Key Takeaway
By the end of this blueprint you will have an AI observability stack that captures distributed traces across LLM calls and tool invocations using OpenTelemetry, feeds cost attribution dashboards in Grafana, runs automated quality scoring with LLM-as-judge evaluators, and alerts on regressions before users notice.
Prerequisites
- An LLM application in production (or staging) generating real traffic
- Docker Compose for running the collector, Prometheus, and Grafana locally
- Python 3.11+ with the OpenTelemetry SDK installed
- Familiarity with distributed tracing concepts (traces, spans, attributes)
- Optional: a Langfuse or LangSmith account for managed LLM tracing
Why Traditional APM Falls Short
Traditional APM tools track request latency, error rates, and throughput. These are necessary but insufficient for AI applications. An LLM call can return HTTP 200 with a perfectly structured response that is factually wrong, off-brand, or unsafe. You need three additional metric dimensions: quality (is the output good?), cost (what did this call cost and who should pay for it?), and safety (does the output violate any policies?). AI observability layers these dimensions on top of standard infrastructure metrics.
Architecture Overview
The stack is built on OpenTelemetry for trace collection, with custom span attributes for LLM-specific metadata such as model name, token counts, and prompt versions. Traces flow into a collector that fans out to a time-series database for metrics, a search index for trace exploration, and an evaluation pipeline that periodically scores sampled outputs for quality and safety.
Instrumenting LLM Calls with OpenTelemetry
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