Google Cloud platform for training, tuning and serving models
Vertex AI is Google Cloud's managed platform for the whole model lifecycle: training, tuning, evaluating and deploying models, plus access to Gemini and third-party models through one API surface. Teams use it when they already run on Google Cloud and want model endpoints, pipelines, feature storage and monitoring wired into existing identity and billing. The differentiator is the breadth inside a single console - AutoML for teams without ML engineers, Model Garden for ready-made and open models, and Vertex AI Search for retrieval over private documents. Pricing is consumption-based, so cost discipline matters from the first experiment.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
enterprise ml pipelines, model training and tuning, gemini deployments, retrieval over private data
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It is Google Cloud's platform for training, tuning and deploying machine-learning models, including Gemini. Teams use it for managed endpoints, AutoML on structured data, and retrieval over private documents through Vertex AI Search.
Not always. AutoML covers common tabular, image and text tasks without custom code. Custom training, pipelines and evaluation still assume real ML engineering skill, and the platform is not aimed at casual users.
Some products carry free monthly allowances and new accounts get trial credit, but most Vertex AI services bill by consumption. Set budgets and quotas early, because costs scale with training hours and endpoint uptime.
Developer console and API keys for the Claude model family
Node-based local interface for running image and video diffusion models
Browser playground for prompting and prototyping with Gemini models
Hosted Jupyter notebooks with optional free GPU and TPU runtimes