# Best Feature Store Platforms for ML
Best Feature Store Platforms for ML
Feature stores that keep training and inference data consistent and governed.
Quick Picks
- Feast: Best open-source starter
- Tecton: Best managed enterprise feature platform
- Databricks Feature Store: Best if you are on Databricks
Pricing Snapshot
| Tool | Entry | Mid | Notes |
|---|---|---|---|
| Feast | OSS | Use your infra; managed options exist | |
| Tecton | Custom | Managed, low-latency serving | |
| Databricks Feature Store | Included | Part of Databricks ML |
What to Look For
- Offline/online consistency and latency
- Backfills, lineage, and data quality
- Point-in-time correctness and training sets
- Monitoring and access controls
Tool Notes
Feast
- OSS with multiple offline/online stores
- Great for teams already on cloud data lakes
- Add your own monitoring and governance
Tecton
- Managed platform with low-latency serving
- Good lineage, quality, and SLAs
- Enterprise pricing but production-ready
Databricks Feature Store
- Integrated with Delta and MLflow
- Good for teams on Databricks
- Leverages existing lakehouse stack
Final Recommendation
Pick the option that fits your stack, compliance needs, and budget. Start lean, measure, and scale when you see ROI.
Try the leaders: Feast | Tecton | Databricks Feature Store
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