// load testing services
Load Testing Services
QACraft's load testing services verify your app holds up under expected peak traffic — measuring response times, throughput and SLOs with k6, JMeter and Gatling, so your busiest day is a tested plan, not a gamble.
what it is
What Is Load Testing?
Load testing measures how your application performs under the traffic you actually expect — normal and peak concurrency — to answer one practical question: will it handle your busiest day? It puts realistic, simulated user load on your system and checks that response times, throughput and error rates stay within the targets your users and SLAs demand.
QACraft's load testing services model your real peak from production telemetry, drive it with k6, JMeter or Gatling, and validate the result against your SLOs — with full observability so a slow response is explained, not just reported.
This service is about expected load — staying within capacity. To deliberately push beyond the limit and find the breaking point, see its sibling stress testing; to measure how performance changes as you scale resources, see scalability testing. All sit under our performance testing pillar.
our services
Our Load Testing Services
We validate your app at the load it will actually face — modelled from real data and measured against your SLOs. Most engagements combine several of the services below.
Simulate your expected busiest-day concurrency and confirm response times and throughput hold within target.
Test directly against your service-level objectives — p95 thresholds, throughput and error budgets — for a clear pass/fail.
Build the load profile from your production telemetry — real traffic shapes and journeys — not round-number guesses.
Measure how many simultaneous users you can serve and how request throughput behaves as concurrency rises.
Generate realistic load from multiple regions at scale, so the test reflects how real distributed traffic arrives.
A fast load smoke test on every change, catching performance regressions before they reach a release.
Flexible engagement models
A focused engagement to prove a known peak — a launch, campaign or seasonal spike — with a load model, peak run and a go-live verdict.
Ongoing load testing embedded in your team, with CI gates and trend tracking release over release.
Performance engineers who plug into your stack and observability under your leadership — scaled monthly.
tools & frameworks
Tools & Frameworks We Use
Tool choice is decided in Phase 1, against your stack, scale and observability — never by default. Load work spans generation and the APM that explains it:
Scriptable, CI-friendly load testing — our default for modern peak 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 workloads.
Cloud-scale distributed load generation across regions.
Live dashboards correlating load results with server-side metrics.
APM tracing so a latency spike is tied to its real cause.
Load smoke tests wired into Jenkins, GitHub Actions or GitLab CI.
why automate
Why Load Testing Matters
Traffic is never evenly polite. It arrives in spikes — a campaign, a feature in the press, the first hour of a sale — and that is exactly when a slow or failing app costs the most. Load testing proves you are ready before the spike, not during it. Here is what it changes:
Your busiest realistic day is a tested, evidenced plan — not a hope that the servers hold.
A clear pass/fail against your p95, throughput and error targets — you know exactly where you stand.
Real numbers let you provision for actual demand instead of over-buying or under-providing.
We find the bottlenecks dragging p95/p99 down at peak and prove the fix with a re-run.
Degradation under load is surfaced in a controlled run, not in abandoned carts and support tickets.
Percentiles, throughput and headroom figures to plan infrastructure and SLOs on evidence.
our process
Our Load 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 Expected Peak
We build a realistic load model from your production telemetry — your actual busiest-hour traffic shape and user journeys — and agree the SLOs the run must meet.
→ artifact: peak load model + SLO targetsScript & Baseline
We script the scenarios in k6/JMeter/Gatling, wire in your observability, and capture a baseline so the peak run has something honest to compare against.
→ artifact: load scripts + baselineRamp to Peak & Hold
We ramp to your expected peak concurrency and hold it, watching response times and throughput against the SLO with server-side telemetry correlated in real time.
→ artifact: peak run results correlated with APMValidate SLOs & Report
We confirm whether the SLOs held, flag any slow tail or bottleneck, and recommend fixes or headroom — then optionally gate it in CI for every release.
→ artifact: SLO pass/fail report + recommendationsSee a peak-load ramp
A sample run ramping to your expected peak and holding there — p95 latency staying under the SLO threshold while throughput climbs and errors stay at zero.
beyond the run
Observability & CI Load Testing
A load run that only says "p95 was 380ms" is a number without a reason. We tie it to observability — correlating client-side results with server-side telemetry in Grafana, Prometheus and Datadog — so a slow response at peak is traced to its real cause, not just reported.
We read results by p50/p95/p99 rather than averages, build the load model from real production telemetry, and can gate load in CI/CD so a regression is caught on every change. For the full performance-engineering picture, see our performance testing pillar.
industries
Industries We Serve
We provide load testing for teams whose busiest day is also their highest-stakes day — where holding up under the spike is the whole point.
why us
Why Choose QACraft for Load Testing
Teams choose QACraft when they want performance engineers who own outcomes — not a body shop billing hours.
Load models built from your real telemetry and traffic shapes — testing your busiest day, not an arbitrary number.
Every run is judged against your service-level objectives, so you get a clear pass/fail, not a wall of metrics.
We tie load results to server-side APM, so a slow response comes with a root cause, not just a figure.
A load smoke test on every change keeps regressions out, between the big peak runs.
All test scripts, dashboards and CI config live in your repository from day one. No black boxes, no lock-in.
Load connects to stress, scalability and broader performance testing under one team — joined-up, not stitched together.
straight answers
Frequently Asked Questions
What is load testing?
Load testing measures how your application behaves under the traffic you actually expect — typical and peak concurrency. It answers a simple question: at your busiest realistic load, are response times, throughput and error rates still within the targets your users and SLAs require?
What's the difference between load and stress testing?
Load testing validates behaviour at expected, realistic peak — will it handle your busiest day within SLO? Stress testing deliberately goes beyond capacity to find the breaking point and confirm graceful failure. Load asks 'does it hold?'; stress asks 'where does it break?'.
How much load should we test for?
We base it on your real data, not round numbers: your historical peak from analytics, plus headroom for growth and spikes (campaigns, launches, seasonal peaks). The target is agreed in Phase 1 so the run reflects your busiest realistic day, not an arbitrary figure.
What metrics do you measure?
Response time by percentile (p50/p95/p99), throughput (requests/sec), error rate, and concurrency — checked against your SLOs. We read percentiles rather than averages because averages hide the slow tail your real users feel.
Which load testing tools do you use?
k6, JMeter, Gatling and Locust for load generation, BlazeMeter for cloud-scale distributed runs, and Grafana, Prometheus and Datadog for observability. The right combination is chosen against your stack and target scale in Phase 1.
Can load tests run in CI/CD?
Yes — we can wire a fast load smoke test into your pipeline so performance regressions are caught on every change, with fuller peak runs scheduled before releases and known high-traffic events.
Ready for your busiest day?
Build your plan in 60 seconds — or bring your next peak event to a 30-minute call and leave with a realistic load model and a single number.