Service
Scalability testing
Know your ceiling before your users find it for you
Scalability testing answers a question every growing product eventually faces: will this hold up when more people show up — and how much should we actually pay to make sure it does?
We combine architectural analysis with real-world load simulation to find your system's true limits, whether that means proving you can handle a million new users in a year, or proving you don't need to overpay for infrastructure a smaller launch will never touch.
How we deliver
What we test for
Architectural analysis, realistic workloads, and production-representative load — so you know the ceiling, and what you can skip paying for.
Static analysis
Before a line of load is generated, we walk the architecture component by component and audit every third-party dependency's SLA and plan limits against expected load. Some of the most damaging scalability risks aren't in your code — they're in a vendor's fine print. We've found integrations capped at a few hundred API calls a day, and email providers whose sending limits silently break registration at campaign peak — failures no load test would catch, because the app itself was never the bottleneck.
Workload modeling
A load test is only as good as the workload behind it. We build profiles from real usage data wherever it exists, mapped across intra-day peaks and seasonal cycles — not flat daily averages. We model actual user journeys (login → search → cart → purchase) rather than isolated request counts, weight requests by true cost (a report export is not a page load), and segment by role, because an admin and an end user never behave the same way.
Graduated load profiles
Ramp, step, and spike tests that reveal the first constraint — not just max RPS. We generate production-representative load to confirm (or disprove) what static analysis predicts, including issues like uneven message-queue consumer configuration that only surface once real concurrent traffic hits a distributed system.
Autoscaling validation
Confirm HPA, cluster autoscaler, and queue consumers keep pace with demand — and that you aren't carrying years of unused capacity for growth that may never come.
Breaking point analysis
Document failure modes and recovery behavior so ops knows what to expect when the system hits its ceiling.
Proven at scale — and at restraint
We've supported a national telecom brand launch that scaled from zero to over a million subscribers in its first year, and we've helped a digital telco deliberately right-size infrastructure for a smaller, defined market entry — avoiding years of overpaid capacity for growth that, as it turned out, never came. Scalability testing isn't only about surviving growth. Done right, it's also capital discipline.