AI That Gets Better With Every Run

Oumi now connects development, deployment, and production improvement into a continuous loop.

Teams can evaluate, train, deploy, learn from production traffic, and improve their models in one environment. The workflow keeps model development tied to the behavior teams see in production.

Improve models from production data

Production traffic can reveal failure modes that become data for the next evaluation and training cycle. Teams can use those signals to improve a model without rebuilding the workflow from scratch.

Evaluate models holistically

Model Playground makes it possible to compare open and closed models, off-the-shelf models, specialized models, and agents. Teams can evaluate task performance alongside latency and token usage.

Deploy and scale with one click

Deployments can scale from zero GPUs when idle to hundreds of GPUs as demand grows. That keeps capacity aligned with real usage.

A new developer workflow

The Oumi CLI brings training, evaluation, and deployment into the terminal. Coding agents can use the same interface, so teams can build and operate model workflows from the tools they already use.