Amazon SageMaker Feature Store vs Vertex AI Feature Store
On the evidence we track, Amazon SageMaker Feature Store leads this comparison with a composite score of 61/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
Capabilities
Feature-by-feature on the axes that matter for feature stores. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Amazon SageMaker Feature Store
LeaderA unified storage system that enables organizations to manage machine learning features for both training and real-time inference. It supports separate offline storage for batch training and online storage for real-time predictions, while providing access controls, feature discovery, and synchronization across teams.
Vertex AI Feature Store
Vertex AI Feature Store provides centralized feature management capabilities by layering metadata and online serving functionality on top of BigQuery datasets. It enables teams to store, discover, share, and serve ML features without duplicating data, while supporting embeddings and vector similarity searches.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Amazon SageMaker Feature Store
LeaderUsage-based pricing with per-second compute billing. Free tier includes 250 hours on ml.t3.medium for 2 months. Savings Plans available for committed usage.
- On-DemandPay per second used, no commitment required
- Per-second compute billing
- Real-time inference
- Batch transform
- Feature monitoring
- Data lineage
- Savings PlansUp to 64% discount with usage commitment
- Up to 64% savings versus on-demand
- Flexible across instance families and regions
- Automatic application to eligible usage
Vertex AI Feature Store
Pricing not documented yet.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
Amazon SageMaker Feature Store
Leader- Apache Spark
- Amazon S3
- Amazon Redshift
- AWS Lake Formation
- Snowflake
- Databricks Delta Lake
- AWS CloudWatch
- AWS Glue Data Catalog
Vertex AI Feature Store
- BigQuery
- Knowledge Catalog
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.