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What is a good Apdex score?

An Apdex score above 0.85 is typically considered good, 0.94 and above is considered excellent, and anything below 0.7 is a problem. Apdex (short for “Application Performance Index”) condenses all of your measured response times into a single satisfaction score between 0 and 1, where 1 means every user had a satisfying experience according to your satisfaction threshold.

How Apdex Is Calculated

Apdex starts with a threshold that you choose, called T, representing the response time a user on your site or application finds satisfying. The T value won’t be the same for every site or every organization; some user communities tolerate slower response times than others, so the T value you choose should be based on your team’s requirements.

When calculating Apdex, requests at or under T count as satisfied, requests between T and 4T count as tolerating, and anything slower than 4T counts as frustrated. The formula is:

Apdex = (Satisfied + Tolerating/2) / Total samples

Let’s say you set T to 1 second (your satisfaction threshold) and collect 1,000 samples: 800 finish within 1 second, 150 land between 1 and 4 seconds, and 50 take longer. That gives (800 + 150/2) / 1000 = 0.875, a good Apdex score.

Keep in mind that the score is only as meaningful as the T value you pick. The exact same response times might produce a great Apdex score or a terrible one depending on where you set the threshold. Set a satisfaction threshold that’s appropriate for your site.

Apdex Score Ranges

The typically used interpretation bands for Apdex scores are:

  • 0.94 to 1.00 — Excellent
  • 0.85 to 0.93 — Good
  • 0.70 to 0.84 — Fair
  • 0.50 to 0.69 — Poor
  • Below 0.50 — Unacceptable

Treat these as guidelines rather than laws of the universe. What’s acceptable depends on context: a checkout page and a heavyweight report export deserve different thresholds because users have different performance expectations around them, and a single site-wide T blurs that distinction.

Apdex vs Response Time Percentiles

Apdex trades detail for simplicity. One number is easy to put on a dashboard and track over time, so it’s a popular way to align teams around performance. However, it sometimes hides how bad the bad experiences actually are. A frustrated request at 4.1 seconds and one at 40 seconds could hurt your Apdex score identically, even though they’re drastically different problems for your users.

Percentile response times answer the same underlying question with more nuance: instead of “what fraction of users were satisfied?”, they tell you “how slow was it for the slowest 10%, 5%, or 1% of users?”. That’s why Loadster doesn’t compute an Apdex score; its reports give you detailed percentile response times (p50 through p99) alongside error rates, which together show both the median experience and the longtail. See Analyzing Test Results for more about how to read and interpret these percentiles.

Apdex and percentiles can be complementary rather than competing. If your team already tracks Apdex in an APM tool, a load test’s percentile and error graphs will help you understand why the Apdex score moves under heavy load, and help you identify outliers to optimize for.