How do I avoid blowing up Prometheus with labels like commit and version?

Use stable labels (`service`, `version`, `env`, `region`, `canary`) and keep churn bounded. Avoid per-request/user IDs in metrics. Drop high-cardinality labels at the scrape or via relabeling, and store them in traces/logs instead. Enforce a label budget per team.

Can I do this without Prometheus/Grafana?

Yes. The same principles apply in Datadog, New Relic, Honeycomb, or Elastic. The key is consistent dimensions across telemetry types and the ability to query them together. Honeycomb’s traces-first model makes correlation fast; Datadog has deployment events and monitors you can hook to rollbacks.

What about AI systems with non-deterministic behavior?

Use leading indicators tailored to LLM workloads: token-per-second throughput, cache hit rate (prompt/embedding cache), upstream latency to model endpoints, and `429`/throttle rates. Canary on top-K user journeys and watch hallucination proxies (e.g., high answer entropy + low retrieval overlap) before full rollout.

How do I justify the investment?

Track MTTR, change failure rate, and error-budget burn before/after. Most teams recoup the effort in reduced outages and faster rollouts within a quarter. Tie it to business KPIs (conversion rate during deploys, ingestion freshness) to make it non-optional.

Reliability-observability · Oct 23, 2025 · 10 minute read

Correlation That Saves Your On-Call: Turning Symptoms into Root Cause (and Automated Rollbacks)

Forget dashboards that look pretty but tell you nothing. Here’s how to design correlation that predicts incidents, ties telemetry to triage, and drives automated rollouts and rollbacks.

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Correlation That Saves Your On-Call: Turning Symptoms into Root Cause (and Automated Rollbacks)

Key takeaways

Implementation checklist