Load Testing Setup

We analyze your production traffic patterns — request distribution across endpoints, authentication flows, read/write ratios, payload sizes, and user session behavior — to build load test scenarios that reflect reality. Hammering a single endpoint with identical requests tells you nothing about production performance. Realistic tests use varied data, concurrent user flows, and think times.

Before you can detect regressions, you need baselines. We run calibration tests at multiple load levels — normal traffic, 2x peak, 5x peak, breaking point — and record p50, p95, p99 latency, throughput, error rates, and resource utilization at each level. These baselines become the benchmark for every future test run.

Load tests run automatically on every release candidate. We configure k6, Locust, or Gatling to execute in your CI environment against a staging cluster that mirrors production sizing. If p99 latency regresses by more than 15% from baseline, the pipeline fails. Performance is no longer a manual check — it is an automated gate.

Load test results feed directly into capacity planning. We generate reports showing current headroom at each infrastructure tier — how many more users your API servers, database, and cache layer can handle before hitting limits. When projected growth intersects capacity limits, you get an alert weeks before it becomes an incident.