Find Open-Source Alternatives
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
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.
These projects match the most common migration paths for teams replacing Amazon SageMaker Feature Store.
Why teams pick it
Hopsworks delivers a real-time AI lakehouse, offering a Python-focused feature store, MLOps tools, multi-tenant projects, and flexible deployment on cloud, serverless, or on-premises.
Run on infrastructure you control
Recent commits in the last 6 months
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to Amazon SageMaker Feature Store.
Why teams pick it
Keep customer data in-house with privacy-focused tooling.
Real-time AI Lakehouse with Python-centric Feature Store
Why teams choose it
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.
Scalable feature store for unified data and AI engineering
SQL‑driven feature platform delivering millisecond real‑time ML features
Turn existing data pipelines into a collaborative virtual feature store
Unified feature store for training and real‑time inference
Teams replacing Amazon SageMaker Feature Store in feature stores workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from Amazon SageMaker Feature Store.
Why teams choose it
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
Why teams choose it
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.
Why teams choose it
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.
Why teams choose it
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.