Production AI Monitoring & Observability

You cannot improve what you cannot see. And with AI systems, what you cannot see can actively harm your users. A model that silently degrades over time. A RAG pipeline that starts returning irrelevant context. An agent that loops indefinitely on 2% of requests. Without proper observability, these problems only surface when users complain — or leave.

What to Monitor

Quality Metrics — Track output quality continuously, not just at deploy time. This means automated evaluation against benchmark datasets, user feedback signals (thumbs up/down, corrections), and anomaly detection on output distributions.

Latency — P50, P95, and P99 latency for every stage of your AI pipeline. Time-to-first-token for streaming responses. End-to-end latency from user input to final output. Set alerts on percentile degradation, not just averages.

Cost — Per-request cost, per-user cost, and total daily/weekly spend. Break down by model, endpoint, and feature. Alert on unexpected spikes before they become budget crises.

Error Rates — Model errors, timeout rates, retry rates, and fallback activation rates. Track these at the pipeline level, not just the model level.

Security Events — Prompt injection attempts, PII in outputs, policy violations, and unusual access patterns.

The Observability Stack

We typically implement monitoring using a combination of purpose-built AI observability tools (Langfuse, Langsmith, or custom solutions) integrated with your existing infrastructure monitoring (Datadog, Grafana, CloudWatch). The AI-specific layer tracks model behaviour and quality. The infrastructure layer tracks system health and cost.

Supporting Technical Guides