H2O.ai

Founded 2012

Build, deploy, and govern machine learning and generative models inside private infrastructure

You can use H2O.ai to build, run, and govern predictive models, generative applications, and autonomous agents entirely within your own infrastructure. It lets organizations deploy machine learning systems on-premises, across private clouds, or in fully air-gapped setups without exposing confidential data to external vendors. You can connect various open-source or commercial models, monitor drift, detect fraud, analyze complex documents, and automate repetitive tasks across regulated environments. The system includes observability tracking to evaluate accuracy and maintain strict compliance with data security standards.

What H2O.ai does

Train predictive models using custom internal datasetsDeploy machine learning agents to private serversQuery document archives with private retrieval-augmented generationMonitor model performance and trigger drift alertsEvaluate language model outputs for factual accuracyFine-tune small language models on proprietary company recordsBuild automated multi-step agent workflowsRuns on-premises, in private clouds, or inside fully air-gapped environmentsConnects with any large language model or open-source machine learning modelMonitors deployed models for data drift, accuracy, and operational healthAutomates retrieval-augmented generation and document question answeringCoordinates multi-agent workflows across generative and predictive systemsCharges based on compute and memory rather than token countsMaintains compliance standards including SOC 2, HIPAA, and FedRAMP High

Frequently asked

Snowflake (Native App), Databricks (Sparkling Water), AWS SageMaker, Azure Machine Learning, Google Cloud Vertex AI, Kubernetes, Teradata, Python/R Clients, Databricks, Python / R Clients, Tableau

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