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Feast

Feast is an open-source feature store for machine learning and AI applications. It provides access to structured data for training and inference, with historical retrieval for training datasets and online retrieval for real-time prediction.

Visit Feast →
Type
ML Platform
Category
MLOps

By Egor Burlakov · Edited · Source dates below

Snapshot

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TopicDetail

Operating model

Open-source feature store that manages existing infrastructure for model training and online inference.

Data access

Offline stores support historical processing; online stores and a feature server support low-latency feature serving.

License

Apache-2.0.

Take

Consider Feast when a team needs one feature-access layer across training and online inference and expects to move between data infrastructure systems. Its value depends on the team being ready to configure and operate the relevant offline, online, and serving components.

Evaluate Feast

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Where Feast fits

  • It is designed to make features available consistently for training and serving.

  • Its historical feature workflow is intended to generate point-in-time-correct feature sets to help avoid future values leaking into training data.

  • Its contributor roadmap lists planned offline and online store options, including warehouse, database, cache, and vector-store entries; some are qualified as community or contrib plugins.

Product screenshot

Feast product interface
Product UI source · Verified

Trade-offs to evaluate

Feast is a project under active development, and several roadmap capabilities are explicitly marked alpha, beta, or in progress. Evaluate the maturity of the specific integration or serving path required for production before adopting it.

  • Feast abstracts feature retrieval from storage, but buyers still select and operate the surrounding data infrastructure.

  • Some transformation and serving capabilities have release-status qualifications, including alpha and beta labels.

Public signals

About these signals

Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.

188 GitHub commits 90d7.4k GitHub stars0 vulnerabilities across 1 package

See all signals from 3 sources
Source
Signals
Last updated
GitHub
Commits 90d:188Stars:7.4k
November 9, 2026
PyPI
Weekly downloads:61.8k
November 9, 2026
OSV
Package vulnerabilities:0 vulnerabilitiesacross 1 package

PyPI · feast@0.67.0

November 9, 2026

Sources

This review is based on Feast’s official website and public GitHub repository, captured on 2026-11-02.