Why not just alert on 5xx rate?

Because it’s a lagging indicator—customers are already impacted. Use 5xx rate as part of your SLO burn rate to page humans, and rely on leading indicators (throttling, queue backlog, p99 latency) to predict and auto-mitigate before customers feel it.

Isn’t this going to spam my team with alerts?

Not if you split signals: predicted alerts route to Slack/Jira with automation, while only SLO burn rate pages humans. Multi-window burn rules dramatically cut false pages while catching real impact early.

We’re on Datadog/New Relic/Honeycomb. Do we need Prometheus?

No. The strategy is tooling-agnostic. Datadog has anomaly and composite monitors, Honeycomb has SLOs and Burn Alerts, and New Relic supports NRQL-based multi-window logic. We show Prometheus because it’s easy to demo and widely used.

How do we add rollout metadata to metrics?

Inject labels via your metrics SDK or OpenTelemetry `resource` processor. In Kubernetes, annotate deployments with version and rollout IDs and propagate them to metrics/traces via environment variables or OTEL resource detectors.

What if automated rollback makes things worse?

Scope automation to canaries and well-understood metrics, keep failure limits low, and test during business hours with chaos drills. Argo Rollouts and Flagger both support conservative step-ups and quick aborts.

Reliability-observability · Nov 9, 2025 · 9 minute read

We Cut MTTD From 14 Minutes to 90 Seconds by Alerting on What Fails Next, Not What Looks Pretty Now

Stop paging on dashboards. Start predicting breakage. Here’s the telemetry, configs, and rollout glue that actually reduces mean time to detection.

Back to all posts

We Cut MTTD From 14 Minutes to 90 Seconds by Alerting on What Fails Next, Not What Looks Pretty Now

Key takeaways

Implementation checklist