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Every call your deployment serves can be recorded, giving you the actual prompts your model receives in production. Those logs are useful twice: first for debugging what went wrong on a specific request, then as raw material for your next training run.

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ENABLE LOGGING

Inference logging is a per-deployment setting, on by default, that records the requests and responses your deployment serves. Turn it off to keep production traffic out of the logs entirely. It works the same way for models you train and for external models served by OpenAI or Anthropic. Logging also feeds the deployment’s observability features: health metrics are computed from the logs, and quality monitoring scores a sample of them. Turning logging off turns those off too. Turning logging on affects future requests. Traffic served while it was off was never recorded and cannot be recovered.

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READ THE LOGS

The Inference Logs tab lists one row per request, with a preview of the exchange alongside its status, timing, and token counts. Open a single entry to see the full input and output messages, the token counts and latency for that call, and the error message if it failed.
To debug a failure, open the failed request and read its error message alongside the input that produced it.

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EXPORT LOGS TO A DATASET

This is how the loop closes. Take a slice of real production traffic and turn it into a dataset you can evaluate against, synthesize from, or fine-tune on. From the Inference Logs tab, click Export as Dataset:
  1. Choose Create new dataset and give it a name, or switch to the other tab to append to an existing dataset.
  2. Set Maximum Logs. The most recent logs are included first, so this acts as a “latest N requests” cap.
  3. Set a Start Date and End Date to bound the window.
  4. Click Generate Dataset.
The export runs in the background, and the dataset appears on your Datasets page when it finishes. You can also ask the Oumi Agent to export logs for you, and to summarize traffic or find failing calls without leaving chat.

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WHAT’S NEXT

Quality monitoring

Score a sample of these logs with LLM judges and watch quality trends.

Explore your exported dataset

Inspect and validate the dataset your exported logs produced.