llm-observability

To monitor LLM application performance and token costs, you need specialized observability that tracks input/output token counts, per-call cost, and latency percentiles for non-deterministic LLM workloads. This provides end-to-end trace collection and alerting for production RAG pipelines and agent systems.

Traditional APM fails for monitoring RAG pipelines because it is built for deterministic systems with fixed latency and predictable costs. LLM applications require observability for non-deterministic outputs, variable token-based pricing, and prompt versioning that traditional tools cannot track effectively.

Yes, you can track prompt versioning and link production traces to evaluation datasets. This allows you to version prompts like code, track performance changes across prompt iterations, and identify which prompt versions reduce hallucination rates in your LLM application.

Yes, this observability approach integrates with LangSmith, LangFuse, and Phoenix/Arize to trace LLM calls, RAG pipeline steps, and agent workflows. It captures LLM-specific metrics like cache hit rates, rate limits, and context length breaches across any LLM provider.

The best way to catch latency spikes and monitor daily cost against budget thresholds is to implement LLM-specific metric collection and performance alerting. This traces retrieval steps in RAG pipelines and categorizes LLM-specific errors like content filter blocks to maintain application reliability.