LLM Observability: Monitoring Production AI Before Users Notice Problems

You can't fix what you can't see

Production LLM apps fail silently — hallucinations, latency spikes, and cost overruns show up in user complaints before they hit your dashboard. Observability is the difference between AI demos and AI products.

Four pillars of LLM observability

• Traces: Full request paths — prompt, retrieval, tool calls, and final output with latency breakdowns.
• Evals in CI: Automated quality checks on every prompt or model change before deploy.
• Cost tracking: Per-feature token spend so product teams own their inference budget.
• Drift alerts: Flag when input distributions or output quality shift from baseline.

Build observability from sprint one

Retrofitting monitoring after launch is expensive. Nanostack ships LLM apps with tracing, eval harnesses, and dashboards baked in — start a project with observability as a requirement, not an afterthought.