H2O.ai vs ML Workspace
H2O.ai
h2o.aiML Workspace
mltooling.orgSide by side
| Figure | H2O.ai | ML Workspace |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| iOS | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Data Science Platforms | 7.6 (#13) | 8.3 (#7) |
| Deployment | H2O AI Cloud is available as H2O.ai-managed cloud or as a self-hosted deployment in a private cloud or on-premises. | The project provides Docker images and says they can be deployed on Mac, Linux, and Windows; Docker is required. |
| Integrations | The AI App Store shares identity and storage with other H2O AI Hybrid Cloud components and integrates with H2O MLOps and AI Engines. | Listed tools and integrations include Git, TensorBoard, Netdata, Jupyter, JupyterLab, and Visual Studio Code. |
| Product | H2O AI Cloud is an end-to-end platform for creating, deploying, monitoring, and sharing AI models and applications. | ML Workspace is a self-deployed, web-based IDE for machine learning and data science. |
| Security | H2O says it maintains SOC 2 Type II certification and uses encryption at rest and in transit, role-based access controls, monitoring, audits, penetration testing, vulnerability scanning, and code reviews. | The documentation describes token or basic authentication options and configurable SSL/HTTPS support. |
| Support | H2O.ai offers a support portal and says customers can connect with its experts for tailored enterprise support. | The maintainers say they cannot provide individual support by email and direct users to public support channels; the page lists [email protected] for other requests. |
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.
ML Workspace
ML Workspace is a self-deployed, web-based development environment for machine learning and data science. It brings browser-accessible Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI together, with libraries such as TensorFlow, PyTorch, Keras, and scikit-learn in its main image. Git-related tools include Ungit, Jupytext, nbdime, and a Jupyter extension for pushing notebooks. TensorBoard supports training monitoring, while Netdata and Glances cover hardware monitoring. The workspace can provide a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, typically through passwordless SSH, and can be accessed through a browser, SSH, or VNC. Image options include minimal, R, Spark, and GPU variants; the GPU flavor requires compatible Nvidia drivers and supports CUDA 11.2. Docker is required, with images deployable on Mac, Linux, and Windows. The project describes token or basic authentication and configurable SSL/HTTPS. It is designed for one user, and at least 2 CPUs and 500 MB of memory are recommended for stable use. The free plan costs nothing.