What is Feast?
A framework that manages feature data pipelines and serves structured features to machine learning models during both training and real-time inference at scale.
Feast pricing
We don't have Feast's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
Platform & deployment
Independently observed- CLI
- Web
- Cloud / SaaS
- On-premise
- Self-hosted
Integrations (21)
Independently observed- Apache Iceberg
- Databricks
- Snowflake
- AWS Glue
- AWS EMR
- Unity Catalog
- Hadoop
- DynamoDB
- Redis
- PostgreSQL
- MySQL
- MongoDB
- Milvus
- ClickHouse
- FAISS
- Apache Spark
- MLflow
- OpenLineage
- dbt
- Amundsen
- DataHub
Security & compliance
Known vulnerabilities: 2 (1 in the last 12 months), max severity HIGH sourcea count reflects scale & disclosure, not quality
Feast alternatives
Other feature stores we track, ranked by the same independent score.
- Amazon SageMaker Feature StoreA managed repository for storing, sharing, and reusing machine learning features across your ML lifecyclemedium · 62%
- Feathrlow · 35%
- TectonReal-time data platform for production AI agentslow · 6%
- Featureformlow · 22%
- Hopsworks Feature StoreA platform for managing machine learning features, model artifacts, and real-time inference serving across the full ML lifecyclelow · 26%
- Vertex AI Feature StoreA metadata management layer for storing and serving machine learning features from BigQuery data sourceslow · 3%
Compare Feast
Side by side against other feature stores, attribute by attribute, with a source on every value.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Feast, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Development activity | 59 | 0.09 | 5.5 | ✓ |
| Release cadence | 86 | 0.05 | 4.5 | ✓ |
| Integrations | 39 | 0.07 | 2.7 | ✓ |
| Security score | 57 | 0.04 | 2.4 | ✓ |
| Dependent projects | 36 | 0.06 | 2.3 | ✓ |
| Stars | 73 | 0.03 | 1.9 | ✓ |
| Capabilities | 0 | 0.08 | 0.0 | - |
| Security posture | 0 | 0.07 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 73 | mediumsource · 2026-08-26 · 65% |
Adoption
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 21 | mediumsource · 2026-08-20 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-08-26 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-08-26 · 95% |