Feature stores · Hopsworks AB

Hopsworks

Feature store and MLOps platform pairing a governed feature store with model registry and serving, available self-hosted or as SaaS.

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Hopsworks is a feature store built as part of a broader data-intensive AI platform, combining feature storage with a model registry, model serving, and vector search in one product. Its feature store separates an offline store (for training data and backfills, typically on object storage or a lakehouse) from an online store (for low-latency inference), with point-in-time joins to prevent data leakage between the two. Unlike lighter registry-only tools, Hopsworks includes its own compute for feature engineering pipelines (via Spark, Python, or Flink) rather than only orchestrating external systems. It is available as a free single-project SaaS tier, a pay-as-you-go SaaS plan, and a self-hosted Enterprise edition for on-premises or air-gapped deployment, with the core platform released under AGPL-3.0. It suits ML teams that want feature storage and model serving together rather than integrating separate tools.

At a glance

Vendor Hopsworks AB
Pricing model Usage-based
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (AGPL-3.0)
Best for ML teams that want a feature store and model serving together, with a genuine self-hosted/on-prem option.

Pricing

A free single-project tier is available; SaaS is pay-as-you-go, and Enterprise (including on-prem) is custom-quoted.

Plan Price Notes
Free $0 1 project, Feature Store + Model Registry, community support
SaaS Pay-as-you-go Unlimited projects, adds Model Serving and platform SLA
Enterprise Custom pricing On-premises/air-gapped deployment, dedicated support, guaranteed SLA

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Combined offline and online feature store with point-in-time joins
  • Built-in compute for feature pipelines (Spark, Python, Flink)
  • Model registry and model serving in the same platform
  • Vector search / embeddings support
  • Feature and model lineage
  • Self-hosted, SaaS, or air-gapped deployment options
  • Role-based access control and project isolation

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Feast Open-source feature store that manages feature definitions, storage, and serving so ML models get consistent features in training and production. Featureform Virtual feature store that turns existing data infrastructure into a governed feature store; now developed by Redis as Redis Feature Form.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data lakehouse A storage architecture that adds warehouse-style structure and transactions directly on top of low-cost data lake storage. Feature engineering Creating, transforming or selecting the input variables a model is trained on, to make the underlying pattern easier for it to learn. Feature store A centralized system that stores, versions and serves machine learning features so training and live prediction use identical definitions. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems.

Related guides

How to choose a feature store The real choice is whether your cloud or warehouse already has one built in, and whether you need sub-millisecond serving or just a shared registry.