Relying on average response time in API monitoring can be dangerously misleading. This article explores how percentile-based metrics—like the 95th or 99th percentile—reveal the true user experience and help detect hidden performance problems before they impact your users.
In the world of API monitoring, average response time has long been used as a standard metric. But here's the catch: it often lies. While your dashboard might show a neat "300ms average," users could still be experiencing frustrating delays. So what's going on?
Averages are easily skewed by outliers. One very fast or very slow response can distort the truth:
In short, average response time tells only part of the story.
Let’s say your API has the following response times during a minute:
250ms, 260ms, 270ms, 290ms, 300ms, 310ms, 320ms, 330ms, 340ms, 3,000ms
The average is about 462ms — not too bad at first glance. But that last spike (3 seconds!) could ruin a user’s checkout experience. Your average metric masks that critical delay.
Percentiles, especially P95 and P99, offer a more honest view:
Percentile metrics help teams detect minor issues before they become major problems.
Watchman Tower goes beyond average values. With advanced response time monitoring, you get:
This means better insights, faster diagnosis, and happier users.
If you're still relying on averages, you're probably missing what matters.
Start tracking your API’s performance the smart way—with percentile metrics.
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