LLM observability covers monitoring, tracing, and evaluating LLM applications to catch hallucinations, control token costs, and diagnose latency issues. Key metrics include token consumption, latency, context recall, faithfulness, and decision quality. The post explains how observability tools work across five steps: instrumentation, data collection, workflow mapping, anomaly detection, and alerting. Best practices include end-to-end lifecycle tracing, cost allocation by team or product, real-time anomaly detection, combining automated evaluations with user feedback, and integrating LLM costs with broader cloud spend. The post concludes with a pitch for Finout's FinOps platform as a solution for unified AI cost observability.
Table of contents
Why LLM Observability MattersLLM Observability vs. LLM MonitoringHow LLM Observability Tools WorkKey LLM Observability MetricsBest Practices for LLM ObservabilityGain Full LLM Cost Observability with Finout20 Impressions