Feast
Open-source feature store that manages the machine-learning data teams use for both model training and real-time predictions.
🔗 Visit Feast
Description
Machine learning models are trained on historical data but make predictions in real time — for example, a fraud model trains on months of past transactions but must score a new transaction in milliseconds. Keeping those two versions of the same data consistent, so the model doesn't behave differently in production than it did in testing, is a surprisingly hard engineering problem. Feast is a free, open-source tool built specifically to solve that mismatch. Feast is an open-source feature store: a system that lets ML teams define, store and serve the input variables ("features") models use, from both historical batch sources for training and low-latency online sources for real-time inference. It supports 10+ offline stores (Snowflake, BigQuery, Redshift, PostgreSQL) and 15+ online stores (Redis, DynamoDB, Cassandra, MySQL) through a common API, guarantees point-in-time-correct data to prevent leakage between training and serving, includes vector similarity search for RAG applications, and provides a feature registry with lineage tracking plus data-quality monitoring. It's fully free and open source under Apache-2.0, with no commercial tier — the project reports 12M+ downloads and a 5,500-member Slack community.
💬 Our review
The short version: Feast is the default open-source answer to "how do I keep my ML training data and production data consistent," and since it's completely free, it's an easy first choice before paying for a commercial feature platform.
Feast's genuine strength is being a thin, flexible abstraction layer over whatever databases a team already uses, rather than demanding a specific data stack — 10+ offline and 15+ online store integrations is a real breadth advantage over more opinionated platforms. Point-in-time-correct joins are the unglamorous but critical feature that prevents "training-serving skew," a bug class that silently degrades model accuracy in production. The honest trade-off: being open source and self-managed, Feast doesn't include managed infrastructure, support SLAs, or the more advanced feature-engineering pipelines that acquired/commercial platforms bundle in (Tecton's functionality, for instance, is now folded into Databricks). For teams with in-house data engineering capacity who want a free, vendor-neutral feature store, Feast is close to the standard choice; teams wanting a fully managed, hands-off experience should look at their existing cloud provider's managed feature store (SageMaker, Vertex AI) instead.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Pros
Completely free, Apache-2.0, no vendor lock-in
Works with 10+ offline and 15+ online stores you likely already run
Point-in-time-correct joins prevent training-serving skew
Cons
No managed hosting or commercial support included
Requires in-house data engineering to operate at scale
🔄 Alternatives to Feast
See all alternatives to Feast →❓ Frequently asked questions
- What problem does a feature store actually solve?
- It keeps the data a model was trained on and the data it sees in production consistent, and lets teams reuse the same feature definitions across multiple models instead of recomputing them each time.
- Does Feast include a managed cloud version?
- No — Feast is purely open source and self-managed. For managed feature stores, cloud providers offer their own (SageMaker Feature Store, Vertex AI Feature Store).
- Can Feast serve features for RAG or LLM applications?
- Yes — it added vector similarity search support, letting teams use the same feature store for both traditional ML features and embedding-based retrieval.
- Is it worth the money compared to alternatives?
- Feast costs nothing beyond the infrastructure you already run, versus managed feature stores that charge for compute and storage on top of cloud fees. Worth it if you have data engineering capacity; a managed option is worth it if you'd rather not operate the infrastructure.
- Which feature store should you pick for your case?
- Want free and vendor-neutral, self-managed: Feast. Already on Databricks and want feature engineering integrated with your lakehouse: Databricks Feature Store. Already on AWS or GCP and want zero infrastructure: SageMaker or Vertex AI Feature Store. Want a dedicated feature platform with managed real-time serving: Hopsworks.