Feature stores · Google Cloud

Vertex AI Feature Store

Managed feature-serving module inside Vertex AI that serves ML features from BigQuery data for training and low-latency inference.

Visit Vertex AI Feature Store Alternatives

Vertex AI Feature Store is Google Cloud's feature-management module rather than an independently sold product. It centers on BigQuery: feature data is managed directly in BigQuery tables or views, and a feature view materializes a logical selection of that data from BigQuery into an online store for low-latency serving at inference time, while bulk feature retrieval for training reads BigQuery directly. This BigQuery-first design differs from Databricks' Delta-table approach and SageMaker's dedicated online/offline stores, letting teams reuse existing BigQuery governance and pipelines rather than duplicating data into a separate system. It supports feature discovery and versioning within the Vertex AI ecosystem so pipelines built with Vertex AI Pipelines or Workbench can consume the same feature definitions used for serving. It is only available inside Google Cloud and is billed as Vertex AI and BigQuery usage rather than as a separate line item.

At a glance

Vendor Google Cloud
Pricing model Usage-based
Free tier —
Deployment Cloud
Open source No
Best for Teams already centered on BigQuery who want feature serving without moving data into a separate store.

Pricing

Not sold separately: billed as Vertex AI online-store serving and BigQuery storage/query usage within a Google Cloud account.

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

Features

  • Feature data managed directly in BigQuery tables and views
  • Feature views materialize BigQuery data to an online store for serving
  • Low-latency online serving for real-time inference
  • Bulk feature retrieval from BigQuery for training
  • Feature discovery, search and versioning
  • Integration with Vertex AI Pipelines and Workbench

Integrations

Profile last reviewed September 21, 2026

Alternatives

Chalk Real-time feature platform that computes and serves ML features from Python definitions, deployed inside the customer's own cloud. Databricks Feature Store Feature registry and serving layer built into Databricks Unity Catalog, using Delta tables as feature tables. Amazon SageMaker Feature Store Fully managed feature repository inside SageMaker that stores, discovers and serves ML features for training and inference.

Vertex AI Feature Store 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. Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions. Feature store A centralized system that stores, versions and serves machine learning features so training and live prediction use identical definitions.

Related guides

How to choose a feature store The real choice is whether your cloud or warehouse already has one built in, and whether you need sub-millisecond serving or just a shared registry.