01 · Challenge
The problem
Logs, metrics and alerts are scattered across systems. Engineers spend too long correlating errors and identifying service degradation.
Symptoms the team sees
- Customers report problems before monitoring does.
- Alerts fire constantly, so the team has learned to ignore them.
- Investigations start with “which server should I look at?”
- No one can say whether a recent release caused a spike in errors.
- The same incidents recur because follow-up actions are never tracked.
Root causes
- Logs written to local disk on individual hosts and containers.
- Metrics collected inconsistently, without standard labels for service or environment.
- Alerts based on resource thresholds (CPU, disk) rather than user-facing symptoms.
- No defined reliability targets or incident process.
02 · Approach
Implementation steps
- 01
Centralize logs
Ship structured application and infrastructure logs to a central ELK / OpenSearch stack with consistent fields and retention.
- 02
Metrics and dashboards
Deploy Prometheus exporters and Grafana dashboards for latency, traffic, errors and saturation per service.
- 03
Define SLIs and SLOs
Agree on indicators that reflect user experience and set objectives with an error-budget policy.
- 04
Actionable alerts with ownership
Replace noisy threshold alerts with SLO-based alerts; give every alert an owner, severity and runbook link.
- 05
Correlate with deployments
Annotate dashboards with deployment events where feasible so changes can be linked to behavior.
- 06
Runbooks and incident process
Write runbooks for common failure modes and define roles, communication and escalation.
- 07
Post-incident reviews
Introduce blameless reviews with tracked follow-up actions.
03 · Architecture
Solution architecture
Signals from applications and infrastructure flow into one observability stack. Dashboards and SLO-based alerts drive incident response, and review findings feed back into better instrumentation and runbooks.
Observability and incident response
- Source
- Build
- Observe
- Gate
- Security
- Deploy
- 01Applications & infrastructureServices, nodes, databases, load balancers
- 02Metrics & logsPrometheus exporters, structured logs
- 03Observability stackPrometheus, Grafana, ELK
- 04Alerts & dashboardsSLO-based, owned, actionable
- 05Incident responseRunbooks, roles, communication
- 06Continuous improvementPost-incident reviews and follow-ups
Review findings feed back into instrumentation, alerts and runbooks.
Technology stack
- Prometheus
- Alertmanager
- Grafana
- Elasticsearch / OpenSearch
- Logstash or Fluent Bit
- Kibana
- PagerDuty / Opsgenie
- OpenTelemetry (where adopted)
04 · Validation
How the result is validated
- Run game days that inject known faults and check whether alerts fire and runbooks lead to resolution.
- Review alert volume and the proportion that required action over several weeks; prune or tune the rest.
- Confirm log retention and access controls meet the organization's compliance requirements.
- Compare investigation workflows before and after using the team's own incident records.
05 · Expected impact
Business impact
- Shorter investigation workflows, because logs and metrics for every service are searchable in one place.
- Alerts the team trusts, each with an owner and a clear first action.
- Shared, measurable reliability targets that inform the balance between features and stability.
- Incidents that lead to tracked improvements instead of recurring.
These are expected directions of improvement, not measured results. Actual impact depends on the starting point and is tracked with the client's own metrics.
06 · Lessons learned
What tends to matter most
- Fewer, better alerts beat comprehensive but ignored ones.
- SLOs are a conversation tool with product teams, not just a monitoring setting.
- Runbooks need owners and review dates, or they go stale quickly.