Hopsworks
Hopsworks is an AI Lakehouse platform designed to streamline the entire machine learning lifecycle, from feature engineering to model deployment and monitoring.
Our verdict
Who it's for
The unified AI Lakehouse platform for real-time ML feature engineering, deployment, and production.
Who should look elsewhere
Requires Kubernetes knowledge for on-premises deployments using the K8s Installer.
Pros
- +Achieves sub-millisecond latency for real-time feature retrieval and inference.
- +Reduces costs and development time by enabling extensive feature reuse across models.
- +Ensures data consistency between training and production environments, mitigating training-serving skew.
- +Offers flexible deployment models including SaaS, on-premises, and air-gapped for data sovereignty.
- +Integrates directly with open data formats like Iceberg, Delta, and Hudi without requiring data migrations.
Cons
- −Requires Kubernetes knowledge for on-premises deployments using the K8s Installer.
- −The full benefits of the platform are realized with a comprehensive understanding of ML engineering practices.
Key features
- ✓Feature Store with sub-millisecond retrieval latency (powered by RonDB)
- ✓AI Lakehouse supporting Iceberg, Delta, and Hudi tables
- ✓Python-native query engine for faster data reads
- ✓MLOps Platform for experiment tracking, model registry, and deployment pipelines
- ✓GPU and compute management for LLMs and ML models (with Ray, KServe/vLLM support)
- ✓Dual Storage System (Online RonDB for serving, Offline Hudi on HopsFS for training)
- ✓Point-in-Time Correctness for generating historical training datasets
- ✓Data Lineage and Feature Monitoring for audit trails and drift detection
Pricing
| Plan | Price | Billing | Notes |
|---|---|---|---|
| Free | $0 | month | 1 project included; Feature Store + Model Registry; No credit card required |
| SaaS | — | month | Unlimited projects; Feature Store + Model Registry; Model Serving |
| Enterprise | — | month | All SaaS features; On-premise & air-gapped; Dedicated support team |