Overview

Feast is an open-source feature store for teams building AI and machine-learning applications that need structured data at training and inference time. It is a strong fit for data and ML groups that want shared feature management across batch and real-time workloads, provided they can take responsibility for authentication and batch transformations.

Key features

Feast serves both batch and real-time use cases, with online and offline stores, data sources, and feature monitoring. Point-in-time joins help prevent future values from leaking into historical training data, a practical safeguard when datasets are assembled from changing feature tables.

Feature services support discovery and collaboration around existing features, while versioning feature sets helps teams coordinate shared definitions. The Python SDK and CLI cover version-controlled definitions, materialization, training-dataset creation, and retrieval of online features. A Python feature server exposes an HTTP endpoint that accepts and returns JSON, allowing clients in other languages to request features without using the Python SDK.

Feast supports transformations for on-demand and streaming sources, but batch transformations require a separate engine. Its Spark processor for consuming Kafka data is experimental, so teams looking for a mature stream-processing path should weigh that limitation. Kubernetes deployments can run feature servers and scheduled or ad-hoc jobs as workloads.

Access control supports OIDC and Kubernetes RBAC, but the default authorization configuration is no_auth. Feast does not provide authentication: clients must manage and pass authentication tokens to the server. That is a meaningful operational responsibility for teams exposing feature services.

Pricing

Feast is open source and free: its Feast plan costs 0.00 USD per free. The plan includes online and offline stores, point-in-time joins, and feature monitoring, with both deployment models and both serving modes. It is a straightforward fit for teams comfortable operating an open-source feature store; there is no paid tier or separate plan to step up to.

Platforms

Feast is available through an API, on Linux, as self-hosted software, and on the web. Its deployment model and serving modes both support batch and real-time use.

Who it's for

Data scientists, MLOps engineers, data engineers, and AI engineers are the intended users. It suits teams coordinating reusable features across training and inference, especially when point-in-time correctness matters. Teams that need built-in authentication or batch transformations within the feature store should look elsewhere or plan to provide those capabilities separately. Feast's homepage also points users to its Slack community for support from Feast developers.

Pros and cons

Pros: Point-in-time joins help guard training data against future-value leakage.
Pros: Feature services combine collaboration and feature-set versioning; the SDK, CLI, and HTTP server cover definition, materialization, dataset building, and serving.
Pros: Free open-source access includes online and offline stores, monitoring, and both serving modes.
Cons: Authentication is left to clients, and authorization defaults to no_auth, creating setup and security responsibilities for operators.
Cons: Batch transformations need a separate engine, while the documented Spark processor remains experimental.

Alternatives

Feature Store Software is the broader category to compare before choosing a feature store.

Choose Hopsworks Feature Store if a free tier with one project, a Feature Store, Model Registry, and community support better matches your needs; its Enterprise plan uses custom pricing.

Chalk is a paid alternative for API, self-hosted, and web platforms.

Chronon is another free, self-hosted option, with an open-source plan that states no usage limits.

Databricks Feature Store may suit teams preferring usage-based monthly billing with per-second granularity; its rates vary by product, cloud provider, and region, and it offers a free trial.

Snowflake Feature Store is a paid, consumption-billed option with Standard and Enterprise plans.

OpenMLDB is a free open-source machine-learning database available in standalone and cluster versions, including Linux and macOS platforms.

Feathr is another free option available through API, Linux, self-hosted, and web platforms.

Red Hat OpenShift AI Feature Store is a paid alternative available on the web.

Verdict

Choose Feast if your team wants a free, open-source feature store for shared batch and real-time feature management, with point-in-time joins and multiple serving routes. Look elsewhere if built-in authentication or batch transformations are essential, or if an experimental stream-processing component is not suitable for your workload.

	Feast	Metarank
Free plan	Yes	No
Free trial	No	No
Paid from	Free	—
Open source	No	No
Platforms	api, Linux, self-hosted, Web	api, Linux, macOS, self-hosted, Windows
Online store	Yes	Yes
Offline store	Yes	Yes
Point-in-time joins	Yes	Yes
Feature monitoring	Yes	—
Deployment model	both	self_hosted
Serving modes	both	both

Feast vs Metarank

Feast

Metarank