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Open-source alternatives to Amazon SageMaker Feature Store

Compare community-driven replacements for Amazon SageMaker Feature Store in feature stores workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.

Amazon SageMaker Feature Store logo

Amazon SageMaker Feature Store

SageMaker Feature Store provides online/offline stores, lineage and search across feature groups, and cross-account sharing—ensuring consistency between training and real-time inference.Read more
Feature Stores
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Key stats

  • 5Alternatives
  • 1Support self-hosting

    Run on infrastructure you control

  • 2Active development

    Recent commits in the last 6 months

  • 4Permissive licenses

    MIT, Apache, and similar licenses

Counts reflect projects currently indexed as alternatives to Amazon SageMaker Feature Store.

All open-source alternatives

OpenMLDB logo

OpenMLDB

SQL‑driven feature platform delivering millisecond real‑time ML features

Active developmentPermissive licenseIntegration-friendlyC++

Why teams choose it

  • Consistent feature generation for training and inference via unified execution plan
  • Real‑time SQL engine produces features in a few milliseconds
  • SQL‑first feature definition with extensions like LAST JOIN and WINDOW UNION

Watch for

Requires SQL expertise; non‑SQL environments may need adaptation

Migration highlight

NYC Taxi Trip Duration Prediction

End‑to‑end ML pipeline built with OpenMLDB and LightGBM to predict ride duration, demonstrating rapid feature development and deployment.

Feast logo

Feast

Unified feature store for training and real‑time inference

Active developmentPermissive licenseIntegration-friendlyPython

Why teams choose it

  • Consistent feature availability for training and serving
  • Point‑in‑time correct datasets to prevent leakage
  • Single data access layer that abstracts storage backends

Watch for

Web UI is experimental and may lack full functionality

Migration highlight

Batch model training with historical features

Generate point‑in‑time training datasets that avoid data leakage.

Feathr logo

Feathr

Scalable feature store for unified data and AI engineering

Permissive licenseFast to deployIntegration-friendlyScala

Why teams choose it

  • Pythonic APIs with native PySpark and Spark SQL UDF support
  • Point-in-time correct feature computation for training and online serving
  • Scalable architecture handling billions of rows and petabyte-scale data

Watch for

Requires a Spark environment, limiting use with non‑Spark stacks

Migration highlight

NYC Taxi fare prediction

Rapidly define, materialize, and serve fare prediction features with point‑in‑time correctness

Featureform logo

Featureform

Turn existing data pipelines into a collaborative virtual feature store

Permissive licensePrivacy-firstFast to deployGo

Why teams choose it

  • Infrastructure-agnostic: works with existing data platforms
  • Immutable feature definitions with lineage and versioning
  • Built-in RBAC, audit logs, and dynamic serving rules

Watch for

Requires orchestration setup and configuration

Migration highlight

Notebook-to-Production Feature Pipeline

Data scientists push transformations from Jupyter notebooks to a central repository, then Featureform orchestrates Spark jobs and serves the feature in Redis for online inference.

Hopsworks logo

Hopsworks

Real-time AI Lakehouse with Python-centric Feature Store

Self-host friendlyIntegration-friendlyAI-powered workflowsJava

Why teams choose it

  • Python‑centric Feature Store with versioning, lineage, and governance
  • Project‑based multi‑tenant environment for team collaboration
  • Integrated MLOps stack: Airflow, Jupyter, GPU training, model serving

Watch for

On‑premises install requires minimum 32 GB RAM, 8 CPUs

Migration highlight

Fraud detection with real-time scoring

Features are ingested and served instantly, enabling online models to flag fraudulent transactions as they occur.

Choosing a feature stores alternative

Teams replacing Amazon SageMaker Feature Store in feature stores workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.

  • 1 project let you self-host and keep customer data on infrastructure you control.
  • 2 options are actively maintained with recent commits.

Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from Amazon SageMaker Feature Store.