Load testing
Functional testing answers whether a feature works. Load testing answers whether it still works once real numbers of people are using it at the same time. It does that by simulating multiple concurrent users or requests hitting a system, then watching how response time, throughput, and error rate change as that simulated traffic climbs.
Running it usually means generating synthetic load with a dedicated tool, ramping traffic up gradually or holding it steady at a target level, and tracking metrics like average response time, a slower percentile like p95 or p99, requests handled per second, and how the error rate shifts as load increases. That testing generally happens against a staging environment built to mirror production as closely as possible, since throwing real synthetic load at production itself risks affecting actual users.
Load testing is often confused with stress testing, but they ask different questions. Load testing checks behavior at the traffic level a system is actually expected to see, a realistic Tuesday afternoon or a known sale event. Stress testing pushes well past that expected level on purpose, to find the point where the system actually breaks and how it fails, which matters for understanding real limits rather than everyday operation.
What gets measured
- Response time, how long a request takes under load, tracked as both an average and a slower percentile like p95 or p99.
- Throughput, how many requests the system handles per second at a given load level.
- Error rate, how many requests start failing as load increases.
- Resource usage, how CPU, memory, and database connections hold up as traffic climbs.
Where it shows up
- Before a big launch, sale, or marketing push where a spike in traffic is expected.
- Checking whether a new feature or a database change holds up once it's under realistic load.
- Finding the point where response time degrades past what's acceptable, before users actually hit it.
- Validating that autoscaling or infrastructure changes behave the way they're meant to under real demand.
Benefits and challenges
| Benefit | Challenge |
|---|---|
| Finds a performance problem before a real spike in traffic does, while it's still cheap to fix. | Needs a realistic environment and realistic traffic patterns to mean anything, unrealistic infrastructure or usage gives a misleading result. |
| Gives concrete numbers, response time, throughput, error rate, instead of a guess about whether the system can handle it. | Generating and running meaningful load takes real infrastructure and setup, it's not as simple as running a functional test suite. |
| Builds confidence ahead of a predictable spike, like a sale or a launch, instead of finding out live. | A result is only as good as how closely the test's traffic pattern matches how real users actually behave. |
Frequently asked questions
What's the difference between load testing and stress testing?
Load testing checks how a system behaves under the amount of traffic it's actually expected to see. Stress testing goes further, pushing traffic past that expected level to find where the system actually breaks and how it fails, which matters for understanding a system's real limits rather than just its normal operating range.
How often should load testing run?
It depends on how often traffic patterns or the system itself change. Many teams run it before a known spike, like a sale or a major launch, and again after a significant architecture or infrastructure change, rather than as a constant part of every release the way a regression suite might be.
Can load testing be automated as part of a CI/CD pipeline?
To an extent. A lighter load test can run automatically as part of a pipeline to catch an obvious regression in response time. A full-scale load test simulating real peak traffic is usually heavier and run separately, on a schedule or ahead of a specific event, rather than on every single commit.
How Klarent helps
Klarent's AI agents focus on functional and regression coverage across an application's user flows, complementing dedicated load testing tools that generate and measure traffic at scale.