Measuring and Monitoring LLM Performance
Design monitoring strategies to track latency, cost, and quality metrics in LLM applications.
Effective monitoring of LLM applications tracks multiple dimensions: latency (response time), throughput (requests per second), cost (tokens consumed), cache hit rates, error rates, and output quality. Developers should establish baseline metrics before optimization and track changes after implementing caching, routing, or format changes. Key metrics include: end-to-end latency, model inference latency, network latency, input/output token counts, cache effectiveness, and user satisfaction. Monitoring infrastructure should capture these metrics at scale and enable rapid detection of performance degradation. Observability tools integrated with AWS CloudWatch or similar services help identify bottlenecks and validate optimization impact.
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