Scaling SRE Systems with GCP + Kubernetes: Lessons from Running at 10x Traffic

How we redesigned our reliability engineering stack on Google Kubernetes Engine — and the SLO framework that changed how our team thinks about uptime. Context Eighteen months ago, our SRE team was managing reliability the old-fashioned way: dashboards nobody looked at, on-call rotations driven by alert fatigue, and postmortems that produced action items nobody followed up on. Then we 10x'd our traffic during a product launch. Everything broke, not catastrophically, but in that slow, grinding way that's actually worse. Cascading latency. Partial outages. Customer-facing errors that took us 40 minutes to even notice. We rebuilt everything. This is what we built, and more importantly, why. The Stack Layer Technology Purpose Orchestration GKE Autopilot Container management, autoscaling Service Mesh Istio on GKE Traffic management, mTLS, observability Metrics Cloud Monitoring + Prometheus SLI/SLO tracking Tracing Cloud Trace + OpenTelemetry Distributed request tracing Logging Cloud Logging