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Feast

No phone app

8.0No. 2 of 15
in Feature Store Software
  • Recognised40% of the score93
  • Phone app26% of the score0
  • Documented20% of the score87
  • Free plan14% of the score100
Free plan
Yes
Runs on
api, Linux, self-hosted, Web
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Summary

Feast is a free, open-source feature store that delivers structured data to AI and LLM applications for training and inference. It manages and serves machine-learning features for batch and real-time applications, with integrations for offline and online stores and data sources. Point-in-time joins are designed to keep future feature values out of training data. Feature services support discovering, collaborating on, and versioning feature sets. Its Python SDK and CLI manage version-controlled feature definitions, materialize values, build training datasets, and retrieve online features. A Python feature server provides an HTTP endpoint that accepts and returns JSON, so clients in any language capable of HTTP requests can use it. Feast can be deployed on Kubernetes, where servers and jobs run as workloads. Its authorization options include OIDC and Kubernetes RBAC, but the default configuration is no_auth. Feast does not provide authentication itself, so clients are responsible for managing and passing authentication tokens. Transformations are supported for on-demand and streaming sources; batch transformations require a separate transformation engine. The project identifies data scientists, MLOps engineers, data engineers, and AI engineers as intended users.

Who it is for

Feast suits data scientists, MLOps engineers, data engineers, and AI engineers managing features for machine-learning or AI applications. It is relevant to teams serving features in batch and real-time workflows.

What is good

  • Supports batch and real-time feature serving
  • Point-in-time joins guard against future-value leakage
  • Python SDK and CLI manage feature workflows
  • Free and open source

What to know first

  • Default authorization configuration is no_auth
  • Clients must manage authentication tokens
  • Batch transformations require a separate engine

Verdict

Feast provides feature management and serving across batch and real-time use, with point-in-time joins and versioned feature services. Teams need to handle authentication themselves, and batch transformations require a separate engine.

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

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