Tacnode Feature Store
Computes and serves features directly on live system state — no warehouse, no reverse ETL, no sync gap.
Visit siteMachine Learning (ML) Feature Stores
A centralized feature store lets teams engineer, store, and serve ML features consistently across training and inference. Below are 11 leading feature store platforms — every link verified working, with dead and acquired products removed.
Amazon SageMaker Feature Store
A fully managed, purpose-built repository to store, update, retrieve, and share machine learning features.
Visit siteFeast
A standalone, open-source feature store that organizations use to store and serve features consistently for offline training and online inference.
Visit siteFeatureform
Easily manage your machine learning features across your organization with a virtual feature store.
Visit siteHopsworks
The AI Lakehouse for real-time ML, built around a sub-millisecond-latency enterprise feature store.
Visit siteDatabricks Feature Store
Lets data teams create new features, explore and reuse existing ones, publish to low-latency online stores, and build training data sets.
Visit siteJFrog ML Feature Store
Optimizes the entire feature lifecycle, enabling feature collaboration, consistency, and reliability in feature engineering and deployment.
Visit siteButterfree
A tool for building feature stores — transform your raw data into beautiful features. Open source on GitHub.
Visit siteIguazio Integrated Feature Store
Easily engineer online and offline features, and share them across teams and ML applications with minimal integration effort.
Visit siteFeathr
An enterprise-grade, high performance feature store, originally built at LinkedIn and open sourced on GitHub.
Visit siteKaskada
A next-generation streaming engine that connects AI models to real-time and historical data, built on Apache Arrow.
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