llm-cost-analysis

To analyze LLM call logs, evaluate ai_call_logs to identify costly operations, latency hotspots, and success-rate patterns across capabilities like topic_tagging, tagging-flow, and embedding.

The best way to diagnose AI call latency is to analyze per-operation hotspots in your ai_call_logs, isolating specific workflows like topic_tagging that drive delays, then reviewing recommended mitigations.

You can find costly LLM operations by running a global overview of your LLM usage to rank per-operation hotspots, exposing which specific capabilities consume the most resources and drive costs.

Yes, you can identify error patterns by analyzing success-rate metrics across capabilities within your ai_call_logs, mapping failure trends to actionable optimization steps and best practices.

This LLM usage analysis specifically targets ai_call_logs containing call frequency, latency, and success rate data to accurately locate operational hotspots and provide optimization guidance.