Capacity Planning
Definition
Capacity planning estimates which computing, storage, or system resources are needed for expected loads.
It connects demand forecasts with performance data, reserves, scaling limits, and costs. Autoscaling reacts to load; capacity planning sets the framework for it.
Simply explained
How to calculate in advance how many workstations and reserves are needed for normal and peak loads.
Why it matters in practice
Relevant for websites, APIs, data pipelines, and AI systems with fluctuating usage.
Example from business
A company measures tokens per second, concurrent requests, and memory requirements and plans GPU or API capacity for peak times accordingly.
Limits and pitfalls
- Only planning average load; forgetting dependencies and quotas; seeing autoscaling as a substitute for capacity limits; not including costs.
Origin & history
Google SRE describes traditional and target-oriented capacity planning. It considers demand, resource needs, performance goals, budget, and dependencies.
Use in mid-sized companies
- Possible applications: web hosting, databases, queue workers, LLM inference, and batch processing.
Frequently asked questions
Is capacity planning the same as autoscaling?
No. What should be considered? Load profile, SLOs, reserves, limits, and costs.
Sources
- Performance Optimization · Google Cloud Architecture Framework, Performance Optimization
- Performance Efficiency · AWS Well-Architected, Performance Efficiency
- Software Engineering in SRE, Capacity Planning · Google SRE, Software Engineering in SRE, Capacity Planning
Sources checked: 10./11.10.2026.
Editorially reviewed on: