Traffic, data volume, and job load projected from real growth plus known events (launches, seasons). Plan for the peak that matters — the Black Friday, the enrollment day, the month-end close.
Systems fail in sequence: usually database first, then queues, then compute. Load-test to learn your order; the fix differs per layer and so does its lead time.
Set target utilization (e.g., peak at 60% of proven capacity) so growth is absorbed calmly; autoscaling handles spikes, planning handles trends.
Quarterly: actuals vs model, bottleneck retest after major changes, cost curve sanity. Capacity planning is a rhythm, not a document.
Skipping the discipline this article describes until an incident, audit, or stalled project forces it — every practice above is cheaper adopted early than retrofitted under pressure.
Let's discuss how we can help you with capacity planning.