capacity-planning

Capacity planning calculates fleet sizing by combining demand forecasts with per-replica saturation metrics and explicit headroom rules, ensuring your service survives burst traffic and autoscaler lag without over-provisioning resources.

Autoscaling fails during bursts when capacity planning ignores autoscaler reaction time and demand shape. Proper planning separates sustained load from short-window spikes and applies headroom percentages to survive uncertainty and scaling lag.

Per-replica saturation requires load testing that matches real request mix, payloads, ramp profiles, concurrency, think time, authentication, and downstream dependencies to accurately measure when individual service replicas reach their throughput limits.

Pre-provisioning for known events involves forecasting peak demand, pre-scaling and pre-warming replicas ahead of time, verifying with load tests, and defining explicit abort criteria to ensure sufficient headroom during the traffic event.

Sizing for projected peak demand requires combining current traffic, growth rate, and peak-to-trough demand shape to calculate replica counts, ensuring your fleet handles forecasted maximums rather than today's average load.

Yes, capacity planning supports post-incident analysis by evaluating whether capacity exhaustion caused the outage, comparing actual load against fleet sizing math, and adjusting headroom rules to prevent recurrence.