support enterprise security and compliance?: Everything runs inside a customer's Google Cloud project, applying VPC-SC perimeters, CMEK encryption, private endpoints, and data residency in over 30 regions. Audit logging to Cloud Logging is also automatically applied.
What tools are provided for building AI agents?: Vertex AI Agent Builder includes a no-code Conversational Agents console, a code-first Agent Development Kit (ADK) with Python and Java SDKs, and an Agent Engine runtime for long-running stateful sessions. It also supports native tool-use with function calling and code execution.
Is Google Vertex AI suitable for solo developers or startups?: It is not recommended for solo developers, hobbyists, or early-stage startups seeking the fastest path from prompt to prototype. The consumer Gemini API, OpenAI, or Anthropic are considered lighter weight options for these groups.
What are the main drawbacks of using Google Vertex AI?: The platform has a steep learning curve regarding IAM, service accounts, and quotas. Pricing can be opaque until fully built out, and the solution locks users into Google Cloud, requiring abstraction for multi-cloud portability.