komiser VS aws-lambda-power-tuning

Here's my recent Pull request that got merged i am quite new to it its been like 2 months i started open source

Note: this comes from the tailwarden project which advertises themselves as open source while using a ELv2 license which puts limitations on certain use. Looks like the project switched from MIT last year.

Yes its free and open-source project, here's the repo: https://github.com/tailwarden/komiser :)

For anybody interested in cloud savings: Join us tomorrow at 10am UTC to learn how to optimise cloud costs (including Azure) with the open source tool Komiser!

Komiser is an open-source cloud cost optimization tool that can help with this challenge. It provides insights into the costs associated with different regions, managed services, and individual resources, making it a valuable addition to any cloud environment. But not only that, Komiser's Kubernetes integration offers even greater visibility into the Kubernetes clusters running on compute instances, allowing users to create custom views that dynamically update to reflect the current state of their microservice resources.

The tool was open-sourced and become a cloud-agnostic with the support of major cloud providers. Upon release, it gained popularity and my colleague Cyril and I noticed that many organizations shared similar challenges, particularly regarding limited visibility into their infrastructure and related tools. To address this challenge, we launched Tailwarden, an open-core company founded on the principles of Komiser and built on an open-source model. Our aim is to empower developers by improving transparency and collaboration in the cloud. Our mission is to put control of the cloud into the hands of developers by tackling one of the most pressing issues in the space.

With Komiser, you can connect multiple cloud accounts, and build your cloud inventory in seconds. In the example below, we’ve connected an AWS production and sandbox accounts:

Neat project! But I noticed it's licensed as ELv2 which isn't typically considered open source, since such a license does not meet the common open source definition due to it's limitations on usage.

Komiser is an open-source cloud-agnostic resource manager. It integrates with multiple cloud providers (including AWS), builds a cloud asset inventory, and helps you break down your cost at the resource level.

Sometimes, changing services, like opting for HTTP over REST API Gateway, leveraging tools like Lambda Powertuning to optimize functions, or reducing a CloudWatch log retention and changing log level, can lead to significant savings.

In case of not enabling SnapStart for the Lambda function we observed that increasing memory reduces the warm execution time for our use case especially for p>90. As adding more memory to the Lambda function is also a cost factor, the sweet spot between cold and warm start time and cost is somewhere between 768 and 1204 MB memory setting for the Lambda function for our use case. You can use AWS Lambda Power Tuning for very nice visualisations.

The aws lambda power tuning tool helps optimise the Lambda performance and cost in a data-driven manner. Let's try it out:

For Lambda, a very useful tool to help optimise is the AWS Lambda Power Tuning tool, released by Alex Casalboni, Developer Advocate at AWS: https://github.com/alexcasalboni/aws-lambda-power-tuning

Lambda memory affects not only the CPU performance and and host execution priority, but also network performance. Be wary though as the price scales linearly. You can use a tool like Lambda Power Tuning to find the sweet spot for your application. https://github.com/alexcasalboni/aws-lambda-power-tuning

This tool, which is open source and available here, takes the form of a Step Function that is deployed on your AWS account. The purpose of this Step Function is to run your lambda with different memory configurations several times and output a comparison in the form of a graph (or JSON) to try to find the optimal balance between cost and execution time. There are three possible optimization modes: cost, execution time, or a "balanced" mode where it tries to find a balance between the two.

The AWS Documentation's Memory and Computing Power page is a good starting point. To avoid configuring it manually, it's worth checking out AWS Lambda Power Tuning, which will help you find the sweet spot.

Utilizing tools such as AWS X-Ray, AWS Lambda Power Tuning, and AWS Lambda Powertools tracer utility is recommended. Read more about it here.

Install the AWS SAM CLI in your local environment. Configure your AWS credentials (requires AWS CLI installed): $ aws configure Clone this git repository: $ git clone https://github.com/alexcasalboni/aws-lambda-power-tuning.git Build the Lambda layer and any other dependencies (Docker is required): $ cd ./aws-lambda-power-tuning $ sam build -u sam build -u will run SAM build using a Docker container image that provides an environment similar to that which your function would run in. SAM build in-turn looks at your AWS SAM template file for information about Lambda functions and layers in this project. Once the build has completed you should see output that states Build Succeeded. If not there will be error messages providing guidance on what went wrong. Deploy the application using the SAM deploy "guided" mode: $ sam deploy -g

Use AWS Lambda Power Tuning to balance cost and performance.