How to Trace GPT-4o Apps With MLflow 3 and OpenTelemetry
MLflow 3's GenAI tracing SDK and the OpenTelemetry Collector together give you production-grade observability for any LLM application — with zero code changes required. This guide covers end-to-end architecture, the two instrumentation paths, Apple Silicon pitfalls, real span data, and a complete troubleshooting playbook.
You deployed a GPT-4o-powered feature to production. Three days later, latency spikes. Token costs double overnight. A prompt change silently degrades output quality. Without observability, you are flying blind.
The problem isn't just logging. It's structured, correlated telemetry that connects each LLM call to a trace, a run, an experiment — so you can reproduce failures, compare model versions, and prove that your prompt engineering actually improved things.
That's what this stack delivers: MLflow 3 + OpenTelemetry Collector + GPT-4o, wired together into a local-first observability pipeline you can run on your laptop today and scale to production tomorrow.
📦 Source code:github.com/anjijava16/mlflow_-observability_sdk_otel
Key Takeaways
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