// performance testing services
Performance Testing Services
QACraft's performance testing services find your breaking point before launch — load, stress, scalability and endurance testing with k6, JMeter and full observability, so peak traffic is a plan, not a gamble.
what it is
What Is Performance Testing?
Performance testing measures how your application behaves under load — how fast it responds, how stable it stays, and how far it scales when real traffic arrives. It is the difference between discovering your limits in a controlled run and discovering them live, on your busiest day, in front of customers.
QACraft's performance testing services cover the full spectrum — load, stress, scalability and endurance — using k6, JMeter and Gatling for distributed load generation and a full observability stack to explain not just what slowed down but why.
This is the hub for our performance work. Four focused disciplines sit underneath it, each answering a different question — and each with a dedicated deep-dive page linked below. Most engagements combine several of them around a launch, a migration, or a peak event.
our services
What We Offer
Performance is several different questions, so we split it into focused disciplines. The four core types differ by intent — expected load, beyond-capacity, scaling, and sustained duration — and each has its own deep-dive page.
How does it perform under expected, real-world traffic? Validate response times and throughput at your target load. Explore load testing →
What happens beyond capacity? Push past the limit to find the breaking point and confirm it fails — and recovers — gracefully. Explore stress testing →
Does it scale as you add users or resources? Measure how performance improves (or doesn’t) as you grow. Explore scalability testing →
Does it hold up over sustained duration? Run for hours to expose memory leaks and slow degradation. Explore endurance testing →
Beyond measuring: we find the bottleneck — DB, CPU, queue, GC, N+1 — and recommend the fix, then prove it with a re-run.
Smoke tests on every PR, fuller runs on merge, soak nightly — so performance regressions are caught continuously, not in production.
Flexible engagement models
A focused engagement to prepare for a known peak — Black Friday, a launch, a campaign — with a load model, runs and a go-live capacity verdict.
Ongoing performance testing and engineering embedded in your team, with CI gates and trend tracking release over release.
Performance engineers who plug into your stack, tooling and observability under your leadership — scaled up or down monthly.
tools & frameworks
Tools & Frameworks We Use
Tool choice is decided in Phase 1, against your stack, scale and observability — never by default. Performance work spans load generation and the APM that explains it:
Scriptable, developer-friendly load testing — our default for modern, CI-friendly runs.
The mature, protocol-rich standard for complex enterprise load scenarios.
High-performance, code-based load testing with expressive scenarios.
Python-based, scalable load generation for custom scenarios.
Cloud-scale distributed load generation across regions.
Enterprise performance testing for complex, regulated environments.
Live dashboards correlating load results with server-side metrics.
APM tracing so a latency spike is tied to its real root cause.
why automate
Why Performance Testing Matters
Slow is the new down. Users abandon a sluggish page, and an outage on a peak day is lost revenue and lost trust at the worst possible moment. Performance testing turns "we think it’ll hold" into evidence. Here is what it changes:
Discover your real capacity ceiling in a controlled run — not live, in front of customers, on your busiest day.
Black Friday, a launch or a campaign becomes a tested plan with a known capacity, not a hopeful gamble.
We locate the bottlenecks dragging your p95/p99 down and prove the fix with a re-run — speed users actually feel.
Know exactly how your system scales, so you provision for real demand instead of over-buying or under-providing.
Endurance runs surface the memory leaks and slow rot that only appear after hours of sustained load.
Real numbers — percentiles, throughput, ceilings — to plan infrastructure and SLOs on evidence, not estimates.
our process
Our Performance Testing Process
Every engagement follows the same disciplined path — and produces a concrete artifact at the end of each phase, so you always know exactly what you are getting.
Model the Load
We build a realistic load model from your production telemetry — real traffic shapes, peaks and user journeys — not round-number guesses, and agree the targets that matter.
→ artifact: data-driven load model + SLOsScript & Baseline
We script the scenarios in k6/JMeter/Gatling, wire in your observability stack, and capture a baseline so every later run has something honest to compare against.
→ artifact: test scripts + performance baselineRamp, Stress & Observe
We run load, stress, scalability and endurance scenarios from distributed/cloud generators, correlating client-side results with server-side APM in real time.
→ artifact: run results correlated with APMAnalyze, Tune & Gate
We read results by p50/p95/p99, find the bottleneck, recommend fixes, and wire performance gates into CI/CD so regressions are caught on every change.
→ artifact: bottleneck report + CI performance gatesSee a load ramp to breaking point
A sample k6 ramp — virtual users climbing while p95 latency and throughput plot live, until the system saturates and a breaking-point marker drops.
beyond a number
Observability-Driven Performance Engineering
A load test that only reports "p95 was 900ms" tells you something is wrong but not what. Modern performance testing is about observability, not a single number — and that is where we differ from a basic load run.
We correlate client-side load results with server-side telemetry in Grafana, Prometheus and Datadog, read results by p50/p95/p99 rather than misleading averages, build baselines from real production telemetry instead of guesses, generate load from distributed/cloud regions, and wire performance gates into CI/CD. The result is not a number — it is a traced root cause and a fix.
That is performance engineering: measuring, explaining and improving — so every run ends with a reason and an action, not just a verdict.
industries
Industries We Serve
We provide performance testing for teams whose worst day is a traffic spike — where milliseconds and uptime convert directly into revenue and trust.
why us
Why Choose QACraft for Performance Testing
Teams choose QACraft when they want performance engineers who own outcomes — not a body shop billing hours.
We tie load results to server-side APM (Grafana, Prometheus, Datadog), so you get a root cause — not just a slow number.
Load models built from your real telemetry and traffic shapes, not round-number guesses that test the wrong thing.
We report p50/p95/p99 and error rate — the slow tail your users actually feel — not flattering averages.
Smoke on PR, full on merge, soak nightly — regressions caught continuously, not discovered at peak.
All test scripts, dashboards and CI config live in your repository from day one. No black boxes, no lock-in.
Performance connects to your functional, automation and broader QA under one team — joined-up, not stitched together.
straight answers
Frequently Asked Questions
What is performance testing?
Performance testing measures how your application behaves under load — its speed, stability and scalability when many users hit it at once. It answers the questions that matter before a launch or peak event: how fast is it under real traffic, how much can it take, and where does it break?
What's the difference between load, stress, scalability and endurance testing?
Load tests expected, real-world traffic. Stress pushes beyond capacity to find the breaking point. Scalability checks how well it improves as you add users or resources. Endurance (soak) holds sustained load over hours to expose memory leaks and slow degradation. Each is a dedicated discipline — see load, stress, scalability and endurance testing.
Which performance testing tools do you use?
k6, JMeter, Gatling and Locust for load generation; BlazeMeter and NeoLoad for distributed/enterprise runs; and Grafana, Prometheus and Datadog for observability. The right combination is chosen against your stack and scale in Phase 1.
What metrics do you report, and how do you read results?
We read results by percentiles — p50, p95 and p99 — not averages, alongside throughput (requests/sec) and error rate. Averages hide the slow tail your real users feel; percentiles and a correlated server-side view tell you what actually happened and why.
Do you integrate with our APM and observability stack?
Yes — it is central to how we work. We correlate client-side load results with server-side telemetry in Grafana, Prometheus and Datadog, so a latency spike is traced to its real cause (CPU, DB, queue, GC) instead of just reported as a number.
Can performance tests run in CI/CD?
Yes. We set up performance gating: a fast smoke test on every pull request, a fuller load run on merge, and a soak/endurance run nightly — so performance regressions are caught continuously, not discovered in production.
Ready to find your breaking point — safely?
Build your plan in 60 seconds — or bring your next peak event to a 30-minute call and leave with a load model and a single number.