Feast

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Score6.9

Rank#4 of 17

PriceFree

Free planYes

Runs onAPI, Linux, Self-hosted, Web

Summary

Feast is an open-source feature store for delivering structured data to AI and LLM applications during training and inference. It supports feature management and serving for batch and real-time applications, with online and offline stores. Its point-in-time joins help prevent future feature values from entering model training data. Feature services support discovery, collaboration, and versioning of feature sets. The Python SDK and command-line interface manage version-controlled feature definitions, materialize values, build training datasets, and retrieve online features. A Python feature server provides an HTTP endpoint with JSON input and output, accessible to clients in any language that can make HTTP requests. Feast’s documentation describes integrations with data sources and stores, including community and custom integrations. It can run on Kubernetes, where servers and jobs operate as workloads. Feast supports OIDC and Kubernetes RBAC authorization, but its default configuration is no_auth. It does not provide authentication itself, so clients must manage and pass authentication tokens. Batch transformations require a separate transformation engine.

Who it is for

Feast is designed for data scientists, MLOps engineers, data engineers, and AI engineers managing features for AI or machine-learning applications. It suits teams that need batch and real-time serving or point-in-time-correct training data.

What is good

Free and open source.
Supports batch and real-time feature serving.
Point-in-time joins help prevent training-data leakage.
Python SDK and CLI manage feature workflows.
Online and offline store support.

What to know first

Default authorization configuration is no_auth.
Clients must manage authentication tokens.
Batch transformations require a separate engine.
Spark stream processor is experimental.

Verdict

Feast brings feature management, training-data creation, and online serving into one open-source feature store. Teams should account for its authentication responsibilities and separate engine requirement for batch transformations.

Feast plans and pricing

All plans

Feast Free Open-source feature store feast.dev · 30 Sept 2026

Compared on feature store software

Online store: Yesfeast.dev
Offline store: Yesfeast.dev
Point-in-time joins: Yesfeast.dev
Feature monitoring: Yesfeast.dev
Deployment model: bothfeast.dev
Serving modes: bothfeast.dev

Facts

What it does: Feast is an open-source feature store that delivers structured data to AI and LLM applications for training and inference.feast.dev · 30 Sept 2026
Batch and real-time: Feast supports machine learning feature management and serving for both batch and real-time applications.docs.feast.dev · 30 Sept 2026
Point-in-time correctness: Feast joins feature tables using point-in-time logic to prevent future feature values from leaking into model training data.docs.feast.dev · 30 Sept 2026
Feature versioning: Feast enables discovery and collaboration on existing features and versioning of feature sets through feature services.docs.feast.dev · 30 Sept 2026
SDK and CLI: The Python SDK and CLI manage version-controlled feature definitions, materialize values, build training datasets, and retrieve online features.docs.feast.dev · 30 Sept 2026
Feature server: The Python feature server serves features through an HTTP endpoint with JSON input and output, usable from any language that can make HTTP requests.docs.feast.dev · 30 Sept 2026
Stores and sources: Feast docs describe integrations with offline and online stores and data sources, including community and custom integrations.docs.feast.dev · 30 Sept 2026
Stream processing: Feast's component overview describes an experimental Spark processor that can consume data from Kafka.docs.feast.dev · 30 Sept 2026
Deployment: Feast can be deployed on Kubernetes, where feature servers and scheduled or ad-hoc jobs can run as Kubernetes workloads.docs.feast.dev · 30 Sept 2026
Access control: Feast supports OIDC and Kubernetes RBAC authorization, while its default authorization configuration is no_auth.docs.feast.dev · 30 Sept 2026
Authentication responsibility: Feast does not provide authentication capabilities; clients are responsible for managing and passing authentication tokens to the server.docs.feast.dev · 30 Sept 2026
Transformations: The architecture docs say Feast supports transformations for on-demand and streaming sources, while batch transformations require a separate transformation engine.docs.feast.dev · 30 Sept 2026
Intended users: The quickstart identifies data scientists, MLOps engineers, data engineers, and AI engineers as users Feast is designed to serve.docs.feast.dev · 30 Sept 2026
Community support: The Feast homepage invites users to join its Slack community for support from Feast developers.feast.dev · 30 Sept 2026