Google Vertex AI
Train, deploy, and run inference on Gemini and 200+ third-party foundation models, plus build AI agents, billed per token and per compute node-hour.
The verdict on Google Vertex AI: Google Cloud teams that want foundation model access and custom ML training under one managed platform Vertex AI is the natural choice for teams already running on Google Cloud who need foundation model access and custom ML training without data crossing cloud boundaries. Pricing: Pay-as-you-go, Gemini 2.5 Flash-Lite from $0.10/M tokens. Last reviewed: August 2026.
Best For
Google Cloud teams that want foundation model access and custom ML training under one managed platform
Standout Feature
Native GCP integration means no cross-cloud data movement for teams already running on Google's infrastructure
TL;DR
The natural choice for a Google Cloud shop, configuring it well requires real GCP expertise on the team.
Alternatives
Overview
Google Vertex AI is the unified ML platform on Google Cloud, it brings together model training, deployment, feature engineering, MLOps, and foundation model access (Gemini, Imagen, Codey) under one managed service with Google's data infrastructure underneath. Data science teams at Google Cloud shops use it to build and deploy both custom ML models and generative AI applications without managing separate infrastructure for each. The Vertex AI Studio lets you prototype with foundation models through a UI; the API and SDK handle production deployment. Integrates natively with BigQuery, Cloud Storage, and other GCP services.
Our Take
Native GCP integration means Gemini, Imagen, and third-party models sit alongside your existing pipelines, eliminating cross-cloud data movement. Pricing complexity across compute, storage, and model calls requires careful planning, and configuring the platform well demands real GCP expertise. If your data science team lives in GCP and needs model access plus MLOps tooling in one managed platform, this fits. For smaller teams or non-GCP shops, the setup cost outweighs the convenience.
Key Features
- Gemini and Imagen access
- Model training
- AutoML
- Feature Store
- Model monitoring
- BigQuery integration
- • Native GCP integration means no cross-cloud data movement for Google Cloud teams
- • Foundation model access + custom ML training in one platform
- • AutoML reduces time-to-deployment for teams without deep ML expertise
- • Google Cloud knowledge required to configure effectively
- • Pricing complexity across compute, storage, and model calls
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Workflows Using This Tool
Step-by-step playbooks that put Google Vertex AI to work.