H2O.ai vs ZenML
H2O.ai
h2o.aiZenML
zenml.ioSide by side
| Figure | H2O.ai | ZenML |
|---|---|---|
| Price from | — | $999/mo |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| iOS | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 3 |
| Spec score · MLOps Platforms | 7.7 (#8) | 7.8 (#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. | ZenML supports local, SaaS, and self-hosted deployment, including Docker and Kubernetes Helm deployment methods. |
| Headquarters | Mountain View, California, United States | Munich, Germany |
| Integrations | The AI App Store shares identity and storage with other H2O AI Hybrid Cloud components and integrates with H2O MLOps and AI Engines. | The integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines. |
| 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. | ZenML states that it is SOC 2 Type II and ISO 27001 certified. |
| Support | H2O.ai offers a support portal and says customers can connect with its experts for tailored enterprise support. | The Open Source plan includes community support, Scale includes priority support, and Enterprise includes dedicated support with an SLA. |
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.
ZenML
ZenML orchestrates AI and machine-learning pipelines and agents written in Python. Pipelines can run on Kubernetes, Vertex AI, SageMaker, AzureML, or a laptop without rewriting code. ZenML versions step outputs such as models and evaluation datasets, supports re-execution from any step, and reuses cached results when code and inputs have not changed. Deployment choices include local, SaaS, and self-hosted setups, with Docker and Kubernetes Helm methods. ZenML says it stores metadata while data, artifacts, and compute remain in the customer's infrastructure or VPC. Its integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines. The self-hosted Open Source plan is free and includes unlimited executions and projects. Scale costs $999 a month and includes 2,000 executions, three projects, and five snapshots. Enterprise has custom pricing and adds SSO, custom-role RBAC, audit logs, and air-gapped deployment. ZenML says it is SOC 2 Type II and ISO 27001 certified.