The difference between load, stress, spike, and soak tests
“We need a load test.” This sounds unambiguous, but it is not. Load testing includes several types of tests, and each answers a different question. If the team chooses the wrong type, it may confirm normal traffic while overlooking the risk of a sudden or prolonged load.
The difference between them is practical: one test verifies expected traffic, another the system’s limit, a sudden surge, or long-term stability.
Common problem: the test answers a different question than the one you have
A short test under normal traffic does not confirm how the system behaves during a sudden surge or after several hours under load. Define the risk first and choose the type of test second. Otherwise, you may receive a correct measurement that does not help with the decision before a campaign or release.
Four questions, four tests
Load—can we handle what we expect? We apply a load that reflects normal or expected traffic and monitor whether response times and the error rate remain within the agreed limits. It answers whether the system meets expectations under the planned load. A repeated test can also highlight performance degradation after a change.
Stress—where is the limit? We gradually increase the load until the system no longer meets the agreed limits. We observe the point at which response times or the error rate rise sharply and what is exhausted first, such as database connections, memory, threads, or CPU. A stress test therefore helps estimate both the capacity limit and the system’s behaviour beyond it.
Spike—can we handle a sudden surge? You send a newsletter, and a large share of its recipients arrive within a few minutes. A spike test does not simulate a gradual increase but a jump. It verifies both the surge itself and whether the system returns to its normal state afterwards.
Soak—can we sustain it? We maintain an even load for several hours. This can reveal slow memory leaks, unclosed connections, or growing logs—problems that a short test may not show.
How we approach it
We design scenarios around how your customers behave. Instead of repeatedly loading one simple page, we model a real journey: finding a product, adding it to the cart, signing in, or paying. The load then affects the same parts of the system as real traffic.
We write scripts in k6 or, depending on the environment, Locust. We use JMeter for older non-HTTP protocols or when you already have an existing suite. We monitor response times, the error rate, and throughput during the test. If application, database, and infrastructure metrics are also available, we compare them on a shared timeline.
The tests we run depend on the stated risk. A spike or stress test may be important before a campaign, a comparative load test after an infrastructure change, and a soak test for a prolonged load. In practice, several types are often combined, but the scope should match the goal and the available time.
What you gain
The result is capacity measured for a specific scenario and agreed limits, a description of how the system behaved under load, and likely bottlenecks. Recommendations can be ranked by risk, expected benefit, and effort.
You only need to know a typical user scenario and be able to complete it manually in the system. You do not need to script anything.
Next step
Start with the question the test should answer: expected traffic, the capacity limit, a sudden peak, or long-term stability. Use it to choose the type of test, scenario, measurement duration, and success thresholds.