Redefine availability
Count requests slower than your SLO threshold as partial downtime. A 200 in 9 seconds still feels down to customers trying to check out.
Track by endpoint, user segment, and region so you know which teams to page. Mobile, web, and API consumers often perceive slowness differently.
Publish the definition so support, product, and engineering agree on when "slow" becomes an outage.
Tune alerts
Trigger alerts on latency percentiles and error rate together to avoid false positives. Alert when p95 or p99 breaches for a sustained window, not on single spikes.
Use dependency dashboards to spot which service is slow. Correlate app latency with DB queries, external APIs, and CDN cache hit rates to find bottlenecks fast.
Run synthetics that include JS execution and third-party tags so you capture frontend latency too.
Key metrics
- p95 and p99 latency per endpoint and region
- Saturation (CPU, worker queues, DB connections)
- Error percentage and retries
Communicate impact
Mention latency in status updates so customers understand degraded states. Clarify which journeys are slow and offer temporary workarounds.
Tie latency regressions to burn rate to drive urgency, especially when SLOs include response-time thresholds.
Close the loop after fixes: share before/after charts and what you changed (indexes, caching, CDN config) to restore trust.
Improve before peak
Load test before seasonal spikes using production-like data and third-party integrations. Set budgets for third-party tags and block or lazy-load the slow ones.
Cache expensive reads, precompute reports, and shorten cold-start paths so you have headroom when demand surges.