Overview
Finding out during a big sale that your database only accepts 20 connections is expensive. We model your traffic from real analytics, including how many visitors browse versus how many reach checkout, and replay it through a scriptable load generator against an environment that mirrors production. Load keeps rising until something gives way, and we then pin down the cause, such as an inefficient database query, a saturated thread pool or throttling at the gateway. Measured limits arrive alongside a ranked list of bottlenecks and fixes.
What you receive
- A traffic model shaped by your actual usage patterns and expected peak
- Reusable load scripts handed over for your team to rerun
- Fully monitored runs at baseline, peak and stress levels, plus a soak test
- The failure threshold, expressed as concurrent users and as throughput per second
- Root causes located across application code, database, caching layer and servers
- Prioritised optimisation list with the expected gain for each
- Capacity plan matched to your forecast growth
How it runs
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Step - 01
Model
We turn your analytics and launch expectations into realistic user behaviour profiles.
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Step - 02
Prepare
The test environment is sized, monitoring added and load scripts written.
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Step - 03
Execute
We start with a baseline, raise load steadily until failure, then soak.
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Step - 04
Analyse
We identify bottlenecks and put numbers on each recommended improvement.
Timeline: 12 working days, counted from the day we receive what we need from you.
Is it a fit?
A good match for
- Businesses that see traffic surge during festivals, sales or campaigns
- Products that visibly slow down during office hours
- Teams deciding on infrastructure size ahead of a launch
What we need from you
- A near-production environment you permit us to overload until it fails
- Access to monitoring, or approval to add our own instrumentation
- Traffic data, or a realistic estimate of your expected peak
Why it helps
- Capacity decisions based on measured numbers, not guesswork
- You discover the weakest component before your customers do
- You keep the scripts and can rerun them before any later release
Questions
Only under strict limits, during quiet hours and with your written agreement. A staging environment that closely matches production is safer and usually gives more useful results.
It limits what we can say. We scale the results with clear caveats, but a much smaller environment reveals how the system behaves rather than its true absolute ceiling.
Fixes are not part of this service. After the weak points are identified, we can quote any optimisation work separately so you only pay for changes you choose to make.