Real-time Model Performance Monitoring: Metrics That Matter

Effective monitoring is crucial for maintaining high-quality LLM applications. Without proper observability, performance degradation can go unnoticed until users complain.

Key metrics to track include latency (p50, p95, p99), error rates, token usage, and cost per request. These operational metrics provide immediate visibility into system health.

Quality metrics are equally important but harder to measure. Techniques include automated evaluation using reference models, user feedback collection, and sampling responses for human review.

Model-specific metrics help identify when particular providers are underperforming. Track success rates, average response quality, and cost-effectiveness for each model in your orchestration layer.

Set up alerting for anomalies: sudden latency spikes, error rate increases, or quality degradation. Automated responses like temporary model switching can maintain service quality while issues are investigated.