> For the complete documentation index, see [llms.txt](https://docs.akamas.io/quick-guides/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akamas.io/quick-guides/quick-guides-akamas-in-a-sandbox/aias-03-guide-create-a-study-to-optimize-k8s-microservices-costs-using-the-akamas-cli/aias-03-explore-the-results.md).

# \[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="/files/Icwz1oGDfv3b0ww4Lb5U" 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="/files/gcyNK6dpQLkUNKTr590i" 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="/files/4Xb280jlayQ4NaIE79sq" 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="/files/DwjryNf5NimIQ3JfjDSz" 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](/quick-guides/quick-guides-akamas-in-a-sandbox.md) 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 %}


---

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