Snowflake Feature Store
Snowflake Feature Store helps data scientists and machine-learning engineers create, maintain, and use ML features within Snowflake.
What Snowflake Feature Store is
Snowflake Feature Store helps data scientists and machine-learning engineers create, maintain, and use ML features within Snowflake. It handles batch and streaming data, updating features as new data arrives, and supports backfill and point-in-time-correct features through ASOF JOIN. Teams can write feature transformations in Python or SQL. Snowflake-managed feature views refresh incrementally on a schedule; external views are maintained by another process, such as dbt. The service integrates with Snowflake Model Registry and supports user-managed dbt pipelines. ML Lineage can trace data from its source through features and datasets to a trained model, and is created automatically when the Feature Store is used. Snowsight offers a UI for searching and discovering features, while the Python API can be used in a local IDE or a Snowsight worksheet or notebook. Data remains under Snowflake governance with fine-grained role-based access control. Pricing is consumption-based, with no plan prices listed. Feature-view versions have a 128-character maximum; Postgres-backed online serving limits the combined name and version to 46 characters.
Good
- Supports batch and streaming data
- Python and SQL feature transformations
- Automatic ML lineage tracing
- Role-based access controls govern data
Watch out
- Consumption-based pricing has no listed prices
- Feature-view versions cap at 128 characters
- Postgres-backed online names and versions cap at 46 characters
- Quickstarts are examples without an SLA guarantee
Who it suits
It suits data scientists and ML engineers who build or maintain features in Snowflake. Teams using Python, SQL, or dbt can work with the corresponding transformation and pipeline options.
Verdict
Snowflake Feature Store brings feature creation, refresh, lineage, and discovery into the Snowflake environment. Consider its naming limits and consumption-based cost model when assessing fit.
Plans and prices
From Snowflake Feature Store's own pricing page, checked 1 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Business Critical | Priced on request | all Enterprise features · Tri-Secret Secure · private connectivity · failover and failback · consumption-based |
| Enterprise | Priced on request | all Standard features · multi-cluster compute · granular governance and privacy controls · extended Time Travel · consumption-based |
| Standard | Priced on request | core platform functionality · Snowpark · data sharing · optimized storage and Time Travel · consumption-based |
| Virtual Private Snowflake | Priced on request | all Business Critical features · completely separate isolated Snowflake environment · consumption-based |
The facts
Each one read from Snowflake Feature Store's own pages, with the date it was checked.
Where it runs
- API platforms
- The Snowflake Feature Store Python API is part of the snowflake-ml-python package and can be used in a local Python IDE or in a Snowsight worksheet or notebook.checked 1 Oct 2026
What it does
- Compliance
- Snowflake lists global certifications and attestations including ISO 27001, ISO 27017, ISO 27018, SOC 1 Type II, and SOC 2 Type II.checked 1 Oct 2026
- Data support
- It supports batch and streaming data with automatic updates as new data arrives.checked 1 Oct 2026
- Examples and support
- Snowflake quickstarts are examples only and are not guaranteed for accuracy or covered by a Snowflake Service Level Agreement.checked 1 Oct 2026
- Feature views
- Snowflake-managed feature views refresh incrementally on a schedule, while external feature views are maintained by another process such as dbt.checked 1 Oct 2026
- Integrations
- The Feature Store integrates with Snowflake Model Registry and supports user-managed pipelines with dbt.checked 1 Oct 2026
- Limits
- Feature view versions have a maximum length of 128 characters, and Postgres-backed online serving limits combined feature-view name and version length to 46 characters.checked 1 Oct 2026
- Lineage
- ML Lineage can trace data flow from source to feature to dataset to trained model and is automatically created when the Feature Store is used.checked 1 Oct 2026
- Point-in-time features
- It supports backfill and point-in-time-correct features using ASOF JOIN.checked 1 Oct 2026
- Purpose
- Snowflake Feature Store lets data scientists and ML engineers create, maintain, and use machine-learning features within Snowflake.checked 1 Oct 2026
- Security
- Data remains under Snowflake governance and does not leave Snowflake, with access managed through fine-grained role-based access control.checked 1 Oct 2026
- Transformations
- Feature transformations can be authored in Python or SQL.checked 1 Oct 2026
- User interface
- Snowsight provides a Feature Store UI for searching and discovering features.checked 1 Oct 2026
Money
- Cost model
- Snowflake-managed feature views use dynamic tables, while external feature views use views and incur no additional storage cost.checked 1 Oct 2026
The company
- Founded
- 2012checked 28 Sep 2026
- Headquarters
- Menlo Park, California, United Stateschecked 28 Sep 2026
