Test scenario design
Journeys modelled from real traffic patterns and usage logs, with think times and data variation, so the load resembles users rather than a hammer.
Reliability & Performance
Load & Performance Testing
Prove where your system breaks, at a load level you chose in advance.
The work
What this actually does
Performance problems are cheapest to find before customers do. We design load tests that reproduce realistic traffic — login, browse, checkout, background jobs — and run them against a production-like environment until something saturates. The output is not a pass mark but a curve: request rate against latency, with the knee visible.
Tests are written in code and kept in the repository, so they run on demand and in the pipeline rather than as a one-off exercise before a launch. Where a test needs production data to be realistic, we work from anonymised or synthetic sets, and we are honest about the parts of the system a synthetic load cannot faithfully reproduce.
If any of these sound familiar
- We discover our limit during the busiest hour
- Performance testing happens once, then never again
- Nobody knows how many users we can actually serve
- A small change quietly doubles response time
Scope
What's included
Every engagement on this page covers the following, sized to your setup rather than delivered as a fixed package. If something here is not relevant to you, it comes off the scope and off the price.
Scripts as code
k6, JMeter or Locust scenarios live beside the application and run in CI, so performance regressions are caught on a pull request instead of in production.
Bottleneck analysis
We correlate load generator output with application, database and network metrics to find which tier saturates first and what limits it.
Spike and soak tests
Short bursts test how the system handles sudden traffic, while long soak runs expose memory leaks, connection exhaustion and slow degradation over hours.
Acceptance thresholds
Each scenario gets an explicit target for latency and error rate, so the test either passes or fails rather than producing a chart for someone to interpret.
Findings and fixes
A prioritised list of the changes that would raise the ceiling most, ranked by effort, so the report leads to work rather than a folder.
What changes
What teams typically see
1 curveRequest rate against latency
2x peakLoad tested before launch
hoursLongest soak run
Handover
What you keep
Everything produced during the engagement is yours: the repositories, the accounts, the documentation. There is no proprietary layer and nothing to unlicense if you take the work in-house.
- Versioned load test scripts for critical journeys
- Baseline performance report with latency curves
- Bottleneck analysis covering app, database and network
- Pass or fail thresholds wired into the pipeline
- Prioritised remediation list with effort estimates
Tooling
Tools we use here
A starting point, not a requirement. We work in whatever you already run wherever it does the job.
Grafana
Kubernetes
AWS
PostgreSQL
Redis
Apache Kafka
OpenTelemetry
How it runs
From first call to handover
The same four steps on every engagement. You see each one before it starts and can stop at any of them.
- 01
Agree what to prove
We start from a question you actually have — can we survive the campaign, will the migration hold — and design the test around answering it.
- 02
Build realistic scenarios
From access logs and product analytics we build journey mixes and data sets that reflect your real traffic, including the boring parts that cause problems.
- 03
Run and observe
Tests run against an isolated, production-like environment while we watch application, database and network metrics together, increasing load until the knee appears.
- 04
Report and re-run in CI
Findings are written up with evidence, then the useful scenarios are moved into the pipeline so the ceiling is checked on every release.
Questions
Asked before we start
Can you test against production?
Sometimes, carefully, and usually not. Most organisations get a better answer from a scaled, production-like environment with real data volumes. If production is the only realistic option, we run low-volume tests in a defined window with a rollback plan agreed first.
How many users should we test for?
The honest answer is that concurrent users is a weak unit, because one user on a heavy report is worth fifty on a landing page. We size tests around request rates and journey mixes, then translate the result back into a user number for the business.
Our test environment is much smaller than production. Is it still worth it?
Yes, if you scale the conclusion as well as the load. A smaller environment finds the shape of the curve and the first bottleneck; we mark clearly which findings will move at production scale and which might not.
Will you fix the bottlenecks you find?
Where they are configuration or infrastructure problems, yes, that is often part of the engagement. Where the fix is application redesign, we document the options and the trade-offs, and your engineers make the change.
Reliability & Performance
Often needed alongside this
Reliability & Performance
Autoscaling & Capacity Planning
Capacity that follows demand automatically, sized from real traffic data rather than guesswork.
- Horizontal, vertical and KEDA event-driven scaling
- Load-model-based capacity forecasting
- Headroom without paying for idle instances
Reliability & Performance
Chaos Engineering & Resilience Testing
Deliberately break things in a controlled way, so you find out now rather than at the worst moment.
- Game days and controlled failure injection
- Dependency and availability failure drills
- Resilience findings turned into backlog items
Reliability & Performance
Distributed Tracing
Follow a single request across every service and see exactly which hop added the 800ms.
- OpenTelemetry instrumentation and collectors
- Jaeger, Tempo and vendor backends
- Trace-to-log correlation for fast triage
Worth a conversation about Load & Performance Testing?
Bring the specific problem. We will tell you honestly whether this is the service that fixes it, and what it would take.