Case studies
B2B Software · Kubernetes

Observability and SLOs that cut detection time from 90 minutes to 5 and freed a fifth of engineering time.

A six-person team ran production with no visibility: incidents took hours to detect and 40% of engineering time went to firefighting. I built the observability stack, defined SLOs and burn-rate alerting, and wrote the runbooks. Detection fell to 5 minutes and the team got 20% of its time back.

Client
Bootstrapped B2B software company, engineering team of 6
Sector
B2B Software
Duration
6 weeks
Engagement
Fixed-Scope Build
90 → 5 min
mean time to detect
99.8%
uptime SLO sustained
20%
engineering time recovered

01

The challenge

Production behaviour was invisible. Incidents surfaced through customers, took two to three hours to diagnose, and the few alerts that existed were noisy enough to be ignored. No SLOs were defined, so nobody could say whether the service was healthy. Forty percent of a six-person team's time went to firefighting.

02

Solution architecture

Solution architecture: observability and SLOs on KubernetesProduction traffic reaches services running on Kubernetes that are instrumented with OpenTelemetry and write structured JSON logs with request correlation. Prometheus collects metrics with retention sized to the SLO windows, and a log pipeline collects logs into the same cluster. Recording rules define each service level indicator against its SLO, and multi-window burn-rate alerts are routed through Alertmanager to the on-call rotation, with a runbook for every alert. Grafana shows service health to engineering and SLO attainment to leadership.KUBERNETESPEOPLECUSTOMERSProduction trafficExperience measured as service level indicatorsINSTRUMENTATIONOpenTelemetry in servicesStructured JSON logging with request correlationCOLLECTIONPrometheusMetrics from every serviceRetention sized to the SLO windowsCOLLECTIONLog pipelineLogs collected into the same clusterCorrelated by requestSLOSRecording rules per SLISLO targets agreed with productand engineeringALERTINGAlertmanagerMulti-window burn-rate alertsA runbook written for every alertGRAFANADashboardsService health for engineeringSLO attainment for leadership, on the same numbersRESPONSEOn-call rotationPaged on SLO burn rateRunbook for every alertPost-incident reviewBUSINESSLeadershipSLO attainment, on thesame numbers as engineersTraffic and alertsTelemetry and rulesReporting

Request path in teal. Dashed lines are private connectivity, deployment and telemetry. Scroll sideways on small screens.

Instrumentation
OpenTelemetry in services. Structured JSON logging with request correlation.
Collection
Prometheus for metrics, a log pipeline into the same cluster, retention sized to SLO windows.
SLOs & alerting
Recording rules per SLI. Multi-window burn-rate alerts routed through Alertmanager.
Dashboards
Grafana: service health for engineering, SLO attainment for leadership.
Response
Runbook per alert, on-call rotation, post-incident review template.

Layers top to bottom. Controls apply at every layer.

03

Approach

  1. 01Define what healthy means first: SLIs tied to user experience, then SLOs the business could stand behind.
  2. 02Deploy one coherent stack for metrics, logs and traces rather than three disconnected tools.
  3. 03Alert on SLO burn rate, not on every symptom, so pages are rare and actionable.
  4. 04Write the runbook for every alert before turning it on.
  5. 05Give leadership the same dashboards as engineering, so reliability conversations use one set of numbers.

04

How the delivery ran

  • Week 1: incident history review, SLI candidates, SLO targets agreed with product and engineering.
  • Week 2–3: Prometheus, Grafana and structured logging deployed as code. Services instrumented.
  • Week 4–5: burn-rate alerting, runbooks and on-call rotation. Alert tuning against real traffic.
  • Week 6: dashboards for engineering and leadership, validation, hand-over. 30 days of support followed.

05

What was delivered

  • Observability stack as code with dashboards
  • SLO catalogue with recording and alert rules
  • Runbook per alert and an incident response procedure
  • Leadership reliability dashboard

06

Results

DimensionBeforeAfter
Mean time to detect~90 minutes5 minutes
Mean time to resolve~180 minutes25 minutes
AvailabilityUnmeasured99.8% SLO sustained
Engineering time on incidents~40%~20%, redirected to features
Alert qualityNoisy, ignoredBurn-rate pages, each with a runbook

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