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Feast

APIyesOSS—FREEyesDOCS4/5
FE7.3#4 of 17
outWeb—Windows—MacoutLinux—Android—iOS

Ranked in Feature Store Software ·Free plan

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About

Feast is an open-source feature store that delivers structured data to AI and LLM applications for training and inference. It manages machine-learning features for batch and real-time applications, with offline and online stores. Point-in-time joins help keep future feature values out of training data, while feature services support feature discovery, collaboration, and versioning. The Python SDK and CLI manage version-controlled definitions, materialize values, build training datasets, and retrieve online features. A Python feature server exposes an HTTP endpoint with JSON input and output, usable by clients in any language that can make HTTP requests. Feast documentation describes integrations with data sources and offline and online stores, including community and custom integrations. It can run on Kubernetes, with feature servers and scheduled or ad-hoc jobs operating as workloads. Feast supports OIDC and Kubernetes RBAC authorization, but its default authorization configuration is no_auth. It does not provide authentication; clients must manage and pass tokens. Batch transformations require a separate transformation engine. Feast is listed at 0.00 USD per free.

Who it is for

Feast suits data scientists, MLOps engineers, data engineers, and AI engineers who need feature management and serving for training or inference. Teams should be prepared to handle client authentication and use a separate engine for batch transformations.

What is good

  • Point-in-time joins help prevent training-data leakage.
  • Supports batch and real-time feature serving.
  • SDK and CLI manage definitions and training datasets.
  • Can deploy feature servers and jobs on Kubernetes.

What to know first

  • Default authorization configuration is no_auth.
  • Feast does not provide authentication capabilities.
  • Batch transformations need a separate transformation engine.
  • Spark processor is described as experimental.

Verdict

Feast provides a free, open-source route to managing and serving features, with point-in-time joins and HTTP-based retrieval. Its authorization defaults and client-managed authentication make security configuration an important integration responsibility.

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

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Where it ranks on Inferse

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