An Azure machine learning service for building and deploying models.
The documented metrics do not state that Azure Content Safety input/output analysis causes Model Requests to be counted twice.
What is supported is:
-
Model Requestscounts calls made to the model inference API over a period of time. - In Azure Monitor, that metric can be filtered and split by dimensions such as
ModelDeploymentName,ModelName,ModelVersion, andStatusCode. - If comparing dashboards, a mismatch is often caused by different scope, filters, dimensions, or time range.
To validate the discrepancy end to end:
- In the Azure portal, go to Monitor > Metrics.
- On Select scope, select the same resource used by the Foundry deployment view. If selecting at resource group or subscription level, ensure Resource types is set to Foundry Tools.
- Select the Models metric category, and use Model Requests.
- Add filters for the exact ModelDeploymentName and, if needed, split by StatusCode.
- Confirm the time range matches the external dashboard exactly.
- If the external dashboard is showing only successful API calls, compare that against Azure Monitor split by StatusCode, because
Model Requestsincludes calls to the model inference API over the selected period.
If the Foundry/Monitor view still shows about 2x after scope, deployment filter, status code split, and time range are aligned, the provided documentation does not identify Content Safety as the reason.