Data science & ML platforms · Google

Google Vertex AI

Google Cloud's unified platform for AutoML, custom model training, and access to foundation models.

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Vertex AI is Google Cloud's managed machine learning platform, combining AutoML tooling, custom training and tuning, and a Model Garden of foundation models (including Gemini and third-party models) under one interface. It provides Pipelines for MLOps orchestration, a Feature Store, Vector Search for embeddings-based retrieval, and generative AI tooling for prompt design and fine-tuning. Vertex AI is deployed exclusively as a managed Google Cloud service and is billed per resource consumed — training compute, prediction endpoints, storage, and API calls — rather than a flat subscription. It is most natural for teams already using BigQuery and other Google Cloud services, since data movement and identity/access controls are shared across the platform.

At a glance

Vendor Google
Pricing model Usage-based
Free tier —
Deployment Cloud
Open source No
Best for Teams on Google Cloud wanting unified access to AutoML, custom training, and foundation models.

Pricing

Consumption-based pricing across training, prediction, and storage; the vendor pricing page could not be fully retrieved for specific figures.

Pricing has not been verified yet — see the vendor's site.

Features

  • AutoML for tabular, vision, and text data
  • Custom model training and hyperparameter tuning
  • Model Garden of foundation models (Gemini and third-party)
  • Vertex AI Pipelines for MLOps orchestration
  • Feature Store and Vector Search
  • Generative AI Studio for prompt design and tuning
  • Model Monitoring for drift and skew detection

Integrations

Profile last reviewed September 21, 2026

Alternatives

Amazon SageMaker AWS's fully managed service for building, training, and deploying machine learning models at cloud scale. Azure Machine Learning Microsoft's cloud platform for building, training, and operationalizing machine learning and generative AI models. Dataiku Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists.

Google Vertex AI in the index now

Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Predictive analytics Analytics that uses historical data and statistical models to estimate what is likely to happen next. Natural language query Asking a data question in plain language and getting an answer without writing code or a query language. AutoML (automated machine learning) Tooling that automates parts of building a machine learning model, such as feature selection, algorithm choice and hyperparameter tuning. Feature store A centralized system that stores, versions and serves machine learning features so training and live prediction use identical definitions. Hyperparameter tuning The process of finding the settings that control how a model learns, such as learning rate or tree depth, rather than what it learns.

Related guides

How to choose a data science & ML platform The decisions that matter are who builds the models, how automated the modeling should be, and whether you want a cloud-native platform or a portable one.