As LLM applications move to production, monitoring becomes critical for maintaining reliability and quality. Traditional application monitoring captures latency and error rates but misses AI-specific issues like output quality degradation, prompt injection attempts, and unexpected cost spikes. Comprehensive observability requires new metrics and monitoring approaches tailored to AI systems.
Essential LLM Metrics
Effective LLM monitoring tracks multiple dimensions simultaneously. Response quality metrics evaluate output appropriateness, completeness, and alignment with expected formats. Cost metrics track token usage per request and identify expensive outliers. Performance metrics monitor latency from both LLM APIs and supporting infrastructure. Safety metrics detect potential harmful outputs, prompt injection attempts, and policy violations.
- Track token usage distribution to identify cost anomalies and optimize prompt efficiency
- Monitor response validation failures that indicate output format drift
- Measure user satisfaction signals like thumbs up/down feedback on AI responses
- Alert on latency spikes that indicate API issues or infrastructure problems
- Log failed requests with full context for debugging and model improvement
Quality Assurance Systems
Automated quality checks validate LLM outputs before they reach users. Simple checks verify format compliance and required field presence. More sophisticated systems use secondary LLM calls to evaluate response appropriateness, factual accuracy, and safety. Critical applications may implement multi-model consensus where several models evaluate the same request, alerting on significant disagreements.
Cost Anomaly Detection
LLM costs can spike unexpectedly due to prompt errors, infinite loops, or adversarial inputs. Monitoring should track costs per request, per user, and per feature, alerting when usage patterns deviate from norms. Budget caps prevent catastrophic spending while allowing normal operation. Detailed cost attribution helps identify optimization opportunities and ensures fair internal cost allocation.