Observability Patterns for AI Services
A compact playbook for tracing quality, latency, and cost across AI-powered backends.
2/24/2026-1 min read-IT Architecture-Mubin Ahmed
AI services need a wider observability surface than conventional APIs.
Track quality and performance together
Quality drift and latency regressions often appear at the same time.
Minimum telemetry set
Log prompts, model versions, response quality labels, latency, token usage, and failure reasons.
{
"requestId": "req_124",
"model": "gpt-4.1",
"latencyMs": 842,
"tokens": 1920
}Alert on meaningful thresholds
Alert fatigue is real, so align thresholds with product impact and user-visible failures.