Performance tests also inform capacity planning: how much load your system can handle today and what changes are needed for tomorrow’s demand. Translating test results into capacity estimates helps guide scaling strategies and investment decisions.
Estimating Capacity from Test Data
By gradually increasing load in tests and observing where metrics break SLAs (for example P95 latency or error rate), you can estimate current capacity in terms of concurrent users or requests per second. You can then apply growth forecasts to decide when additional capacity or optimisations will be needed.
Example capacity insight:
- Current: meets SLAs up to 800 RPS, then error rate > 2%
- Forecast: traffic expected to grow 50% for upcoming campaign
- Required: either raise capacity to >= 1200 RPS via scaling/optimisation or adjust expectations and featuresScaling Strategies: Vertical, Horizontal and Architectural
Scaling options include vertical scaling (bigger machines), horizontal scaling (more instances with load balancing) and architectural changes (caching, queuing, microservices). Test data helps you choose which combination offers the best trade-off between cost, complexity and resilience.
Capacity planning closes the loop between performance testing and long-term system evolution.
Common Mistakes
Mistake 1 — Assuming linear scaling
This can mislead forecasts.
❌ Wrong: Expecting that doubling servers will always double throughput.
✅ Correct: Use tests to see how the system behaves at higher loads; bottlenecks may shift.
Mistake 2 — Ignoring business growth plans
This risks surprises.
❌ Wrong: Planning capacity only for today’s traffic.
✅ Correct: Incorporate marketing, product and seasonal forecasts into capacity scenarios.