# logicalclocks/hopsworks

Hopsworks - Data-Intensive AI platform with a Feature Store

Repository: https://github.com/logicalclocks/hopsworks
Canonical: https://ross.abutalabs.com/products/hopsworks
Homepage: https://hopsworks.ai
Language: Java
License: AGPL-3.0
License Family: copyleft
Topics: feature-store, aws, azure, data-science, feature-engineering, feature-management, gcp, governance, kserve, machine-learning, mlops, model-serving, pyspark, python, serverless, ml, hopsworks
Last push: 2025-02-10T05:53:39+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 6, release rhythm 8, longevity 100
- inputs: {"age_days": 2960, "days_push": 569, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1303, forks 160 (observed 2026-08-28T04:04:18.360175+00:00)

## What it is
Hopsworks is an open-source, data-intensive AI platform (an 'AI Lakehouse') built around a Python-centric Feature Store with online/offline storage powered by RonDB, plus MLOps capabilities including a model registry, model serving, and experiment tracking. It is a modular Java-based platform deployable on Kubernetes (AWS, Azure, GCP, on-prem) that integrates with Spark, Flink, Pandas, Databricks, SageMaker, and Kubeflow.

## Use cases
- manage and reuse ML features across models with a feature store
- serve features with sub-millisecond online latency for real-time inference
- prevent training-serving skew between batch and online data
- track experiments and manage a model registry
- deploy and monitor ML models in production
- build feature pipelines with Spark, Flink, or Pandas
- govern and share ML assets across data science teams
- run an on-premise or air-gapped ML platform

## When to choose
- you need an open-source feature store with both online and offline stores
- your team requires feature reuse, governance, and lineage for ML data
- you want an integrated MLOps platform with model registry and serving
- you need sub-millisecond feature vector retrieval at serving time
- you deploy on Kubernetes across AWS, Azure, GCP, or on-premises

## When to avoid
- you only need a lightweight feature store without a full platform (consider Feast)
- you cannot accept the AGPL-3.0 license for your use case
- you lack Kubernetes or multi-node infrastructure for deployment
- your ML stack is simple enough that a full lakehouse platform is overkill

## Facets
- artifact type: application
- maturity: active
- function: machine-learning, data-science, etl, streaming, monitoring, search-engine, vector-database, self-hosted, workflow-automation, sdk
- domain: machine-learning, data-science, large-language-models, big-data, databases
- platform: cloud, self-hosted, python, jvm
- tags: feature-store, mlops, model-registry, model-serving, ai-lakehouse, rondb, online-offline-store, training-serving-skew, kserve, spark, flink, pandas, data-governance, agpl, data-engineering, kubernetes, docker, web-server

## Member repositories
- logicalclocks/hopsworks (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:18.360175+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:51:12.456372+00:00, confidence not recorded.
  - readme: https://github.com/logicalclocks/hopsworks (fetched 2026-08-28T04:04:18.360175+00:00, sha 0d785be55e95)
  - homepage: https://hopsworks.ai (fetched 2026-08-29T12:09:24.044746+00:00, sha d1a03b44fe80)
  - site_page: https://www.hopsworks.ai/about-us (fetched 2026-08-29T12:09:24.059223+00:00, sha f3d4f3ce3263)
  - site_page: https://docs.hopsworks.ai/latest (fetched 2026-08-29T12:09:24.062519+00:00, sha bc629227c874)
  - site_page: https://www.hopsworks.ai/pricing (fetched 2026-08-29T12:09:24.053764+00:00, sha b37745956cc3)
  - site_page: https://www.hopsworks.ai/product-capabilities/feature-store (fetched 2026-08-29T12:09:24.055778+00:00, sha ef9c4dfa2cb1)
  - site_page: https://www.hopsworks.ai/integrations (fetched 2026-08-29T12:09:24.057654+00:00, sha a1ea7edea3b6)
  - site_page: https://www.hopsworks.ai/faq (fetched 2026-08-29T12:09:24.060798+00:00, sha 6f5bb782410e)
- Data as of 2026-08-30T08:39:29.467469+00:00.