Data science & ML platforms · H2O.ai

H2O.ai

Open-source machine learning library and framework, paired with commercial enterprise AutoML and MLOps products.

Visit H2O.ai Alternatives

H2O.ai's core offering is H2O-3, an open-source, distributed machine learning library (Apache-2.0) that supports algorithms such as gradient boosting, random forests, and generalized linear models, usable from R, Python, or a web UI, and scalable across clusters. Around this open-source core, H2O.ai sells commercial products: Driverless AI for automated feature engineering and AutoML, and H2O AI Cloud for managed, end-to-end model building, deployment, and monitoring. It also publishes h2oGPT and other open generative AI tooling. Commercial products are sold as self-hosted or cloud-managed deployments through direct sales, with pricing based on seats, compute, or workload rather than a published price list.

At a glance

Vendor H2O.ai
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Teams wanting a free, scalable open-source ML core with an optional path to commercial AutoML/MLOps.

Pricing

The H2O-3 core library is free and open source; Driverless AI and H2O AI Cloud are commercial products sold by quote.

Pricing has not been verified yet — see the vendor's site.

Features

  • Open-source distributed ML library (H2O-3)
  • Automated feature engineering and AutoML (Driverless AI)
  • Managed end-to-end ML platform (H2O AI Cloud)
  • Model interpretability and explainability tools
  • R, Python, and web UI access
  • Distributed/cluster-scale training
  • Open generative AI tooling (h2oGPT)

Integrations

Profile last reviewed September 21, 2026

Alternatives

DataRobot Enterprise AutoML and generative AI platform for building, deploying, and governing predictive models. Dataiku Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists. Amazon SageMaker AWS's fully managed service for building, training, and deploying machine learning models at cloud scale.

H2O.ai in the index now

Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Predictive analytics Analytics that uses historical data and statistical models to estimate what is likely to happen next. Regression analysis A statistical method for estimating the relationship between one outcome variable and one or more predictor variables. AutoML (automated machine learning) Tooling that automates parts of building a machine learning model, such as feature selection, algorithm choice and hyperparameter tuning. Feature engineering Creating, transforming or selecting the input variables a model is trained on, to make the underlying pattern easier for it to learn. Gradient boosting An ensemble method that builds trees one at a time, each one correcting the errors made by the trees before it.

Related guides

How to choose a data science & ML platform The decisions that matter are who builds the models, how automated the modeling should be, and whether you want a cloud-native platform or a portable one.