Tracing and Observability for LLM Apps

LLM observability is the practice of instrumenting your AI applications to collect, visualize, and analyze traces, logs, and metrics from every step of a language model inference or chain. Unlike traditional application monitoring, LLM observability must account for token economics (cost per input and output tokens), latency variability across API providers, multi-step reasoning chains, and failures that may be silent until they compound across a user conversation.

This 10-article series takes you from foundational concepts through advanced tracing patterns used in production systems at companies deploying thousands of daily inference calls. You will learn structured logging practices that enable AI agents to emit queryable, JSON-formatted events; distributed tracing that maps the path of a request through multi-step chains and external API calls; metrics collection for token counts and latency; and debuggable alerting rules that catch cost spikes or quality degradation before they hit your SLA. By the end, you will be equipped to instrument an LLM app with OpenTelemetry, correlate errors across services, and optimize token spend through data-driven observability insights.

Articles in this series

1. What Is LLM Observability? Monitoring AI Apps from Logs to Traces
2. Structured Logging for LLM Applications: JSON Logs & Context
3. Distributed Tracing Basics: Follow a Request Through Your LLM Chain
4. Token Counting & Latency Metrics: Measure LLM Performance in Real Time
5. OpenTelemetry for LLMs: Instrument Your AI Chains
6. Building an LLM Observability Dashboard: From Langfuse to Production
7. Error Tracking & Root-Cause Debugging for LLM Failures
8. Alerting on LLM Degradation: Cost Spikes, Latency, and Quality
9. Cost Optimization Through Observability: Reduce LLM Token Spend
10. Advanced LLM Tracing: Agent Spans, Multi-Step Chains & Ray Tracing