Tracking AI System Health

What Makes AI Monitoring Different

Traditional monitoring asks: Is the server up? Is latency acceptable? Are error rates normal? AI monitoring asks all of that plus: Are the responses accurate? Is the model behaving as expected? Are we spending more than we should?

A 200 OK response from your AI endpoint might contain complete nonsense. Your monitoring needs to catch that. This is the fundamental difference — in AI systems, "working" and "working correctly" are two very different things.

What to Track

Latency per AI call: Track p50, p95, and p99 latency for every AI provider call. LLM responses can vary from 500ms to 30 seconds — know your distribution.

Token usage: Log input tokens, output tokens, and total tokens for every call. This directly maps to cost and helps you identify expensive prompts or unexpectedly verbose responses.

Cost per request: Calculate and log the actual dollar cost of each AI operation. Aggregate by user, feature, and time period.

Error rates by provider: Track 4xx and 5xx responses from each AI provider separately. If one provider's error rate spikes, you want to know immediately — especially if you have fallback logic.

Cache hit rates: If you're caching AI responses (you should be for common queries), track how often the cache serves a response vs. making a fresh API call. Low cache hit rates mean you're spending more than necessary.

Response quality signals: Track user feedback (thumbs up/down), response length anomalies, and any automated quality checks you run on outputs.