# Apdex score Copy as Markdown[Open in ChatGPT](https://chatgpt.com/?q=Read%20https%3A%2F%2Fdocs-docusaurus.kinsta.page%2Fuser-guides%2Fapm%2Ffeatures%2Fapdex-score.md%20and%20help%20me%20with%20my%20question%20about%20this%20Coralogix%20documentation%20page.)[Open in Claude](https://claude.ai/new?q=Read%20https%3A%2F%2Fdocs-docusaurus.kinsta.page%2Fuser-guides%2Fapm%2Ffeatures%2Fapdex-score.md%20and%20help%20me%20with%20my%20question%20about%20this%20Coralogix%20documentation%20page.) ## Overview[​](#overview "Direct link to Overview") AApplication Performance Index (Apdex) is a standardized metric used to measure and quantify user satisfaction with the response time of software applications. It provides a numerical score on a scale of 0 to 1, where a score of 0-0.74 is considered poor, 0.75-0.84 is fair, 0.85-0.94 is good, and 0.95-1 is excellent. Apdex takes into account the response time threshold, categorizing user interactions as satisfied, tolerated, or frustrating based on a predefined performance threshold. This metric is particularly valuable for organizations and developers in assessing and optimizing application performance, as it offers a concise and standardized way to communicate and benchmark user satisfaction with application responsiveness. ## How it works[​](#how-it-works "Direct link to How it works") An Apdex score is calculated using a simple formula that involves defining a response time threshold and then categorizing "Satisfied" user requests based on those thresholds. The formula is as follows: [![](data:image/webp;base64,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)](data:image/webp;base64,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) In this formula, request satisfaction is categorized as follows: * **Satisfied requests**: The response time is less than or equal to T (Threshold). * **Tolerated requests**: The response time is greater than T and less than or equal to 4T. * **Frustrated requests**: The response time is greater than 4T or the request returns a server-side error (not shown in the formula). * **Total requests**: The total number of requests. ## Configure response time threshold[​](#configure-response-time-threshold "Direct link to Configure response time threshold") The threshold (T) is used to calculate Apdex and classify requests as satisfactory (≤ T), tolerated (> T and ≤ 4T), or frustrated (> 4T). For example, if T = 2 seconds, then 0–2s is satisfactory, 2–8s is tolerated, and ≥ 8s is frustrated. Apdex then produces a score from 0 to 1, where values closer to 1 indicate higher user satisfaction. For example, if the threshold is 0.5 seconds (default value), it produces the following response times: * 0 - 0.5 seconds: **Satisfied** * 0.51 - 2 seconds: **Tolerated** * 2+ seconds: **Frustrated** To configure response time threshold for your service: 1. In your Coralogix toolbar, navigate to **APM**, then **Service Catalog**. 2. Select a service to view the Overview widgets. 3. From the **Apdex score** widget, select **Threshold**. [![Apdex score widget](/assets/images/apdex-wiget-48fdaa282e49030ec0e2be9c86be6517.webp)](https://docs-docusaurus.kinsta.page/assets/images/apdex-wiget-48fdaa282e49030ec0e2be9c86be6517.webp) 4. Select the threshold and unit for the Apdex score. [![Apdex configuration](/assets/images/set-up-apdex-04a954ec1498410cd9b9d866e836c0d9.webp)](https://docs-docusaurus.kinsta.page/assets/images/set-up-apdex-04a954ec1498410cd9b9d866e836c0d9.webp) The Latency graph shows your selected threshold along with the P99, P95, P75 and Average latencies, enabling you to see how the threshold compares with the current latency of your service. This graph offers a clear context to evaluate the appropriateness of your threshold. For instance, setting a threshold of 5 seconds for services with a p99 latency of 1 second can be overly tolerant, indicating a need for a more stringent threshold. 5. Select **Save changes**. ## When Apdex cannot be calculated[​](#when-apdex-cannot-be-calculated "Direct link to When Apdex cannot be calculated") If your service's duration histogram is missing the threshold bucket (`le=T`) or its 4× counterpart (`le=4T`), the **Apdex score** widget cannot run the calculation and shows an empty state in place of a score. To resolve this, configure your collector so both `T` and `4T` are present in the histogram bucket list. For the full setup, see [Apdex settings](https://docs-docusaurus.kinsta.page/user-guides/apm/getting-started/span-metrics-recommended-configuration/.md#apdex-settings) under Span Metrics configuration. ## Next steps[​](#next-steps "Direct link to Next steps") Compare Apdex across releases by enabling [Group by service version](https://docs-docusaurus.kinsta.page/user-guides/apm/features/group-by-service-version/.md). ## Additional resources[​](#additional-resources "Direct link to Additional resources") | | | | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Documentation | [APM](https://docs-docusaurus.kinsta.page/user-guides/apm/getting-started/apm-onboarding-tutorial/.md)
[Service Catalog](https://docs-docusaurus.kinsta.page/user-guides/apm/features/service-catalog/.md) | | Blogs | [Apdex Score: Calculation, Pros/Cons & 5 Ways to Improve Yours - Coralogix](https://coralogix.com/guides/real-user-monitoring/apdex-score/) |