Feast - The Open Source Feature Store for Machine Learning

Serving Data for Production AI

Feast is an open source feature store that delivers structured data to AI and LLM applications at high scale during training and inference

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[ADOPTERS AND CONTRIBUTORS]

293

Contributors

12M+

Downloads

5.5K

Slack Members

[USE CASES]

SOLVE REAL PROBLEMS

Real-Time Recommendations

Serve personalized product and content recommendations with real-time user interaction features

Fraud Detection

Detect fraudulent transactions using historical patterns and real-time behavioral features

Risk Scoring

Calculate risk scores for financial services using consistent features across training and inference

Customer Segmentation

Create dynamic customer segments using consistent feature definitions across teams

[INTEGRATIONS]

CONNECT WITH YOUR STACK

OFFLINE STORES

ONLINE STORES

IKV

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START SERVING IN SECONDS

 from feast import FeatureStore

# Initialize the feature store
store = FeatureStore(repo_path="feature_repo")

# Get features for training
training_df = store.get_historical_features(
    entity_df=training_entities,
    features=[
        "customer_stats:daily_transactions",
        "customer_stats:lifetime_value",
        "product_features:price"
    ]
).to_df()

# Get online features for inference
features = store.get_online_features(
    features=[
        "customer_stats:daily_transactions",
        "customer_stats:lifetime_value",
        "product_features:price"
    ],
    entity_rows=[{"customer_id": "C123", "product_id": "P456"}]
).to_dict()

# Retrieve your documents using vector similarity search for RAG
features = store.retrieve_online_documents(
    features=[
        "corpus:document_id",
        "corpus:chunk_id",
        "corpus:chunk_text",
        "corpus:chunk_embedding",
    ],
    query="What is the biggest city in the USA?"
).to_dict() 

[BLOG POSTS]

THE LATEST FROM FEAST

Blog

Native MongoDB Support in Feast: One Database for Operational Data, Features, and Vectors

Feast now ships first-class support for MongoDB as both an online and an offline store, plus native Vector Search for embedding-based retrieval. Machine Learning teams running on MongoDB can serve features at low latency, generate point-in-time-correct training datasets, and power RAG or recommender workloads, all from a single MongoDB Atlas cluster, with no separate cache, no separate warehouse, and no parallel vector database to keep in sync.

Blog

Building AI Agents with Feast: Feature Stores as Context and Memory

How Feast's MCP integration turns your feature store into a governed context and memory layer for AI agents, bridging the gap between experimental agents and production-ready systems.

Blog

Feast Introduces Experimental Feature View Versioning

Feast now supports experimental feature view versioning — bringing automatic version tracking, safe rollback, and multi-version online serving to your feature store. Only supported for SQLite today; we're inviting the community to test and give feedback.

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Docs

See our comprehensive documentation and start building with Feast today

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