# feast-dev/feast

The Open Source Feature Store for AI/ML

Repository: https://github.com/feast-dev/feast
Canonical: https://ross.abutalabs.com/products/feast
Homepage: https://feast.dev
Language: Python
License: Apache-2.0
License Family: permissive
Topics: machine-learning, features, ml, big-data, feature-store, python, mlops, data-engineering, data-science, data-quality
Last push: 2026-08-26T17:50:14+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 2823, "days_push": 7, "days_rel": 12, "gap_med": 21, "n_releases_24m": 30}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7230, forks 1413 (observed 2026-08-28T04:09:57.649561+00:00)

## What it is
Feast is an open-source feature store for machine learning that manages offline stores for historical training data and low-latency online stores for real-time inference, with a feature server for serving pre-computed features. It provides point-in-time correct feature retrieval to prevent data leakage and abstracts feature storage so models remain portable across data infrastructure.

## Use cases
- serve features for real-time model inference with low latency
- generate point-in-time correct training datasets without data leakage
- manage feature definitions shared across training and serving pipelines
- build fraud detection or recommendation systems with real-time behavioral features
- retrieve documents via vector similarity search for RAG applications
- materialize features from data lakes like Apache Iceberg into online stores
- decouple ML models from underlying data infrastructure

## When to choose
- you need consistent features across model training and online serving
- your team wants to productionize ML features on existing data infrastructure like Snowflake, Redis, or Iceberg
- you need point-in-time correct joins for training data
- you are building real-time ML applications such as fraud detection or personalization
- you want an open-source, vendor-neutral feature store with broad integrations

## When to avoid
- you only need simple batch analytics without ML serving requirements
- you want a fully managed feature store and prefer a commercial SaaS like Tecton or SageMaker Feature Store
- your team cannot operate additional data infrastructure like online stores and feature servers
- you need heavy feature transformations computed at request time rather than pre-computed features

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, etl, caching, vector-database, rag, sdk, cli
- domain: machine-learning, data-science, big-data
- platform: python, cross-platform, cloud, self-hosted
- tags: feature-store, mlops, online-store, offline-store, feature-serving, point-in-time-correctness, real-time-inference, apache-iceberg, llm-applications, data-engineering, docker, kubernetes

## Member repositories
- feast-dev/feast (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:57.649561+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:39:47.051752+00:00, confidence not recorded.
  - readme: https://github.com/feast-dev/feast (fetched 2026-08-28T04:09:57.649561+00:00, sha af4d24def4b7)
  - homepage: https://feast.dev (fetched 2026-08-29T08:34:39.993212+00:00, sha 62c39e749eba)
  - site_page: https://docs.feast.dev/ (fetched 2026-08-29T08:34:39.996304+00:00, sha 878669a25892)
  - registry_pypi: https://pypi.org/pypi/feast/json (fetched 2026-08-29T08:34:40.003726+00:00, sha 988c39d1904e)
  - site_page: https://feast.dev/blog/aerospike-feast-benchmark-harness-results (fetched 2026-08-29T08:34:39.998347+00:00, sha 15aff8519ebc)
  - site_page: https://feast.dev/blog/aerospike-feast-now-available (fetched 2026-08-29T08:34:40.000254+00:00, sha b66ff562599e)
  - site_page: https://feast.dev/blog/feast-unity-catalog-integration (fetched 2026-08-29T08:34:40.001971+00:00, sha 6e80f1399795)
- Data as of 2026-08-30T08:39:29.467469+00:00.