If you’re an engineer who’s tired of scaling “by gut feel,” this article is an engineer-friendly playbook for cloud capacity planning—how to translate CPU, memory, QPS, latency, and scaling limits into real decisions (what to scale, when to scale, and how to avoid overprovisioning while still protecting performance).
Capacity planning isn’t just “add more nodes.” It’s a repeatable loop:
✅ Measure → baseline CPU/memory, QPS, p95/p99 latency, saturation signals
✅ Model → understand bottlenecks, set SLO-based headroom, identify constraints (DB, cache, network, limits)
✅ Scale → right autoscaling strategy (HPA/VPA/Cluster Autoscaler/Karpenter), safe thresholds, load tests
✅ Operate → dashboards + alerts + regular review so growth doesn’t become incidents
#CapacityPlanning #Cloud #PerformanceEngineering #SRE #DevOps #PlatformEngineering #Kubernetes #Autoscaling #Observability #SLO
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