
H2O.ai
6.2#10 in mlops platformsiOSWebAPISelf-hosted
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| Fact | H2O.ai | Weights & Biases |
|---|---|---|
| Audience | H2O.ai describes H2O AI Cloud as serving businesses, government entities, nonprofits, and academic institutions. | The maker presents the platform for AI developers and highlights use cases including model training, fine-tuning, AI application development, and production monitoring. |
| Deployment | H2O AI Cloud is available as H2O.ai-managed cloud or as a self-hosted deployment in a private cloud or on-premises. | W&B offers multi-tenant SaaS, isolated dedicated cloud and customer-managed cloud or on-premises deployments. |
| Founded | 2012 | LinkedIn lists the company as founded in 2017. |
| Headquarters | Mountain View, California, United States | San Francisco, 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. | W&B is designed to integrate with customers' ML stacks and tools and offers managed or self-managed deployment choices. |
| Product | H2O AI Cloud is an end-to-end platform for creating, deploying, monitoring, and sharing AI models and applications. | Weights & Biases describes itself as an AI developer platform for building AI agents, applications, and models. |
| 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 SaaS deployment states SOC 2 Type 2 certification, team-level data isolation and SSO support. |
| Support | H2O.ai offers a support portal and says customers can connect with its experts for tailored enterprise support. | Support tiers are Standard, Standard Plus and Premium; Premium adds 24/7 coverage, faster response targets, roadmap sessions and engineering access. |
No prices published.
H2O.ai describes its cloud platform as serving businesses, government entities, nonprofits and academic institutions. It may also suit teams seeking self-hosted deployment or open-source machine-learning tools.
It suits AI developers working on model training, fine-tuning, AI applications, or production monitoring. The free tier may fit smaller projects, while larger teams should note the stated Pro-to-Enterprise transition guideline.