Observability Best Practices: SLOs, Tracing, and Alert Design

Cost without questions is waste

Observability spend balloons when every service logs at debug and traces at 100%. The question is not whether you have dashboards — it is whether those signals shorten incidents enough to justify the bill.

We tie instrumentation investment to user-facing SLOs. If a metric never informs a decision or alert, it is a candidate for deletion.

During incident reviews, ask which signal actually shortened recovery. Keep funding those; sunset the rest.

SLOs that drive behaviour

Pick a few latency and availability SLOs customers would recognise. Error budgets create permission to move fast and a forcing function to stop when reliability debt accumulates.

Alert on symptoms users feel, not on every disk spike. Page-worthy alerts should be rare, actionable, and tied to runbooks.

During incident reviews, ask which signal actually shortened recovery. Keep funding those; sunset the rest.

Tracing with intentional sampling

OpenTelemetry everywhere, sample intelligently. Keep head sampling high for rare error classes and critical paths; reduce volume on chatty success traffic.

Exemplars that link metrics to traces turn "p99 is high" into a specific slow dependency within minutes.

During incident reviews, ask which signal actually shortened recovery. Keep funding those; sunset the rest.

Noise reduction as an engineering goal

Track alert volume and acknowledge rates. If on-call ignores a class of alerts, fix the signal — do not hire people to endure it.

Observability that pays for itself shows up as lower MTTR and fewer repeat incidents, not as a prettier flame graph.

During incident reviews, ask which signal actually shortened recovery. Keep funding those; sunset the rest.

Delete signals that never decide

Quarterly, remove metrics and traces that never informed an alert or incident decision. Observability spend should buy lower MTTR, not prettier unused dashboards.

Tie instrumentation to a few customer-recognisable SLOs and page only on symptoms users feel.

During incident reviews, ask which signal actually shortened recovery. Keep funding those; sunset the rest.

Alert on user symptoms, not every spike

Pick a few latency and availability SLOs customers would recognise. Error budgets create permission to move fast and a forcing function to stop when reliability debt accumulates.

Page-worthy alerts should be rare, actionable, and tied to runbooks. If on-call ignores a class of alerts, fix the signal.

During incident reviews, ask which signal actually shortened recovery. Keep funding those; sunset the rest.

Sample traces with intent

OpenTelemetry everywhere, sample intelligently. Keep higher sampling for errors and critical paths; reduce volume on chatty success traffic.

Exemplars that link metrics to traces turn “p99 is high” into a specific slow dependency within minutes.

During incident reviews, ask which signal actually shortened recovery. Keep funding those; sunset the rest.

Track alert volume as an engineering metric

Watch acknowledge rates and noise. Observability that pays for itself shows up as lower MTTR and fewer repeat incidents — not as a prettier flame graph nobody opens.

During incident reviews, ask which signal actually shortened recovery. Keep funding those; sunset the rest.