Quality Engineering
Performance TestingKnow your breaking point before your users find it.
Every system has a breaking point; the only question is whether you find it before your users do. We run load, stress, spike, and endurance tests with JMeter and k6, then translate the graphs into specific bottlenecks and fixes.
What this covers
Load, stress, spike, and soak Testing with JMeter and k6
Realistic workload modeling from production traffic patterns
Bottleneck analysis, DB queries, API latency, infra limits
Performance baselines wired into CI so regressions are caught early
Capacity planning recommendations backed by data
Find the breaking point before your users do
Every system has a limit. The only real question is whether you discover it during a controlled test or during a launch, a campaign, or the first Monday of the month when everyone logs in at once.
Every system has a limit. The only real question is whether you discover it during a controlled test or during a launch, a campaign, or the first Monday of the month when everyone logs in at once. Performance Testing is how you convert that unknown into a number you can plan against, and, more usefully, into a named bottleneck with an owner and a fix.
The failure mode we see most often is not a missing test but an unrealistic one: a flat load curve against synthetic data, hitting one endpoint, from a single region, with caches warm. It produces a reassuring graph and tells you nothing. Useful load Testing models how your product is actually used, the mix of journeys, the ramp shape, the think time, the cache-cold cases, the long tail of heavy tenants.
The four questions we answer
We run these with JMeter and k6 against realistic workload models, then do the part that actually creates value: correlate the response-time curve with database query plans, API traces, thread pools, garbage collection and infrastructure metrics until each degradation has a named cause.
We run these with JMeter and k6 against realistic workload models, then do the part that actually creates value: correlate the response-time curve with database query plans, API traces, thread pools, garbage collection and infrastructure metrics until each degradation has a named cause. A report that says 'p95 rose at 800 users' is an observation. A report that says 'p95 rose at 800 users because this unindexed query on the orders table serialises behind a connection pool capped at 20' is a fix.
Load: does the system meet its latency and throughput targets at expected peak, sustained, with headroom to spare?
Stress: where does it break, how does it break, and does it fail gracefully or take the database with it?
Spike: what happens when traffic multiplies in sixty seconds, an email send, a match kick-off, a flash sale, and does autoscaling arrive in time to matter?
Soak: does it survive twelve or twenty-four hours without leaking memory, exhausting connection pools, or filling a disk with logs?
Performance as a gate, not an annual event
Performance regressions are introduced by ordinary commits, an added join, a chatty loop, a dependency upgrade.
Performance regressions are introduced by ordinary commits, an added join, a chatty loop, a dependency upgrade. Testing once before launch finds the state of the system on that day only. We wire a lightweight baseline into CI so a meaningful regression fails a pipeline while the change is still fresh in someone's head, and reserve full-scale runs for release candidates and capacity planning. That pairs naturally with Cloud Testing when autoscaling behaviour is part of the answer.
Engagement path
How the engagement runs
Every Performance Testing engagement runs the same way: understand the risk, build the thing that reduces it, prove it works, hand it over.
Targets and workload model
We agree explicit targets, p95 latency, throughput, error budget, then model realistic journey mixes and ramp shapes from production traffic patterns.
Rig, data and observability
Load scripts, representative data volumes, and the monitoring needed to see inside the system while it is under pressure rather than only from outside.
Execute and correlate
Load, stress, spike and soak runs, each correlated against query plans, traces and infrastructure metrics to isolate the actual constraint.
Fix, retest, baseline
Prioritised recommendations, a retest to prove the fix, and a CI baseline so the next regression is caught by a pipeline, not a customer.
What a load test says
The average is the number that lies to you
Median response time barely moves. It is still under a third of a second at a thousand concurrent users, and a dashboard showing only that number stays green all the way through the incident.
The tail is where your users are. At the same moment the median reads 300 ms, one request in a hundred is taking nearly three seconds — and the curve went vertical two hundred users earlier. Finding that knee, and naming the query or pool that causes it, is the job.
Illustrative: the characteristic shape of a load test, not client data.
Drag across the chart to read any point, or open the table.
Deliverables
What you get
Artefacts you keep and can run without us. Everything lives in your repositories and your tooling.
Handover pack
6 artefacts · yours to keep
Agreed, written performance targets where none existed before
Version-controlled JMeter or k6 scripts you can re-run without us
A realistic workload model derived from production traffic patterns
Bottleneck analysis naming the query, service or limit responsible
Prioritised remediation recommendations with expected impact
A CI performance baseline that fails builds on meaningful regression
Self-check
Signs your team needs this
If more than one of these is true, it is usually cheaper to fix now than after the next release.
The system slows under load and nobody can say which component is the cause
A marketing campaign, launch or seasonal peak is coming and capacity is a guess
Autoscaling is configured but has never been tested against a real spike
Response times degrade over days until a restart fixes it
There are no written latency or throughput targets to test against
A Senior Engineer replies within one business day. NDA first.
Questions
Frequently Asked Questions
Straight answers, written the way we'd say them on a call.
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Where to go next
Performance Testing rarely stands alone. These are the disciplines and resources teams pair it with most often.
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- A Senior Engineer replies, not a sales layer
- Within one business day, every time
- NDA available before you share any details
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