Quality Engineering Domain
Executive Overview
Prevent high-profile downtime during peak traffic events. Qaston simulates tens of thousands of concurrent virtual users to identify memory leaks, database deadlocks, network latency bottlenecks, and CPU thresholds under extreme load.
Most performance problems are not caused by one slow line of code. They appear when many users, background jobs, integrations and database queries compete for the same resources. Our performance testing services reproduce that competition in a controlled environment, so slow pages, saturated connection pools and memory growth show up in a test report instead of a production incident.
Qaston is a software testing company based in Madurai, India, working remotely with product teams in India and abroad. We design workload models from real usage patterns, build repeatable scripts in open-source tools such as Apache JMeter, k6 and Gatling, and watch the system under test through monitoring platforms like Grafana, Prometheus, Dynatrace or New Relic when your team already uses them.
This page is for engineering and QA leads who need more than a single stress run. If you are planning a launch, a migration, a seasonal peak or a move to the cloud, we can help you decide what to measure, what a good result looks like and what to tune first. Functional correctness is covered separately under functional testing.
Key Strategic Benefits
Core Deliverables & Work Products
Tools & Technology Matrix
Our certified engineers leverage industry-leading toolsets, open-source automation libraries, and proprietary accelerators:
Types of load and performance tests we run
Load and website load testing
Load testing applies the traffic you expect on a normal and a busy day and records response times, throughput and error rates. It is the right starting point for a public website, a customer portal or an internal enterprise system that has never been measured under realistic concurrency.
Stress, spike and endurance testing
Stress tests push beyond expected limits to find the breaking point and see how the system fails and recovers. Spike tests model sudden surges such as a campaign email or a ticket release. Endurance (soak) tests run for hours to expose memory leaks and resource exhaustion that short tests miss.
Scalability and capacity testing
Scalability testing increases load in steps while resources are added, showing whether the architecture scales linearly or hits a shared bottleneck such as a database or a licensed component. The results feed capacity planning and help you size cloud infrastructure on evidence rather than guesswork.
API and microservice performance testing
Many user-facing delays originate in a single downstream service. We load test REST and other API endpoints directly, isolating latency per service, and pair this with our API testing services when contract and functional behavior also need checking.
How a performance testing engagement works
Goals, workload model and acceptance criteria
We start by identifying critical user journeys, expected concurrency, data volumes and service-level targets. Where production analytics or logs exist, we derive the workload model from them. The outcome is a short performance test strategy with agreed pass and fail thresholds for each journey.
Scripting, test data and environment readiness
We build parameterized scripts with realistic think times, correlation and test data, and check that the test environment is close enough to production to give meaningful results. Gaps, such as smaller databases or disabled caches, are documented so results can be interpreted honestly.
Execution, monitoring and bottleneck analysis
During each run we correlate client-side metrics with server, database and network metrics. When a threshold is missed, we trace the symptom toward a likely cause, such as a slow query, thread pool limit or chatty service call, and suggest where your developers should look first.
Reporting and retesting after tuning
You receive a report for engineering and a short summary for decision makers. After fixes or configuration changes, we repeat the same scenarios so the before and after comparison is like for like.
Tools and monitoring we work with
JMeter, k6 and Gatling for load generation
As a JMeter testing services provider we cover protocol-level HTTP, database and messaging tests. We use k6 and Gatling when a team prefers scripts written as code and stored in the same repository as the application, which makes review and reuse easier.
Grafana, Prometheus, Dynatrace and New Relic
Load numbers alone rarely explain a slowdown. We read resource utilization, garbage collection, query timings and traces from your monitoring stack, or help set up dashboards in Grafana and Prometheus when none exist, so every test run leaves behind observable evidence.
Performance checks inside CI/CD
Full-scale tests are scheduled, but lightweight smoke-level performance checks can run in your pipeline on every build. These catch regressions in response time early, while heavier scenarios run before major releases or infrastructure changes.
Choosing performance testing and how it differs from other services
Performance versus functional and regression testing
Functional and regression testing confirm that features work for one user. Performance testing confirms they keep working when many users arrive together. A feature can pass every functional test and still time out under concurrency, so the two are best planned together.
Enterprise performance testing for packaged platforms
Large business platforms bring their own constraints, from batch windows to shared integration layers. We plan enterprise performance testing around those constraints, agreeing realistic test windows and non-production environments with your platform and infrastructure owners.
Performance testing consulting without a full test project
Sometimes a team only needs advice: which scenarios to automate, how to read a result, or whether an architecture is likely to scale. We offer performance testing consulting on a review basis, and teams can later move to hands-on execution if it makes sense.
Qaston 4-Phase Delivery Framework
Discovery & Architecture Audit
Evaluating application dependencies, defect history, test environments, and SLAs.
Framework Engineering & Design
Designing modular test suites, synthetic data pipelines, and CI/CD quality gates.
Continuous Automated Execution
Parallel execution across real devices, distributed cloud runners, and regression suites.
Analytics & Optimization
Real-time quality dashboards, defect root-cause analysis, and continuous feedback.
Plan your next performance test with Qaston
Tell us about your application, your expected traffic and your release date. We will suggest a practical test plan and the tools that fit your stack.