A team deploys a generative AI application that uses a model deployed in Microsoft Foundry.

Microsoft ' s observability guidance for Azure AI Foundry applications describes a three-stage activation sequence. First, enable Tracing in the Microsoft Foundry project settings before deployment — Tracing instruments the application ' s LLM calls with OpenTelemetry-compatible spans that capture timing data for each step in the flow. Second, deploy the application to a production endpoint so that real traffic flows through the instrumented code path — without actual production traffic, there is no latency data to observe. Third, configure Azure Monitor and Application Insights to receive, aggregate, and visualize the telemetry emitted by Tracing. Azure Monitor ' s metrics explorer and Application Insights ' performance views display p50, p95, and p99 latency distributions over time, enabling the team to identify latency regressions and set alert thresholds. This sequence — instrument, deploy, visualize — is the canonical Microsoft path to production AI performance observability.

Microsoft Learn Reference Topic: Monitor generative AI applications with Azure Monitor and Microsoft Foundry Tracing