Performance Testing That
Finds the Limit.

The earlier, the cheaper. Ideal timing is before your first production launch and then before any release that introduces significant architectural changes like new databases, caching layers, third-party integrations, or major traffic-generating features. The worst time to find a performance problem is after launch when real users are affected and your team is firefighting under pressure. Running a baseline load test before launch and re-running it after major changes is the minimum viable performance testing program for most teams.

Industry benchmarks give you a starting point: under 200ms for API responses is generally considered fast, 200ms to 1 second is acceptable for most web interactions, and anything over 3 seconds at the P95 level is a problem worth investigating. But the right targets for your system depend on your user expectations, your competitors, and your SLAs with customers or stakeholders. We work with you to define targets before testing begins, not after results come back. Testing against undefined targets is guesswork.

Yes, and often this is the more useful approach. API-level load testing isolates backend performance from frontend rendering variability, giving you a clearer picture of where the bottleneck actually lives. We test at the protocol level, directly against your endpoints, and can separate that from browser-rendered performance testing if you also want to measure user-facing load times. The layer you test depends on where you need the signal. We scope this during the requirements phase so you get data that is actually actionable.

We model traffic based on your actual analytics: what pages users visit, in what order, with what think time between actions, and what the distribution looks like across different user types. A login-heavy morning rush looks different from an API-heavy batch processing load. We do not just fire concurrent requests at a single endpoint and call it a load test. We build scenarios that reflect real user behavior so the results tell you something meaningful. For systems without existing analytics, we collaborate with your team to define representative journeys based on product knowledge.

The report covers: test objectives and acceptance criteria, scenario descriptions and load profiles, key metrics (response time at P50/P95/P99, throughput, error rate, resource utilization), identified bottlenecks with evidence and root cause analysis, pass/fail verdict against your defined SLAs, and prioritized recommendations with expected impact. Where relevant, we include infrastructure behavior charts and comparisons against baseline. The goal is a document your team can hand to an engineer and use to fix the problem. Not a dashboard screenshot with no interpretation.