Hopsworks AI Lakehouse is a platform for building batch, real-time, and LLM AI systems on open standards. It supports Apache Iceberg, Delta Lake, and Apache Hudi, and streaming feature pipelines connect to a low-latency serving database with sub-second feature freshness. Its Query Service transfers data to Python clients using Arrow and supports temporal joins, pushdown filters, credential vending, and reproducible training-data creation. For LLM workflows, teams can query Lakehouse data from prompts, integrate vector search for retrieval-augmented generation, and create instruction datasets for fine-tuning while maintaining data governance. Feature, training, and inference pipelines cover batch, streaming, real-time, and LLM workflows, with trained models output to a model registry. Deployment choices include managed SaaS, managed Kubernetes on AWS, Azure, or GCP, and air-gapped data centres. Security features include encryption at rest and in transit, project-based access controls, audit logs, 2FA, and SSO. The free plan is free and includes one project, Feature Store, Model Registry, and community support.
Who it is for
The platform suits developers, data scientists, platform engineers, security engineers, and administrators working on AI systems. The free tier provides a one-project starting point, while SaaS and Enterprise plans cover additional deployment and support options.
What is good
- Supports three open table formats
- Streaming features have sub-second freshness
- Free plan includes Feature Store and Model Registry
- Offers managed and air-gapped deployment options
What to know first
- Free plan is limited to one project
- SaaS uses usage-based pricing
- Enterprise pricing is custom
Verdict
Hopsworks AI Lakehouse brings data storage, feature pipelines, and AI workflows together across batch, streaming, and LLM use cases. Its free tier is limited to one project, while expanded plans use usage-based or custom pricing.
Get started with Hopsworks AI Lakehouse
- Open the Hopsworks AI Lakehouse product page.
- Choose the Free, SaaS, or Enterprise plan for your deployment needs.
- Use the Free plan without a credit card, or choose SaaS pay-as-you-go pricing.
- For Kubernetes deployment, use Kubernetes 1.27 or higher; a minimum of 4–5 nodes is recommended.
- Connect supported systems such as Airflow, Spark, or Snowflake, or use the Python Query Service.
What the free plan stops at
The Free plan allows one project and includes Feature Store, Model Registry, and community support. Model Serving and unlimited projects are included in SaaS; dedicated support, air-gapped deployment, and a guaranteed SLA are Enterprise features.
Questions about Hopsworks AI Lakehouse
Is Hopsworks AI Lakehouse free?
Yes. The Free plan is free and includes one project, Feature Store, Model Registry, and community support. No credit card is required.
What platforms does it support?
Listed platforms are API, Linux, self-hosted, and web. Deployment options include managed SaaS, managed Kubernetes in AWS, Azure, or GCP, and air-gapped data centres.
What data formats does it support?
It natively supports Apache Iceberg, Delta Lake, and Apache Hudi.
Which integrations are listed?
Listed integrations include Airflow, Apache Flink, Databricks, Snowflake, AWS SageMaker, Spark, Vertex AI, and Weights & Biases.
What are the SaaS and Enterprise prices?
The SaaS plan is billed pay as you go with usage-based pricing; no price is listed. Enterprise is billed custom, and no price is listed.
What support comes with each plan?
Free and SaaS include community support. Enterprise includes a dedicated support team and guaranteed SLA.
Hopsworks AI Lakehouse plans and pricing
All plansCompared on ML experiment tracking software
- Free plan
- Yes
- Run comparison
- Yes
- Artifact tracking
- Yes
- Dataset versioning
- Yes
- Model registry
- Yes
- API and SDK access
- Yes
