# featureform/featureform

The Virtual Feature Store. Turn your existing data infrastructure into a feature store.

Repository: https://github.com/featureform/featureform
Canonical: https://ross.abutalabs.com/products/featureform
Homepage: https://www.featureform.com
Language: Go
License: MPL-2.0
License Family: copyleft
Topics: machine-learning, data-science, vector-database, embeddings-similarity, embeddings, hacktoberfest, feature-store, mlops, data-quality, feature-engineering, ml, python
Last push: 2025-07-03T19:09:35+00:00

## Health v2 (maintenance only)
Score: 36/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 29, release rhythm 8, longevity 100
- inputs: {"age_days": 2147, "days_push": 426, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1985, forks 108 (observed 2026-08-28T04:06:02.644315+00:00)

## What it is
Featureform is a virtual feature store that sits atop your existing data infrastructure and orchestrates it to define, manage, and serve ML model features. It standardizes transformations, features, labels, and training sets with metadata like lineage, variants, and ownership for data science teams.

## Use cases
- turn my existing data warehouse into a feature store
- define and serve ML features from notebooks to production
- manage feature definitions and lineage for a data science team
- materialize features on a schedule for online serving
- share and reuse transformations across ML projects
- store and search embeddings for similarity lookups
- build consistent training sets from raw data

## When to choose
- you want feature store capabilities without migrating off existing databases and warehouses
- a team needs standardized, versioned feature definitions with ownership and lineage
- you need both offline training sets and low-latency online serving from one source of truth

## When to avoid
- you need a fully managed turnkey feature store with no infrastructure to operate
- your ML workflow is small-scale single-user experimentation without collaboration needs
- you only need ad-hoc data transformations without feature serving or metadata management

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, data-science, vector-database, etl, workflow-automation
- domain: machine-learning, data-science
- platform: python, go, self-hosted, cloud
- tags: feature-store, mlops, feature-engineering, embeddings, data-quality, virtual-feature-store, data-engineering, docker, kubernetes

## Member repositories
- featureform/featureform (main) score 36

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:02.644315+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 2026-08-30T03:03:16.577951+00:00, confidence not recorded.
  - readme: https://github.com/featureform/featureform (fetched 2026-08-28T04:06:02.644315+00:00, sha 198b1b9da9e8)
  - homepage: https://www.featureform.com (fetched 2026-08-29T10:43:06.898330+00:00, sha 36d212cfd121)
  - site_page: https://redis.io/docs/latest/integrate/riot (fetched 2026-08-29T10:43:06.902801+00:00, sha cef94f24dc53)
  - site_page: https://redis.io/docs/latest/develop/clients (fetched 2026-08-29T10:43:06.904268+00:00, sha a107489bc126)
  - site_page: https://redis.io/docs/latest/integrate (fetched 2026-08-29T10:43:06.906076+00:00, sha 9e9119d0ab77)
  - site_page: https://redis.io/docs/latest/develop/ai (fetched 2026-08-29T10:43:06.907870+00:00, sha 073b7bea706a)
  - site_page: https://redis.io/docs/latest/develop/get-started/rag (fetched 2026-08-29T10:43:06.909724+00:00, sha 900b9542c65b)
  - site_page: https://redis.io/docs/latest/develop/ai/featureform (fetched 2026-08-29T10:43:06.911421+00:00, sha db8621d502e3)
  - site_page: https://redis.io/docs/latest/operate/rc (fetched 2026-08-29T10:43:06.913230+00:00, sha c79815761f73)
  - site_page: https://redis.io/pricing (fetched 2026-08-29T10:43:06.901028+00:00, sha da067de15b21)
- Data as of 2026-08-30T08:39:29.467469+00:00.