Performance and load testing that finds the bottleneck before your users do
A page that is correct but slow still loses people. We model realistic demand, push the system until it shows where it bends, and report the cause in terms your engineers can act on.
Overview
What it is. Performance and load testing measures how a system behaves as demand grows: response time, error rate, throughput and resource use under expected load, peak load, sudden spikes and long-running use.
Who it is for. Teams preparing a launch, a marketing event or a seasonal peak; products moving to a new architecture or cloud setup; and any service where slow responses carry a direct business cost.
Why it matters
The problem it addresses. Capacity problems rarely show up in functional testing. They appear when real volumes, real data shapes and real third-party latency meet code that was only ever run with a handful of users.
When it matters most. Before a high-traffic event, after significant architectural change, when response times drift upward release by release, or when support tickets mention slowness no one can reproduce.
What happens without it. The first people to find the limit are your customers. Fixing a scaling problem during an incident costs far more than finding it in a planned test.
What we test
Load, stress, spike, soak and breakpoint tests chosen to match the release risk
Workload modelling from real usage patterns
journeys, think times, data volumes and mixes
Server-side measurement
latency, throughput, error rate and saturation of CPU, memory, connections and queues
Client-side measurement for web pages
Third-party dependencies
how slow or failing partners affect your response times
Bottleneck analysis and a prioritised list of findings with likely causes
Repeatable tests that can be re-run after each fix or major release
How UpDoer approaches Performance & Load
Understand
Requirements, tickets, code diffs and product context.
We learn the business calendar, traffic history, architecture and past incidents, and ask where slowness has already cost something.
Risk Mapping
Where a failure would cost the most.
We rank journeys by revenue and exposure (checkout, login, search, API calls from partners) and note shared resources that can fail together.
Test Strategy
Coverage chosen, test cases drafted.
We pick test types and success thresholds up front, such as target response times at the 95th percentile and acceptable error rates, and agree the test environment's limits honestly.
Execute
Manual and automated testing runs.
Scripts are built from agreed journeys and run with Apache JMeter, ramping load in stages while server and client metrics are collected.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI helps summarise run data, cluster slow transactions by shared cause and surface patterns across runs that a long chart review would hide.
Human Verification
A QA engineer checks every flagged result.
A performance engineer checks that the workload was realistic, separates test-harness artefacts from product limits, and confirms where the bottleneck really is.
Report & Improve
The verified quality signal, then better coverage.
We report findings by journey and cause, state what the environment could not prove, and keep the scripts so the test can be repeated after fixes.
Performance: finding where demand breaks the system
01
Where it breaks
Slow checkout at peakLeaks over long runsThird-party delays02
Test workflow
BaselineLoadSpikeSoak03
AI assists
Summarise run dataCluster slow callsDraft scenarios04
Humans decide
Check the workloadLocate the bottleneck05
Release signal
Findings by journeyLimits not provenFix order set by people
Illustrative workflow. Not a client result or a measured outcome.
Performance test selection
| Question the business asks | Test type | What is measured | Who interprets it |
|---|---|---|---|
| Does it cope on a normal busy day? | Average-load test | Response time and error rate at expected concurrency | Performance engineer, with the product owner |
| What happens at our biggest peak? | Stress test | Degradation curve, first resource to saturate | Performance engineer and the team that owns the bottleneck |
| Can it survive a sudden surge? | Spike test | Recovery time, dropped requests, queue behaviour | Engineer, with operations |
| Does it stay healthy for hours? | Soak test | Memory, connection and disk trends over time | Engineer; AI helps spot slow trends |
| Where is the ceiling? | Breakpoint test | Capacity limit and the failure mode beyond it | Engineer and architect |
| Is the page quick for real users? | Client-side measurement | Loading, interaction delay and layout stability on key pages | Front-end engineer and QA |
| Is a fresh build still fast? | Pipeline smoke check | Key endpoints against a stored baseline | QA reviews exceptions |
Question the business asks: Does it cope on a normal busy day?
- TEST TYPE
- Average-load test
- WHAT IS MEASURED
- Response time and error rate at expected concurrency
- WHO INTERPRETS IT
- Performance engineer, with the product owner
Question the business asks: What happens at our biggest peak?
- TEST TYPE
- Stress test
- WHAT IS MEASURED
- Degradation curve, first resource to saturate
- WHO INTERPRETS IT
- Performance engineer and the team that owns the bottleneck
Question the business asks: Can it survive a sudden surge?
- TEST TYPE
- Spike test
- WHAT IS MEASURED
- Recovery time, dropped requests, queue behaviour
- WHO INTERPRETS IT
- Engineer, with operations
Question the business asks: Does it stay healthy for hours?
- TEST TYPE
- Soak test
- WHAT IS MEASURED
- Memory, connection and disk trends over time
- WHO INTERPRETS IT
- Engineer; AI helps spot slow trends
Question the business asks: Where is the ceiling?
- TEST TYPE
- Breakpoint test
- WHAT IS MEASURED
- Capacity limit and the failure mode beyond it
- WHO INTERPRETS IT
- Engineer and architect
Question the business asks: Is the page quick for real users?
- TEST TYPE
- Client-side measurement
- WHAT IS MEASURED
- Loading, interaction delay and layout stability on key pages
- WHO INTERPRETS IT
- Front-end engineer and QA
Question the business asks: Is a fresh build still fast?
- TEST TYPE
- Pipeline smoke check
- WHAT IS MEASURED
- Key endpoints against a stored baseline
- WHO INTERPRETS IT
- QA reviews exceptions
Where AI helps
- Summarising large result sets into readable run comparisons so regressions between builds stand out.
- Clustering slow transactions by shared characteristics, such as payload size, endpoint or time window, to point at likely causes.
- Drafting first versions of test scenarios from traffic and journey descriptions for an engineer to correct.
- Producing a short plain-language release-risk note from the numbers, for a person to review.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Choosing scenarios, think times and data volumes that resemble real behaviour rather than convenient scripts.
- Deciding what counts as acceptable for this product: a 300 ms delay may be fine on a report page and fatal on checkout.
- Diagnosing where a bottleneck lives (application code, database, network, third party) and when the test itself is at fault.
- Judging whether a pre-production environment is representative enough to trust the result.
- Recommending the fix order, and stating clearly what has not been proven.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Related services
Relevant industries
Frequently asked questions
Do you test in production?
Our default is a production-like environment, because load testing a live system carries risk. If production testing is considered, it is a deliberate decision with your operations team, including limits and a stop condition.
Which tool do you use?
Apache JMeter is part of our published tool set. If your team already uses another load tool, we discuss using it rather than adding a second one.
Does AI run the load test?
No. AI helps analyse results and draft scenarios. Engineers design the workload, run the tests and interpret the findings.
How do you know the test is realistic?
We start from your real traffic patterns and journeys, state our assumptions in writing, and flag where the environment differs from production, because that limits how far a result can be trusted.