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Small open-weight language and vision-language models, trained from scratch by H2O AI under Apache 2.0, built to be fine-tuned and to run inside your perimeter, even offline.
A model behind someone else's API sends every call outside your perimeter and bills you for each one. Open weights answer both. Small ones also answer where it can run.
Danube is the default backbone of H2O Cascade, and Enterprise h2oGPTe serves both families beside external LLMs. Highlighted: the open models and the tool that fine-tunes them.
Enterprise h2oGPTe, which ships the H2O AI Super Agent, can route each call to a model on latency, cost and accuracy. A guardrail check, a page read or a classification does not need a frontier model, and on Danube or Mississippi it does not leave the building.
Same platform either way: the same capabilities, the same governance, and the same audit trail. The choice stays open, so you can switch between them as the business changes, without rebuilding what runs on top.
The H2O AI Cloud for Government has achieved FedRAMP High Certification, delivering the highest standard of cloud security so federal agencies can harness predictive and generative AI without compromising data sovereignty or compliance.
H2O Cascade uses Danube3-500M as its default fine-tuning backbone, so a frontier model can label the data once and a small Danube model can serve the task from then on. Enterprise h2oGPTe serves Danube and Mississippi beside external LLMs and can route each call on latency, cost and accuracy. Cascade also tunes other open models, so Danube is a default, not a requirement.
We will run it through Mississippi inside your environment and show you the extraction, the cost, and where the data did not go.