Feast
Feast: A broad, flexible feature store for teams managing training data and online inference. Ranked #2 of 20 in Feature Store Software by our editors (9.3/10); pricing: Open source; best for teams wanting a broad open-source feature store.
At a glance
- Editor score9.3 / 10
- PricingOpen source
- Best forTeams wanting a broad open-source feature store
- Paid fromFree
- Deployment modelBoth
- Facts checked21 Sep 2026
Where it wins
- Supports both offline training data and low-latency online serving
- Combines point-in-time joins, materialization, and batch or streaming ingestion
- Works with major warehouses, databases, processing engines, and serving stores
Where it doesn't
- Infrastructure-oriented rather than a vendor-hosted SaaS product
- Deployment and operations require decisions across stores and environments
- Community and documentation are the listed support channels
Our verdict on Feast
Feast is an open-source feature store for machine-learning and AI applications. It manages feature definitions and connects historical offline stores with online stores used for low-latency inference. Teams can use it locally, in self-hosted infrastructure, or in cloud environments, making it suitable for small, mid-market, and enterprise organizations that want control over deployment. Its core scope covers feature retrieval, training datasets, online serving, and the operational path between them.
The feature set is broad for teams building production data and ML workflows. Feast supports point-in-time-correct historical joins, feature materialization from offline to online stores, batch and streaming ingestion, and a REST feature server for retrieval, pushing, and materialization. Feature definitions, a registry, and version-controlled feature services help organize feature delivery, while data-quality monitoring includes metrics, baselines, APIs, CLI workflows, and a UI. This combination makes Feast a fit when offline and online feature workflows need to remain connected rather than managed as separate systems.
Its ecosystem includes Snowflake, BigQuery, Redshift, Spark, PostgreSQL, Trino, DuckDB, Azure Synapse, Dask, ClickHouse, MSSQL, Redis, DynamoDB, Bigtable, and Cassandra. Deployment can be cloud-based or self-hosted, but Feast remains infrastructure-oriented rather than a vendor-hosted SaaS product. The open-source model suits teams that want a flexible foundation and are prepared to manage their surrounding infrastructure. Organizations seeking a fully managed service with vendor-operated deployment and a packaged support model should consider a different type of feature store.
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Feast pricing
Feast fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Free |
| Online store | Yes |
| Offline store | Yes |
| Point-in-time joins | Yes |
| Feature monitoring | Yes |
| Deployment model | Both |
| Serving modes | Both |
| Deployment | Cloud, Self-hosted |
| Platforms | Web |
| Support | Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 20 integrations: Snowflake, BigQuery, Redshift, Spark, PostgreSQL, Trino … See all → |
| Pricing | Open source |
| Website | feast.dev |
| Facts checked | 21 Sep 2026 |
Feast integrations
Feast lists 20 integrations on its own site.
- Snowflake
- BigQuery
- Redshift
- Spark
- PostgreSQL
- Trino
- DuckDB
- Azure Synapse
- Dask
- ClickHouse
- MSSQL
- Redis
- DynamoDB
- Bigtable
- Cassandra
- MySQL
- SQLite
- Dragonfly
- SingleStore
- Hazelcast
Alternatives to Feast
- Hopsworks Feature StoreA governed feature store spanning training, serving, monitoring, and deployment.9.4
- ChalkAdvanced serving, versioning, and observability for production ML teams.9.0
- Databricks Feature StoreA broad, governed feature platform for teams already working in Databricks.8.9
Feast vs the competition
- Feast vs Hopsworks Feature Store
- Feast vs Chalk
- Feast vs Databricks Feature Store
- Feast vs Snowflake Feature Store
- Feast vs Chronon
- Feast vs Feathr
Compare Feast with any tool side by side →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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