As an AWS enthusiast, software architect, and developer, I’m deeply involved with tooling. For some time now, my focus has been particularly on AI tools—not only as a support for software development, but also from the perspective of engineering and process optimization. Whether in a professional context or in personal projects, AI has become indispensable to me.
I have always viewed AI tools as instruments that can make our lives easier. Since 2026, I have been an AWS Community Builder in the “Serverless” category, as this is where my developer’s heart lies and most of my projects are built on AWS serverless services. However, this article is less about serverless itself and more about dev tools—specifically Amazon Q, both in the IDE (VS Code) and in the AWS Management Console.
What is Amazon Q?
Amazon Q is an AI assistant that can be used both in the IDE and in the AWS Management Console. In addition, Amazon Q Business is a version that can be integrated with various business tools (such as SharePoint) to streamline processes and aggregate information from different systems.
In this article, however, I will focus on Amazon Q as a development and engineering tool. As with all AI tools, the following applies: Amazon Q can provide support, but the responsibility always remains with the developer.
Amazon Q in the AWS Management Console
In my opinion, Amazon Q in the AWS Management Console is primarily aimed at solution architects and technically savvy users.
One particularly interesting feature is the ability to record manual steps and generate CLI commands, SDK code, CDK applications, or CloudFormation templates from them. This allows you to track actions that were initially performed manually and then automate them.
This is especially useful if you initially feel more comfortable setting up infrastructure manually but want to make it reproducible later on.
The console chat can also be a good starting point for automation. I’ve personally used the management console’s context to generate scripts that allow me to automatically manage existing resources.
Amazon Q in the IDE (VS Code)
Within the IDE, Amazon Q can be used in both chat mode and inline. It also offers helpful shortcuts, such as for refactoring or documenting code snippets and entire components.
Chat mode makes it very easy to interact with Amazon Q, for example, to develop new components iteratively. You can think of it, to some extent, as collaborating with a colleague. This quickly brings to mind pair programming: What used to take place exclusively between multiple developers can now be supplemented by tools like Amazon Q—or even implemented entirely with them.
At the same time, Amazon Q can also be used in a traditional team setting to obtain an additional, well-founded opinion. Especially in the context of AWS projects, the results are often very convincing.
Lessons Learned
Amazon Q in the AWS Management Console is a huge help, especially for those new to AWS, but it can also serve as a starting point for automation. In some services, you can record click paths and derive CLI commands directly from them. This allows you to quickly automate a workflow that was previously performed manually.
The chat feature is also very useful here. I’ve already used it to generate specific CLI commands that subsequently served as the basis for automation.
Within the IDE, Amazon Q really shines in the context of AWS projects. This clearly highlights where the tool’s focus lies. Even though other tools may offer a wider selection of models or be stronger for more general use cases, Amazon Q can absolutely hold its own in the AWS environment. This strength is particularly evident in serverless projects—especially with AWS SAM.
Conclusion
Amazon Q is a powerful tool in both the console and the IDE, offering different strengths depending on the environment. In the IDE, it supports developers directly during the coding process, while in the AWS Management Console, it is better suited for architectural tasks and operational activities.
If you want to take it a step further and generate applications directly based on specifications, Kiro is also worth checking out.
What has been your experience with Amazon Q so far? What do your workflows look like when you use Amazon Q?
Feel free to share your thoughts in the comments—and stay tuned for more content on serverless computing and software development.
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