By takumi_Sat0 takumi_Sat0
on AI Developer Tools

Hopsworks

The AI Lakehouse for real-time production ML

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Hopsworks
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Freemium

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4.3
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What Is Hopsworks?

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Hopsworks is a modern AI Lakehouse platform designed to support the full lifecycle of production machine learning, from data ingestion and feature engineering to deployment and monitoring. It offers a unified feature store, model registry, and MLOps tooling so teams can build, share, and reuse features efficiently across projects.

With real-time pipelines, low-latency feature access, and support for open table formats, Hopsworks is well-suited for demanding workloads like fraud detection, personalization, and LLM-powered applications. The platform serves industries such as financial services, retail and e-commerce, and government and defense, with SaaS, on-premises, and air-gapped deployment options.

Built for reliability, security, and scalability, Hopsworks helps organizations standardize AI infrastructure, accelerate time-to-production, and increase the impact of their ML investments.

Quick Snapshot

Hopsworks unifies feature store, model registry, and model serving in a single AI Lakehouse so teams can build, deploy, and scale real-time ML systems faster. It standardizes AI infrastructure, improves reliability, and helps organizations move models to production with lower risk and cost.

Works on
  • Web
  • Linux
  • API
Pricing Model
Freemium — Hopsworks provides a free tier with one project, feature store, and model registry and no credit card required. SaaS pricing is pay-as-you-go with usage-based billing, and enterprise plans offer custom on-premises and air-gapped deployments with dedicated support.
Fits on
Affiliate Program
We could not identify an affiliate program.
API Availability
Hopsworks has an API available.
Key Features
  1. Unify features, models, and serving in one lakehouse
  2. Power real-time ML with low-latency feature access
  3. Deploy securely across SaaS, on-prem, air-gapped
Audience
  • data scientists
  • machine learning engineers
  • MLOps engineers
  • data engineers
  • AI platform teams
  • enterprise IT teams
  • financial services organizations
  • retail and e-commerce companies
  • government and defense agencies
  • startups building AI products
URL
https://www.hopsworks.ai

Screenshot

Hopsworks

Key Features of Hopsworks

AI Lakehouse architecture

Provides a unified AI Lakehouse that integrates data, features, models, and MLOps workflows to support end-to-end production ML.

Unified feature store

Lets teams build, share, and reuse features across multiple models and projects with real-time and batch access patterns.

Model registry

Stores and manages machine learning models alongside their associated features to streamline deployment and lifecycle management.

Real-time pipelines

Supports real-time data and feature pipelines with low-latency access, enabling applications like fraud detection and personalization.

Model serving

Enables deployment and serving of models in production, integrated with the feature store for consistent and reliable predictions.

Enterprise deployment options

Offers SaaS, on-premises, and air-gapped deployments to meet security, compliance, and infrastructure requirements across industries.

Security and reliability

Built with enterprise-grade security and reliability in mind, helping organizations manage sensitive data and mission-critical ML workloads.

Support for open formats

Works with open table formats, giving teams flexibility in how they store and manage data used for features and models.

Use Cases for Hopsworks

Real-time fraud detection

Use low-latency feature access and real-time pipelines to power fraud detection models that respond to suspicious activity as it happens, improving risk control for financial services.

Personalization at scale

Centralize user and product features in the feature store to drive personalized recommendations and offers across retail and e-commerce channels in real time.

LLM and AI applications

Feed high-quality, reusable features into large language model and AI applications, standardizing data access and improving reliability across teams.

Enterprise MLOps standardization

Unify feature store, model registry, and serving in one platform so AI platform teams can create consistent MLOps workflows across business units and environments.

Government and defense analytics

Deploy machine learning workloads in secure on-premises or air-gapped environments while maintaining a modern AI Lakehouse architecture for sensitive data.

Frequently Asked Questions

What is Hopsworks used for?

Hopsworks is used to build and operate production machine learning systems by combining an AI Lakehouse, feature store, model registry, and model serving in a single platform.

Who should use Hopsworks?

Hopsworks is designed for data scientists, ML engineers, MLOps engineers, data engineers, AI platform teams, and enterprises in sectors such as financial services, retail, and government that need reliable, real-time ML infrastructure.

Does Hopsworks support real-time machine learning?

Yes, Hopsworks supports real-time pipelines and low-latency feature access, making it suitable for use cases like fraud detection, personalization, and other real-time AI applications.

Can Hopsworks be deployed on-premises or air-gapped?

Yes, Hopsworks offers on-premises and air-gapped deployment options in addition to SaaS, addressing strict security and compliance requirements.

Is there a free version of Hopsworks?

Yes, Hopsworks offers a free tier with one project, feature store, and model registry, and no credit card is required to get started.

Does Hopsworks provide an API?

Yes, Hopsworks includes APIs that allow teams to integrate its feature store, model registry, and MLOps capabilities into their existing data and ML workflows.

What industries does Hopsworks support?

Hopsworks supports multiple industries including financial services, retail and e-commerce, and government and defense, especially where real-time and secure ML workloads are required.

Hopsworks · Our Verdict

Hopsworks stands out as a focused platform for teams that need a serious feature store and real-time ML infrastructure rather than a generic data lake. Its combination of an AI Lakehouse, model registry, and model serving in one environment makes it particularly attractive for enterprises standardizing their MLOps stack.

For regulated or security-sensitive industries, the availability of on-premises and air-gapped deployments adds practical flexibility that many cloud-only tools lack.

Reviews 4.3 (1)

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