Hopsworks
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What is Hopsworks
Hopsworks is an MLOps platform centered on building and operating feature stores for machine learning. It supports feature engineering, feature versioning, offline/online feature serving, and governance workflows used by data science and ML engineering teams. The platform is commonly deployed in cloud or Kubernetes environments and integrates with common data and ML tooling. A key differentiator is its emphasis on a managed feature store with built-in metadata, access controls, and training/serving consistency patterns.
Governance and access controls
The platform includes project-based organization, role-based access control, and metadata management around features and datasets. These controls help teams manage who can create, modify, and consume features in shared environments. This is useful for organizations that need traceability and controlled reuse of ML assets.
Strong feature store capabilities
Hopsworks provides a dedicated feature store with support for offline and online feature access patterns. It includes feature definitions, versioning, and metadata to help teams reuse features across projects. This focus can reduce duplicated feature engineering work and improve consistency between training and inference.
Integrates with ML workflows
Hopsworks is designed to fit into end-to-end ML workflows, including feature pipelines and model training/serving patterns. It supports integrations and deployment approaches commonly used in modern ML stacks (for example, cloud object storage and Kubernetes-based operations). This makes it suitable for teams that want a feature-store-first approach without replacing their entire data platform.
Feature-store-first scope
Hopsworks’ core value is strongest when an organization standardizes on a feature store as a central ML asset. Teams primarily looking for broad, general-purpose analytics, data preparation, or full-stack data warehousing may need additional platforms alongside it. As a result, it may not be a single-vendor solution for all data and AI needs.
Operational complexity at scale
Running an MLOps platform with online/offline serving components can introduce operational overhead, especially in self-managed deployments. Organizations may need Kubernetes, security, and data engineering expertise to operate it reliably. This can be a barrier for smaller teams without dedicated platform engineering support.
Integration work varies by stack
The effort to integrate Hopsworks depends on existing storage, compute, orchestration, and model serving choices. Some environments may require custom connectors, data modeling decisions, or pipeline refactoring to align with feature store patterns. This can extend implementation timelines compared with more tightly coupled, single-stack platforms.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Free | $0 | 1 project included; Feature Store + Model Registry; Full features with capped capacity; No credit card required; Community support. |
| SaaS (Pay-as-you-go) | $0.35 per credit (pay-as-you-go) — example shown for EU-WEST (up to 5 projects); general SaaS listing on the pricing page also states "Unlimited projects" | Usage-based billing; Feature Store + Model Registry; Model Serving; Pay only for what you use; Platform SLA (contact site for region/pricing differences). |
| Enterprise | Custom | Dedicated support and custom deployments; On-premise & air-gapped deployments; Dedicated support team; Custom integrations; SLA guaranteed; Contact sales for pricing. |
Seller details
Hopsworks AB
Stockholm, Sweden
2017
Private
https://www.hopsworks.ai/
https://x.com/hopsworks
https://www.linkedin.com/company/hopsworks/
