Performance testing services that find your breaking point before your users do
Innostax runs load tests, stress tests, and performance profiling against your actual system — identifying bottlenecks, memory leaks, and scalability limits before they become production incidents. Because finding out your system can't handle 10,000 concurrent users during a launch is the wrong time to find out.
The performance problem that only surfaces at the worst moment
Your system works fine in development. Production is a different environment.
Development environments are clean. Databases are small. There’s one user — you — making one request at a time. Everything is fast because nothing is under load.
Production is different. Real users make concurrent requests. Databases grow. Background jobs compete with API calls for resources. A query that returns in 50ms against a test dataset returns in 4 seconds against a production one. A connection pool that’s never been exhausted in testing gets exhausted in the first hour of a product launch.
Performance failures are particularly damaging because they tend to happen at exactly the wrong moment — a product launch, a marketing campaign, a spike in press coverage. The moment when you have the most users is the moment when your system is most likely to fail under load, and the moment when the cost of that failure is highest.
Performance testing is the discipline of finding those failure modes before your users do — under controlled conditions where you can fix them, not under production conditions where you can only apologise.
What we test
Find the right option for your situation.
Every engagement is different. Use the links below to explore focused pages for this service.
Load testing
Simulating your expected production traffic to verify that your system performs within acceptable parameters under normal operating conditions. We establish baselines — response times, throughput, error rates, resource utilisation — and validate that your system meets them at the load levels your users will actually generate.
Learn more →Stress testing
Pushing your system beyond its expected operating limits to find where it breaks and how it breaks. Stress testing answers the question your load test doesn't: what happens when traffic is 3x your expected peak? Does the system degrade gracefully, or does it fail catastrophically? Graceful degradation is a design goal. Stress testing is how you verify you've achieved it.
Learn more →Spike testing
Simulating sudden, sharp increases in traffic — the kind generated by a product launch, a viral moment, or a marketing campaign. Spike testing is distinct from load testing because it's not about sustained load, it's about the system's ability to absorb a rapid change in demand and recover when it subsides.
Learn more →Soak testing (endurance testing)
Running your system under sustained load for an extended period — hours or days — to identify issues that only surface over time. Memory leaks, connection pool exhaustion, database lock accumulation, log file growth. The failure modes that don't appear in a 30-minute load test but bring a system down after 48 hours of continuous operation.
Learn more →Performance profiling
Identifying the specific code paths, queries, and operations that are consuming disproportionate resources — the 20% of your codebase that's responsible for 80% of your response time. Profiling turns "the system is slow" into "this specific query is slow, for this specific reason, and here's how to fix it."
Learn more →API performance testing
Load testing individual API endpoints to identify which ones degrade under concurrent requests, which ones have response time variance that suggests caching opportunities, and which ones are bottlenecks for the user flows that depend on them.
Learn more →
Against your actual system. With metrics that mean something.
Baseline establishment first.
Before we run load tests, we establish what “good” looks like for your system — acceptable response times, throughput targets, error rate thresholds. Performance testing without agreed baselines produces numbers. Performance testing with agreed baselines produces pass/fail results your team can act on.
Realistic traffic simulation.
We model test scenarios against your actual traffic patterns — not generic load profiles. The concurrent user counts, the request distribution across endpoints, the think times between user actions — all calibrated to reflect how your real users behave, not how a test script behaves.
Production-representative environments.
Performance tests run against environments that mirror production as closely as possible — same infrastructure sizing, same database volumes, same external service dependencies. Tests run against under-resourced environments produce results that don’t predict production behaviour.
Root cause, not just symptoms.
When a performance test reveals a problem, we don’t just report the symptom — slow response times, high error rates — we profile the system to identify the root cause. The slow query. The N+1 problem. The missing cache. The connection pool that’s too small. Actionable findings, not just numbers.
Continuous performance monitoring.
For teams that need ongoing visibility into performance trends — not just pre-launch testing — we configure monitoring that tracks performance metrics in production and alerts on degradation before it becomes a user-facing incident.
Performance failures happen at launch. The test that prevents them happens now.
2-week free trial on your actual system.
Real load tests against your real infrastructure — not a sanitised demo environment. You’ll see within two weeks whether we find real performance risks or just generate a report. If it’s the latter, walk away. No invoice.
1-day termination notice.
If the engagement isn’t surfacing the performance insights your system needs, you’re out tomorrow. No lock-in, no notice periods.
Engineers who understand your architecture.
Performance problems are architecture problems. Great Place to Work certified — the engineer who profiles your system in month one understands the context behind the numbers in month six. Performance optimisation without architectural context produces local fixes that miss systemic causes.
Performance Testing Services for High-Traffic, Scalable, and SLA-Critical Systems
Engineering teams approaching a significant launch
product launches, marketing campaigns, or anything that will generate a sharp increase in traffic. The time to find your system’s breaking point is before the launch, not during it.
CTOs at growth-stage companies
whose systems were designed for a fraction of their current load. The performance characteristics that were acceptable at 1,000 users become unacceptable at 100,000. Performance testing identifies what needs to change before users experience the degradation.
Teams with SLA commitments
enterprise customers, regulated industries, or any product where uptime and response time are contractual obligations. Performance testing is how you verify you can meet those obligations before you’re held to them.
Teams after a production incident
who need to understand what happened and ensure it doesn’t happen again. Post-incident performance testing establishes the new baseline and validates that the fix actually addresses the root cause.
Performance under real conditions
What managed delivery looks like in the real world.
Travelstart
15% increase
Platform enhancements on a high-traffic travel booking system only drive revenue if they hold up under real production load. Innostax’s work on the Travelstart platform — including performance validation under production traffic conditions — contributed to a measurable 15% increase in ticket sales. Improvements that work in development and fail under load don’t move business metrics.
Nuw
100,000+ downloads
Scaling a consumer app to 100,000+ downloads requires a backend that handles concurrent user load without degrading. Innostax’s performance work on the Nuw build ensured the app held up under the load that a top 10 App Store ranking generates — the kind of load that exposes every performance shortcut taken during development.
Online Education Platform
The $300,000 Mulesoft replacement wasn’t just a cost saving — it was a performance improvement. The custom integration layer Innostax built was designed for the platform’s actual traffic patterns, not a generic middleware configuration. Right-sized infrastructure performs better and costs less.
Tech stack
Performance Testing Tech Stack and Tools We Use
We use modern performance testing and observability tools to measure system load, monitor infrastructure, profile databases, and ensure applications stay fast, stable, and scalable under real-world production traffic.
- k6
- JMeter
- Locust
- Gatling
- Datadog
- New Relic
- Sentry
- Prometheus
- Grafana
- CloudWatch
FAQ about Performance Testing Services
Load testing validates that your system performs acceptably under expected production traffic — it's about verifying normal operating conditions. Stress testing pushes beyond expected limits to find where the system breaks and how it breaks — it's about understanding failure modes. Both are necessary: load testing tells you whether you're ready for launch, stress testing tells you what happens when launch goes better than expected.
We start with your actual traffic data — current peak concurrent users, growth projections, and any specific events (launches, campaigns) that will generate spikes. If you don't have production traffic data yet, we use industry benchmarks for your product category as a starting point and adjust as real data becomes available.
Staging is the right environment for most performance testing — it lets us run tests that would be disruptive in production without affecting real users. The key requirement is that staging mirrors production as closely as possible: same infrastructure sizing, same database volumes, same external service configurations. Performance tests against an under-resourced staging environment produce results that don't predict production behaviour.
We profile the system to identify the root cause — not just the symptom. Slow response times are a symptom. The specific query, the missing index, the N+1 problem, the connection pool configuration — those are root causes. We deliver actionable findings with specific remediation recommendations, not just a report showing that something is slow.
At minimum, before any significant release and before any event expected to generate a traffic spike. For teams shipping frequently, we recommend integrating lightweight performance benchmarks into the CI/CD pipeline — so performance regressions are caught at the PR level rather than discovered pre-launch. Full load and stress test cycles should run quarterly or when significant architectural changes are made.