H2O.ai vs H2O Driverless AI
H2O.ai
h2o.aiH2O Driverless AI
h2o.aiSide by side
| Figure | H2O.ai | H2O Driverless AI |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| iOS | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Data Science and Machine Learning Platforms | 7.5 (#10) | 7.8 (#8) |
| Deployment | H2O AI Cloud is available as H2O.ai-managed cloud or as a self-hosted deployment in a private cloud or on-premises. | Models can be deployed as REST endpoints, cloud services, or optimized Java code for edge devices. |
| Founded | 2012 | 2012 |
| Headquarters | Mountain View, California, United States | Mountain View, California, United States |
| Integrations | The AI App Store shares identity and storage with other H2O AI Hybrid Cloud components and integrates with H2O MLOps and AI Engines. | Documented integrations include Kubernetes, Kubeflow, SageMaker, Alteryx, Jupyter/Python, KDB, Qlik, Trifacta, and UiPath. |
Plans
In their own words
H2O.ai
H2O.ai provides software for generative AI, predictive AI, machine learning operations, and AI application development. H2O AI Cloud combines automated machine learning with no-code deep learning in an end-to-end environment for creating, deploying, monitoring, and sharing models and applications. AutoML handles feature engineering, validation, model selection, and tuning, while H2O MLOps validates models and monitors performance. Deployment can use H2O.ai’s managed cloud, a private cloud, or on-premises infrastructure, including Google Cloud Platform, Amazon Web Services, and Microsoft Azure. h2oGPTe supports multimodal, agentic, code-assistant, citation-RAG, guardrail, and model-routing capabilities, with responses grounded in private repositories, knowledge bases, and databases. H2O LLM Studio provides no-code language-model fine-tuning. H2O Wave is an open-source Python framework, and H2O-3 is Apache-licensed for Python and R. The platform supports SQL, natural-language queries, visualizations, team collaboration, and CSV or Parquet exports.
H2O Driverless AI
H2O Driverless AI automates machine-learning work from feature engineering and model building through validation, tuning, selection, visualization, interpretability, and scoring-pipeline generation. It explains model output at both global and individual-prediction levels. Models can be deployed as REST endpoints, cloud services, or optimized Java code for edge devices. Supported hardware includes NVIDIA GPUs, IBM Power 9, and Intel x86 CPUs; GPU-accelerated algorithms include XGBoost, TensorFlow, and LightGBM. Documented integrations include Kubernetes, Kubeflow, SageMaker, Alteryx, Jupyter/Python, KDB, Qlik, Trifacta, and UiPath. Data sources include HDFS, Amazon S3, Azure Blob Storage, Google BigQuery, Google Cloud Storage, Snowflake, JDBC, Databricks, and Delta Table. Cloud installation options are H2O AI Cloud, AWS, Azure, and Google Cloud. H2O offers a 21-day public-cloud evaluation license and a guided two-hour cloud test drive. Commercial licensing requires contacting sales; no price is listed. The default installation disables security, so production use calls for secure installation.