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CASE STUDY · OBSERVABILITY

Full-stack Kubernetes observability with New Relic and Datadog

Unified metrics, traces, and logs across production clusters gave on-call engineers the context to detect and resolve incidents in minutes instead of hours.

Industry
Retail & e-commerce
Scale
8 Kubernetes clusters · 300+ workloads · 24/7 operations
Duration
12 weeks
Service
Observability & Monitoring
79%
faster mean time to resolve
5 min
mean time to detect, down from 45
70%
less alert noise
100%
of production services instrumented
THE CHALLENGE

Where things stood.

Incidents were usually reported by customers first. Engineers pieced together what happened from separate tools, and most alerts were threshold noise that on-call teams had learned to ignore.

Coverage was uneven: some services had APM and tracing, while others had only basic host metrics.

OUR APPROACH

How the engagement ran.

Delivered in phases with a weekly status review and a steering check-in at each milestone.

Weeks 1–2

Assess coverage

Audited existing telemetry, alert rules, and incident history, and defined service-level objectives for critical customer journeys.

Weeks 3–6

Instrument

Rolled out New Relic APM and Datadog cluster, container, and log collection across every production cluster.

Weeks 7–10

Signals, not noise

Rebuilt alerting around SLOs and burn rates, created service dashboards, and linked alerts to runbooks.

Weeks 11–12

Operationalize

Trained on-call engineers, set up incident review workflows, and documented ownership for every dashboard and alert.

Technology stack.

Kubernetes
New Relic
Datadog
AWS
RESULTS

Before and after.

Measured against the baseline agreed at the start of the engagement.

MeasureBeforeAfter
Mean time to detect (MTTD)~45 minutes~5 minutes
Mean time to resolve (MTTR)~4 hours~50 minutes
Weekly alert volumeBaseline70% lower
Services with APM & tracing~40%100%
Incidents first reported by customersMostRare
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