Your system will
fail under load.
We prove it first.

Performance testing validates how a software system behaves under various load conditions — measuring response time, throughput, resource utilisation, error rates and stability. It encompasses load testing (validating performance at expected peak volumes), stress testing (finding breaking points beyond capacity), endurance testing (detecting memory leaks over time), spike testing (validating behaviour under sudden traffic surges) and scalability testing (confirming the system grows with demand). Performance testing is critical before major releases, during infrastructure changes and continuously in CI/CD pipelines. Without it, performance degradation reaches production where it directly impacts user experience, conversion rates and revenue.

Load testing validates system performance under expected peak concurrent user volumes — confirming that response times, error rates and resource utilisation remain within agreed SLA thresholds under normal and peak operating conditions. It answers: does our system perform acceptably at the load we anticipate? Stress testing deliberately exceeds system capacity — often to 2×, 5× or 10× the expected load — to find breaking points, measure how gracefully the system degrades under extreme pressure, and validate recovery behaviour after failure. Both are essential: load testing gives production confidence, stress testing reveals the safety margin and informs capacity planning decisions.

K-SPARC (Survey, Prepare, Appraise, Rationalise, Combine) is KiwiQA's 5-phase proprietary performance engineering framework. Survey defines business objectives and SLA requirements. Prepare builds the test environment, load model and concurrent user scenarios based on actual traffic analytics. Appraise executes load, stress, spike and endurance tests with full infrastructure monitoring. Rationalise analyses bottlenecks, DB query performance, memory leaks and infrastructure constraints. Combine delivers a consolidated report with risk-rated findings, tuning recommendations and capacity plan. Unlike ad-hoc load testing, K-SPARC produces documented, repeatable outcomes aligned to business SLAs — not just raw JMeter results.

KiwiQA uses JMeter, Gatling, Locust, k6, BlazeMeter and Azure Load Testing — selected based on the technology stack, scale requirements and client infrastructure. For API performance testing we use Postman and custom Python scripts. JMeter suits complex enterprise scenarios; Gatling and k6 excel for modern microservices and developer-led performance testing; BlazeMeter and Azure Load Testing provide cloud-based load generation capable of simulating millions of virtual users globally. All tools integrate with monitoring stacks including Prometheus, Grafana, Dynatrace, New Relic and Datadog for real-time bottleneck identification during test execution.

KiwiQA can simulate millions of concurrent virtual users through cloud-based load generation using BlazeMeter and Azure Load Testing. For most enterprise applications we model realistic concurrent user loads based on your actual traffic analytics — typically simulating 1,000 to 50,000 concurrent users representing genuine peak conditions rather than arbitrary large numbers. For global platforms like those serving DP World's 70+ country supply chain operations, we design geographically distributed load scenarios that reflect real user locations. The right number of virtual users is determined by your business SLAs and peak traffic data, not guesswork — which is why K-SPARC starts with the Survey phase.

Performance testing should be run at four critical points in the software lifecycle. During architecture design, to validate that infrastructure sizing matches anticipated load before expensive commitments are made. Before major releases, to confirm SLA compliance and catch new performance regressions introduced by feature development. After significant code changes affecting database queries, caching layers or API integrations, which are the most common sources of production performance incidents. And continuously in CI/CD pipelines as automated performance regression tests on every build — catching degradation at the commit level when it's cheapest to fix, before it accumulates into a production crisis.