Feast

Feast is ranked #3 of 17 in feature store software on The Geeks Club. It runs on API, Linux, Self-hosted, Web. There is a free plan.

Feast plans and pricing

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

Compared on feature store software

Online store: Yes
Offline store: Yes
Point-in-time joins: Yes
Feature monitoring: Yes
Deployment model: both
Serving modes: both

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Feast

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

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Where it ranks on The Geeks Club

Best Feature Store Software in 2026#3 of 17

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Sources

feast.dev· checked 30 Sept 2026
docs.feast.dev/getting-started/quickstart· checked 30 Sept 2026
docs.feast.dev/getting-started/components/overview· checked 30 Sept 2026
docs.feast.dev/reference/feature-servers/python-featur· checked 30 Sept 2026
docs.feast.dev/getting-started/third-party-integration· checked 30 Sept 2026
docs.feast.dev/how-to-guides/feast-on-kubernetes· checked 30 Sept 2026
docs.feast.dev/getting-started/components/authz_manage· checked 30 Sept 2026
docs.feast.dev/getting-started/architecture/overview· checked 30 Sept 2026