AWS CodeWhisperer vs Copilot: Features and Issues

When I first started using AI tools (ChatGPT in my case), I would only use them for occasional text rewrites and understanding lines of code. 

Fast forward to 2025, I now have multiple AI tools within my IDE, each helping me with different tasks (which we will read about in the later half of the article) and also some customizations, making AI my pair programmer. 

When it comes to teams, according to reports by IT Revolution, there is a 13.5% increase in code commits and a 38.4% rise in compilation frequency. 

This suggests that developers using AI assistants are producing more code and iterating more quickly and that there is a need among developers for tools to do more than just auto-complete 

In the later half of the article, we will see how GitHub Copilot has improved with agent mode, and how by integrating it with tools like Pieces, we can achieve more. 

With this, we have a “good problem” to solve. 

Almost every month, there is a new product/AI code assist tool coming up, and existing competitors are adding more features. 

Now this brings us to the question, “Which tool should I use?” 

In this article, I will introduce you to Copilot and CodeWhisperer and do a detailed comparison of both, helping you choose the one that best suits your coding workflow.

If you want to know more about AI tools and comparison among the popular ones such as tabnine vs copilot vs code whisperer read this blog on “10 Best AI Tools for Developers in 2025”.

What does “copilot” actually mean?

Before AI became popular, "copilot" referred to pilots sitting in the second chair of the cockpit, assisting the captain in flying the plane. 

Then, in 2021, GitHub launched Copilot (an AI pair programmer), and since then, "copilots" means AI tools that can help you work more efficiently and creatively.

On a daily basis, some of the tasks I need to do in my dev job are: understanding pieces of code that have already been written by someone else, working on new features, fixing existing bugs, documenting the code, and collaborating with other developers for code review and pair programming. 

All of these can be done with the help of GitHub Copilot within IDEs of your choice. 

GitHub Copilot has a free plan and now comes already installed in VSCode, but also has paid plans starting from $10 per month. You can try the free version of GitHub Copilot here and to use it as plugins in IDEs of your choice, you can install it from here.

What makes GitHub Copilot really stand out for me is their recent agent mode. 

In agent mode, it acts as your peer programmer, analyzing your codebase, reading relevant files, proposing file edits, and running terminal commands and tests. 

It responds to compile and lint errors, monitors terminal and test output, and auto-corrects in a loop until the task is completed.

Copilot is incredibly helpful, especially for writing code. 

However, I realized there are other challenges I face regularly, such as:

• Remembering resources, links, and solutions I used to solve specific problems. 

A recent example is the deployment errors I encountered in Vercel. Even after solving them multiple times, I still had to search through answers every time the error reappeared.

• The time spent gathering context from different sources. 

The common developer question, "How many tabs do you have open?" is not just a joke. 

When building a solution, we often refer to multiple resources like documentation, code examples, and forums. Even if we don't notice, a significant amount of time is spent on this.

That's when I took advantage of Copilot's flexibility to integrate with other tools and used Pieces along with Copilot to help with long-term memory and code assistance.

You can use Pieces in your IDE of choice similar to GitHub Copilot, and with all the resources (like docs, code, etc.) that you are accessing, you can use that as context and ask Pieces Copilot questions related to it.

Let’s see how this works. I am using Copilot and Pieces with VSCode and working on building a GitHub Action. If I read the GitHub documentation once and keep the long-term memory feature on in Pieces, it will remember and use that as context. Now when I ask it, How do I declare inputs in a GitHub Action? it will give me the solution, which I can add in the GitHub Copilot chat to build my action.

What is CodeWhisperer?

CodeWhisperer is an AI tool developed by Amazon that can help you code and deploy AWS services. 

As per reviews from users on G2, it is popular among devs who use AWS and work with cloud infrastructure, and is also very useful for command line completions, especially in Mac. 

It provides code suggestions in real-time, based on your existing code and comment, and can help you code functions from scratch. 

When you start typing out single lines of code or comments, CodeWhisperer makes suggestions based on your current and previous inputs.

As of April 30th 2024, Amazon Code Whisperer is a part of Amazon Q Developer.

To download Q Developer, go to the link here and click on Download Amazon Q Developer. It will give you a list of IDEs to choose from. Select the IDE you work with and download it to get started. 

You can read the full installation instructions here. 

When compared with Pieces Copilot, the installation process is not a single click and does not follow a `one-size-fits-all` solution.

 If you want to integrate it with your choice of IDE, you need to download it from their specific marketplace and then proceed with installation. 

One feature in Q Developer that really stands out for me, is security scanning. It can scan your code for vulnerabilities such as exposed credentials and log injection and suggest fixes to write better and safer code.

Other key features of Q Developer are:

• Understanding legacy codebase: As developers, part of our work also includes improving and updating the existing codebase. Using Q Developer, you can select lines of code and ask it to explain what they mean. This is very helpful, especially in large codebases, and helps you start refactoring codebases faster than before.

• AWS Integration: Q Developer is highly integrated with AWS services, it can help in managing cloud infrastructure, querying resources, generating AWS CLI commands, and receiving cost-related insights.

• Integration with IDEs: It has integration with popularly used IDEs like JetBrains, VSCode, Visual Studio, and Eclipse,and can also be used directly from the command line in MacOS.

• Building ML models: It can assist in machine learning development in Amazon SageMaker Studio using natural language, across data preparation, model training, and model deployment.

• Pricing and availability: Q Developer is accessible through the AWS Console, IDEs mentioned above, and platforms like Slack and GitLab Duo. It has a free tier as well as a pro tier that is priced at $19 per month per user.

Similar to GitHub Copilot, you can also use Q Developer with multiple tools. When integrated with Pieces within your IDE of choice (VSCode in my case), it is effective in adding context to your code, which Q Developer does not do by default.

For example, I want to write safer code, so I asked Pieces Copilot about the security practices I usually follow in my code. It can gather this information from the code I write and from the snippets I have saved in Pieces. 

By asking Pieces, "What are the best security practices I usually follow?" and using that response with Q Developer’s native security scanning, I was able to prevent common security vulnerabilities.

I personally use a lot of AI tools for my day-to-day work and have seen how significantly it has helped me learn and build new things. 

The example above is proof of how you can pick and choose the hero features of the AI tools and use them to get the most effective solution for yourself.

Feature comparison: Copilot vs. CodeWhisperer

Both the tools are good on its own, but as a developer, here are the feature differences that I found: 

Note: These differences are based on personal experience, community opinions on Copilot vs. CodeWhisperer, and from discussions on Reddit.

When to choose Copilot vs CodeWhisperer

I hope the detailed breakdown has helped you understand both products better. In this section, we will look at the use cases of both Copilot and CodeWhisperer and when to choose them. 

When to choose GitHub Copilot

GitHub Copilot is great for general-purpose coding. Whether it's for auto-completing code or providing suggestions, one thing that stands out for GitHub Copilot is the ability to choose from multiple models and use your existing files as context for more accurate results.

It is best suited for:

• General development across multiple languages and frameworks.

• GitHub related workflows and documentation related work. It can help you document your code, write README, write comments on your code, and even commit messages for every PR you raise.

• Code related research in natural language. It has multiple keywords, variables (#) and slash (/) commands that can help you get answers to your queries faster.

Considering the capabilities of Copilot, one might ask, is there anything better than Copilot for certain tasks? This depends on your focus, and below are some areas where Code Whisperer works the best.

When to choose CodeWhisperer (Q Developer)

Amazon CodeWhisperer, like Pieces Copilot, can be used for general-purpose coding, but it stands out for AWS-centric development support and security-related analysis.

It is best suited for:

• Developers who are focused on AWS and use AWS Lambda, S3, DynamoDB, and other AWS services in their day-to-day tasks.

• Teams who need assistance with serverless applications and cloud infrastructure.

• Developers who focus on security analysis and would benefit from features like vulnerability detection.

Final thoughts

• If your day-to-day tasks include general purpose coding, then Pieces is a great choice.

• If you are more involved in AWS related development, then CodeWhisperer (Q Developer) is a better choice.

Conclusion

As we come to the end of this article, I hope it was helpful for you to get a deep dive into both AI tools. They not only have features that can assist you differently across your development workflow but can also help you in different use cases. For example, GitHub Copilot is better for general development scenarios, while CodeWhisperer is better for AWS-centric development.

Now coming to a burning question that you might have, “Is Copilot better than CodeWhisperer?”

If I had to choose a tool, I would decide based on what I want to achieve. If my purpose is general coding, I would prefer using GitHub Copilot and integrating it with Pieces in VSCode (my IDE of choice) to make the best use of both.

By using Pieces’ features like snippet saving, code collaboration, and long-term memory, I would use it to reduce my context switching and then use the outputs from this in GitHub Copilot to write code that is more tailored to my specific needs.

If my purpose is cloud infrastructure-related, I would make use of CodeWhisperer, while also using Pieces as an AI second brain.

All in all, both tools are great and have their own perks. The choice should depend on what you want from a tool and also the task at hand. Below are a few resources that can help you make the most out of both AI tools:

• Q Developer features that can help you build

• Exploring Microsoft’s Phi-3-Mini and its integration with tools like Ollama and Pieces

• Everything a dev community should know about using Pieces in Cursor

This article was first published on March 27th, 2024, and was improved by Haimnatika Mitra as of March 5th, 2025, to improve your experience and share the latest information.