# \[AIAS-03] Explore the results

Let's now take a look at the results and benefits Akamas achieved in the optimization study we ran as an example.

First of all, the best configuration was quickly identified, providing a cost-efficiency increase of 17%, without affecting the response time.

<figure><img src="https://1455297369-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWaLHgCJcLYwHY9VZwaxO%2Fuploads%2Feqa4JRWuDmPYqprLiebI%2Fsummary-insights.png?alt=media&#x26;token=08deb5ac-84c6-44e5-aac4-d91186a33156" alt=""><figcaption></figcaption></figure>

Let's look at the best configuration in the Summary tab. Here you can see the right amount Akamas AI found for all CPU and memory requests & limits, considering the goal of maximizing cost efficiency and matching the application performance and reliability constraints.

<figure><img src="https://1455297369-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWaLHgCJcLYwHY9VZwaxO%2Fuploads%2F8fMXwobpYlyX0xtuLEUP%2Fbestconf-insights.png?alt=media&#x26;token=f11e2c88-56b0-454f-9c6b-5b3d22375524" alt=""><figcaption></figcaption></figure>

It’s interesting to notice the best configuration Akamas found for every single microservice:

* For some microservices (e.g., frontend), both the CPU and memory resources were increased.
* For others (e.g., paymentservice), the memory was decreased while the CPU was slightly increased.
* For some others (e.g., productcatalogservice), only the memory was decreased.

Let's navigate the Highlights section, which provides details of the best experiment for each of the selected KPIs.

<figure><img src="https://1455297369-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWaLHgCJcLYwHY9VZwaxO%2Fuploads%2FZ3HJhPDlZ2P04Z2Blx0v%2Fbest-insights.png?alt=media&#x26;token=82611db2-0030-4e87-84e6-ee7269d8e936" alt=""><figcaption></figcaption></figure>

The best experiments according to the selected KPIs are automatically tagged and listed in the table. Interestingly, experiment #34 reached the best efficiency, while experiment #53 achieved the best throughput and a significant decrease in the application response time. Also, notice that a couple of identified configurations improved the application response time even more (up to 87%)!

The experiments can be plotted with the histogram icon to better analyze the impact of the selected configurations.

<figure><img src="https://1455297369-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWaLHgCJcLYwHY9VZwaxO%2Fuploads%2FXOAEW8KW0nk94ifkJJv9%2Fradarchart-insights.png?alt=media&#x26;token=7b3ac2bf-5681-4903-9e37-25ef61546ba7" alt=""><figcaption></figcaption></figure>

This optimization study shows how it is possible to tune a Kubernetes application made by several microservices, which represents a complex challenge, typically requiring days or weeks of time and effort even for expert performance engineers, developers, or SRE. With Akamas, the optimization study only took about 4 hours to automatically identify the optimal configuration for each Kubernetes microservice.

{% hint style="success" %}
**Congratulations!** You have completed your first study to optimize a Kubernetes application!\
\
As a next step, take a look at all the other [guides](https://docs.akamas.io/quick-guides/quick-guides-akamas-in-a-sandbox) available in our free trial sandbox.\
\
Have you already completed all of the free trial guides? Get in touch with us and share your feedback!
{% endhint %}