GitHub Copilot vs Amazon CodeWhisperer | Who's Best in 2024?

In this article, we explore the question, GitHub Copilot vs Amazon CodeWhisper, which is the best AI coding assistant in 2024? The short answer is they’re both excellent, but it comes down to your development style, budget, and preferred language.

To help us answer this question properly, we’ve taken a deep dive into each AI coding assistant, including key features, language support, IDE support, data privacy, and, of course, pricing. 

Plus, if you’re new to the idea of an AI coding assistant and you’re not sure why you need to use one, we’ve also tackled these questions, including a breakdown of the benefits of AI coding assistants for students, beginner developers, and experienced pros.

So, if you’re ready, let’s dive in!

What Is An AI Coding Assistant?

If you’re brand new to the world of AI coding assistants and you’re not totally sure what we’re talking about, start here! 

There’s no doubt that 2024 continues to be the year of AI, with close to 80% of developers in favor of AI tools and more than 40% using AI tools in their daily development duties. Wow.

But what is an AI coding assistant? Well, it's an AI-powered tool that’s been designed to help you write, review, debug, and optimize code.

But where did they come from? Great question. The short answer is that they are natural evolutions of the advances we’ve seen in machine learning, large language models, and natural language processing (NLP). 

Sure, it’s great that we can all access ChatGPT, Google Bard, and even Meta’s new AI glasses, but when it comes to software development, AI coding assistants are the product of this digital revolution.

So whether you’re just starting out or a development professional with lots of experience, chances are that you will be seeing a lot more about AI coding assistants. 

AI coding assistants are also a subtype of the broader array of AI development tools, which might include tools for testing and documentation.

Let’s take a look at some of the main features you’d expect from an AI coding assistant:

• Code Suggestions: Unlike an IDE’s auto-complete feature, an AI coding assistant can predict and suggest the next line of code or offer completions based on the context of what you’ve already written. 
• Code Reviews: One of their greatest strengths is the ability to spot issues, bugs, or deviations from best practices while also suggesting fixes.
• Bug Detection: Every developer is prone to adding bugs to their code, which is why these tools are excellent at finding subtle bugs that might go overlooked during reviews.
• Code Optimization: AI coding assistants are ideal for optimizing code snippets for better performance or memory usage - yes, we’re talking better Big-O performance.
• Refactoring: AI assistants can offer refactoring suggestions to improve your code’s maintainability and structure.
• Natural Language Queries: This is when you ask the AI how to implement a particular code feature with a normal sentence. If you’ve spent any time asking for development advice from ChatGPT, this will be familiar. 
• Documentation Assistance: AI coding assistants are great for generating or updating documentation after making changes.
• Learning and Tutorials: If you’re new to coding, AI assistants can offer a personalized learning experience. Think of them as a personal tutor for your coding tasks with real-time feedback.
• Integration with Development Environments: Whether you’re using one of the best Python IDEs or a super popular editor like VSCode, many AI coding assistants integrate directly.

Why Use An AI Coding Assistant?

We like to think that AI coding assistants can be helpful for all developers, regardless of experience or skill level, to complement traditional learning and coding practices.

Important note: We strongly believe these AI tools will not replace coders or remove the need to learn how to code. And while the temptation can be great to rely on AI to do the work for you, it can be a slippery slope!  

That said, let’s consider this question from the point of view of a student, beginner developer, and professional developer:

Students: Whether in high school, college, or perhaps you have a child you’re teaching to code, AI coding assistants can be helpful for:

• Accelerated Learning: Instant feedback and suggestions can help you learn faster.
• Homework & Project Assistance: AI assistants can offer guidance to make sure you don't get stuck with challenging assignments.
• Concept Clarification: You can ask for explanations of how code works.
• Building Confidence: Positive reinforcement and corrections can help students feel more confident about coding.

Beginner Developers: Perhaps you’ve been coding for a short period of time, or you’re starting a role as a junior developer role after taking a Python course, in either case, AI coding assistants can help with:

• Code Quality: AI assistants can help guide beginners to write cleaner, more efficient, and maintainable code.
• Productivity Boost: You can ask AI coding assistants directly for help rather than heading over to Google or Stack Overflow to find help.
• Familiarity with Multiple Languages: Many AI assistants support multiple languages, which is great for learning additional languages to broaden your skill set.

Professional Developers: If you’ve been coding for a while, you’d probably class yourself as a pro developer. You might also be on the fence about these new tools, which we completely understand.

Overall, we like to think of these tools as AI-powered interns who are there to help us in the following areas:

• Speed and Efficiency: Real-time suggestions allow pros to code faster.
• Code Review: AI assistants can quickly highlight potential issues.
• Bug Detection: Even seasoned devs make errors. and AI assistants can quickly find common or uncommon bugs.
• Reduced Boilerplate: For repetitive tasks, you can use AI to create boilerplate code that allows you to focus on the more complex challenges.
• Refactoring Assistance: AI tools can be great for refactoring to improve your code’s maintainability.
• Up-to-date Best Practices: An AI coding assistant can help professionals stay current as best practices evolve.

So now you know what an AI coding assistant is and how it can help you in your development, let’s take a look at two of the most popular options available right now in GitHub Copilot and Amazon CodeWhisperer.

What Is GitHub Copilot?

Introduced in 2021 and born out of a collaboration between GitHub, OpenAI, and Microsoft, GitHub Copilot was the first mainstream AI coding assistant, which is why it’s currently the most popular AI coding assistant in 2024.

Let's dive into what GitHub Copilot offers, its rise to popularity, and the differences between the free, individual, and business plans.

Understanding GitHub Copilot

Origins and Development

GitHub Copilot is built atop OpenAI's Codex model. Released in June 2021 as a limited beta, it quickly gained traction among 1.2 million users, many of whom saw it as a transformative tool in their coding arsenal when learning how to use Copilot. 

Functionality

More than just an autocomplete tool, Copilot can provide entire lines or blocks of code based on the context it’s provided. Trained on billions of lines from public repositories, it offers educated code suggestions, enhancing developer efficiency.

All you need to do is start writing the code you want to use or write a natural language comment that describes your goals, and Copilot can analyze the context to offer suggestions.

So whether you’re trying to figure out how to use a tricky Python operator or you need help writing an algorithm, Copilot can offer help.

Multilingual Support

At the time of writing, Copilot has been optimized for popular languages like Python, JavaScript, TypeScript, Ruby, Go, C#, and C++. 

That said, Copilot can generate code for almost any programming language or framework as long as there’s a public repository on GitHub that uses it (this is where Copilot gets its training data).

Interaction

Whether you're in the middle of writing code or jotting down a comment about what the code should do, Copilot jumps in with contextually relevant suggestions. This is what you’d expect from something that’s billed as an AI pair programmer!

There’s also a beta Chat feature (more on that soon) that offers direct interaction to enhance your AI coding experience. Yep, your very own chatbot to help when you code.

Compatibility

Given its popularity, it’s not a surprise that it offers integration with various popular IDEs and code editors via plugins for Visual Studio Code, Visual Studio, JetBrains IDEs (IntelliJ IDEA, PyCharm) suite, Vim, Neovim, and Azure Data Studio, providing flexibility for developers.

Training Data

One of the most interesting things about Copilot I found when reading the docs is that it’s been trained on public GitHub repositories. 

Because of this, newer or more niche languages that suffer from a lack of public code examples are less likely to be supported by Copilot. Equally, the quality of the suggestions may not be on par with Python or JavaScript.

Here’s a great example. If you’re working on a new Python project, you’ll probably get much better suggestions than you would if you were writing code in something like Fortran (depending on how young you are, you may not know what that is!), as this features much less on GitHub.

So, while being trained on public repositories means there is a vast pool of data to draw suggestions from, you have to consider the popularity of your language.

Plus, having been trained on open-source code of varying quality and accuracy, you also need to bear in mind that any suggestions might include outdated coding patterns, bugs, or references to outdated APIs.

In fact, GitHub stats indicate that only 26% of Copilot's suggestions are accepted. The main takeaway here is to always check the code suggestions, don’t take them on faith! This is exactly why it’s important to remember that the tools are assistants, not replacements for developers.

Data Security

Copilot’s commitment to data privacy is fairly clear when you consider that data is encrypted in transit and at rest. Plus, your code won't be shared with other Copilot users.

That said, if you’re hyper-security conscious, it’s important to know that GitHub and Microsoft personnel can access the data, making it important for users to be aware of the security implications.

There’s also some variance in data retention for individual and business users, with prompts and suggestions being retained for individuals unless you turn off code snippet collection in your settings. 

If you don’t like the idea of your data from prompts and suggestions being retained and associated with your GitHub account, you can contact GitHub Support to have it deleted.

Business users don’t have to worry about this, as Copilot does not retain any prompts or suggestions.

Individual vs. Business: Which Copilot?

Depending on whether you’re a beginner, student, or professional coder, you might want to weigh the differences between the individual and business plans for a Copilot paid subscription. 

Whichever you choose to go with, you do have the option of a free trial when starting out, but let’s take a closer look at these paid subscriptions.

Stand-Out Features: Copilot Chat

One of the newest features of Copilot is an integrated chatbot tool called GitHub Copilot Chat.

At the time of writing, this is currently in beta and only supported in Visual Studio Code and Visual Studio, but with this handy feature, you get direct interaction with an LLM in the same format as you’d expect with ChatGPT or Google Bard.

From generating unit tests and explaining code to suggesting bug fixes, Copilot Chat covers a wide range of tasks. However, like any tool, it has its limitations, like potential biases. More importantly, it can also lead to inaccurate code suggestions.

This is probably the key limitation you need to consider, as there are times when it can generate code that appears to be valid, but when you look more closely, it might be semantically or syntactically incorrect. 

Again, the key takeaway is to review any code suggestions!

Given their partnership with OpenAI, I'd also be curious to see if there are any plans to integrate your own GPT with chat in the future, as this could be a cool way to personalize your CoPilot chat experience.

GitHub Copilot Summary

With features like code suggestions, auto-completion, documentation insight, and support for multiple languages, Copilot offers everything you’d expect from an AI coding assistant.

Being so popular also means that in addition to excellent documentation, there is a lot of community support for new and experienced users.

You have the option of writing code or natural language comments that describe your goals to get code suggestions. There’s even a Chat beta feature that you can use to interact directly with Copilot.

Interestingly, Copilot was trained on public GitHub repositories. While this means it was trained on a vast dataset, stats indicate that only 26% of suggestions are accepted.

The main crux here is that newer or niche languages suffer from a lack of public code examples, meaning less support than major languages like Python, where it excels at pointing out your Python mistakes.

Copilot also offers data privacy and encryption, which means your code won't be shared with other Copilot users. 

Then again, if you’re security conscious and an individual user, consider whether you want to tweak your settings to prevent GitHub from retaining your prompts and suggestions. 

This is the first thing I switched off when using Copilot, as I’d prefer to avoid any potential headaches with data leakages.

What Is Amazon CodeWhisperer?

As one of the newest AI coding assistants, Amazon CodeWhisperer is one of the most desired AI tools among developers on the latest Stack Overflow developer survey. 

Let's take a closer look at what Amazon CodeWhisperer offers, including the differences between the free (individual) and professional tiers.

Understanding Amazon CodeWhisperer

Origins and Development

Amazon announced the arrival of CodeWhisperer in June 2022, a day after the public launch of Copilot. 

This strategic release, likely influenced by Copilot's transition from beta, was a testament to Amazon's commitment to supporting developers with an advanced AI-driven coding assistant.

In fact, CodeWhisperer is touted as an AI pair programmer that can accelerate software development by offering real-time, tailored code suggestions.  

Functionality

CodeWhisperer is designed to generate real-time, contextually relevant code suggestions straight inside your IDE. It can also help with comment completion, which is great for speeding up documentation.

Plus, being an Amazon product, it’s also optimized for AWS, offering code suggestions for AWS APIs that align with AWS best practices (more on that soon!).

In fact, while running their product preview phase, Amazon found participants were 27% more likely to complete tasks successfully while also being 57% faster than those who were not using CodeWhisperer. Wow, that’s quite a stat!

That said, it’s always really important to review all code suggestions before accepting them, and you should also be prepared to edit the code to ensure it functions as you intended. Remember, it’s an assistant, not a replacement!

CodeWhisperer also places a strong emphasis on code security, with a built-in security scanner that can detect hard-to-find issues, including those in the top 10 Open Worldwide Application Security Project (OWASP).

This feature is currently only available for VS Code and JetBrains IDEs with Python, Java, and JavaScript, but we’re fairly certain it will be rolled out more broadly over time.

Multilingual Support

While CodeWhisperer can’t claim to support as many languages as Copilot, it does boast an impressive range of compatible languages that cater to a wide range of developers in some of the most popular languages. 

This includes support for Python, Java, JavaScript, TypeScript, C#, Rust, Go, Ruby, Scala, Kotlin, PHP, C, C++, Shell Scripting, and SQL.

Interaction

By analyzing natural language code comments or partially completed code blocks, CodeWhisperer can intelligently infer and suggest code snippets to help you complete your task, including entire functions or logical blocks that match your coding style.

That said, to get the most out of CodeWhisperer, it’s essential to use short comments that map to small and discrete tasks. It’s also essential to use intuitive naming conventions for variables and function names. But any good developer worth their salt should be doing this anyway!

Another great feature, thanks to its links with AWS, is that you can write comments to ask for help in creating code that interacts with AWS APIs, which can be helpful if you’re new to AWS or cloud in general.

Compatibility

As you’d likely expect, CodeWhisperer is compatible with an extensive list of IDEs, including JetBrains IDEs (IntelliJ IDEA, PyCharm), Visual Studio Code, AWS Cloud9, AWS Lambda console, JupyterLab and Amazon SageMaker Studio.

Plus, if you’re using a Python, JavaScript, or Java IDE, you get access to the security scans.

Training Data

Much like Copilot, Amazon CodeWhisperer was trained on billions of lines of code, both publicly available and from Amazon. 

Amazon’s documentation claims that the AI engine has been designed to provide tailored code suggestions without biases, although, like any generative AI, this is harder to achieve than it sounds.

One feature that we really like is the built-in reference tracker that flags code suggestions that closely match the open-source training data. This allows you to use the code as is, modify it, or even discard it.

Plus, you can then review these flagged cases later, allowing you to add license attributions - ideal for avoiding code plagiarism! You can also opt to filter out all code suggestions that CodeWhisperer determines to resemble open-source code, which is a nice touch.

Data Security

As we mentioned when discussing the built-in vulnerability scanner, CodeWhisperer places a big emphasis on data security. 

It’s no surprise then to see that code snippets, comments, and other data are transmitted with TLS to ensure secure communication between your IDE and CodeWhisperer. And, like Copilot, CodeWhispereer ensures encryption for data that’s in transit and at rest.

That said, individual users who use the free tier should know that their code snippets and comments may be retained for service enhancement, although you can opt out of this.

On the other hand, professional users have nothing to worry about here, as this data is neither stored nor used for training or service improvement. However, you do have to opt out of telemetry data collection on the professional tier if this is also something you want to avoid.

Individual vs. Professional: Which Tier?

The choice of whether to opt for the free individual or paid professional tiers of CodeWhisperer is naturally a personal one. 

That said, whether you’re a professional developer, a beginner, or maybe even a student, it makes sense to understand the differences between the two, so let’s take a closer look.

Stand-Out Features: AWS Support

Perhaps the biggest stand-out feature of CodeWhisperer is that it’s heavily optimized for AWS development, including real-time code suggestions for developing with AWS APIs.

But what does this mean exactly? Well, CodeWhisperer can offer code suggestions for AWS APIs like Amazon EC2, AWS Lambda, and Amazon S3, which is ideal if you’re someone who currently builds or plans to build apps on AWS.

I, for one, would have been very intrigued by CodeWhisperer’s code suggestions for AWS APIS when building AWS applications in the past!

Regardless of your current level of cloud proficiency, this type of support can be a huge plus for your implementation of AWS best practices within your code. 

Remember, though, the idea here is not to remove the need to understand how to code with AWS APIs but to supplement and streamline your workflow. That said, if you are new to AWS services, it can be a helpful way to supplement your learning.

New Feature: Integration With Amazon Q

Fresh off the latest AWS re:Invent conference, Amazon announced a new contender for the generative AI and LLM market with Amazon Q.

But what is it?

Well, to my mind, the best way to describe it is as a ChatGPT-style tool for AWS designed for business use and to help cloud professionals work with AWS.

For our purposes as developers, the part we're interested in is the fact that it's been optimized to help us work within AWS.

This is possible because it's powered by more than 17 years of AWS knowledge and experience in cloud-building.

And most importantly, it's being integrated with their AI coding assistant, Amazon CodeWhisperer!

Yes! That's right, we'll also be getting a chat interface that we can use inside our own IDE.

This is pretty huge, and for me, it makes CodeWhisperer an even more formidable contender for GitHub Copilot and its integrated chat feature!

It's still early days for Amazon Q, as it's only in preview mode, but I'm really looking forward to playing this with more.

Amazon CodeWhisperer Summary

With the options for code completion or comment interpretation, CodeWhisperer can offer real-time code suggestions for entire functions or logical blocks that match your coding style. It even takes care of comment completions to speed up documentation!

There’s also the added benefit of a built-in security feature that scans your code for vulnerabilities, assuming you’re working in a compatible language and IDE.

We also really like the reference tracker that flags code suggestions that closely match open-source training data, allowing you to avoid plagiarism or to offer licensing credit. 

As you’d expect, you get support for the most popular languages, including Python, Java, C++, and others, not to mention integration with the usual suspects for IDEs like Visual Studio Code, the JetBrains IDE suite, AWS Cloud9, and more.

CodeWhisperer also offers privacy and encryption for data, including zero storage of content on the professional tier. However, you’ll need to opt-out on the free individual tier to get the same level of data protection.

Plus, being an Amazon product, it’s optimized for AWS, including suggestions for AWS APIs. Not to mention the latest announcement of Amazon Q and the future integration of a chat interface within your IDE.

GitHub Copilot vs Amazon CodeWhisperer

If you’ve made it this far, you should know the key details about these AI coding assistants, but which one should you choose?

Well, when it comes to choosing the best AI coding assistant, we like to compare key criteria. Let’s dive into the key differences to see if anything tips the scales! 

As you can see, there’s not a lot to pick between the two! 

When it comes to pricing, only CodeWhisperer is totally free, whereas Copilot only offers a free option to students and educators. 

Then again, they have equal pricing for professional plans, so there’s nothing to separate them there. 

This leads to the major point that you’re always going to be better off by choosing the option that suits your development needs.

For instance, Copilot is better for niche languages that are not supported by CodeWhisperer,  assuming it features in the GitHub repository ecosystem. 

Equally, if you plan to develop apps on AWS, Codewhisperer is the obvious choice, thanks to its coding suggestions for AWS APIs. 

And this provides a natural segue to the main differentiator here - the stand-out features.

Look at it like this. If you don’t need AWS support and are happy to pay for a subscription, Copilot might have the edge with its ChatGPT-style chatbot that you can use to ask questions within your IDE.

Then again, if you don’t want to pay for a Copilot personal plan to get this feature, you can use CodeWhisperer for free. You’ll then get the added bonus of 50 security scans per month to check your code for vulnerabilities (although this is language and IDE-dependent). 

Plus, CodeWhisperer will soon include integration with Amazon Q, which means you'll also get a chat interface that you can use to ask AWS-related questions.

The bottom line is that if you code in any of the major languages and you’re looking for the general features of an AI coding assistant, either option will be great!

We’d also recommend you try both to see if you prefer the user experience in one or the other. We also think it’s a great idea to put them through their paces for the same coding tasks to see if either tool offers up more accurate suggestions for your development style.

Remember, neither of these tools will be 100% accurate, but you might find that one has a better accuracy rating for your development area. 

There’s no doubt that Copilot has the edge in popularity right now, but that’s most likely down to the fact it’s been available for longer. But with more and more developers expecting to use AI tools in the future, we’d be really curious to see how this evolves.

Speaking from personal experience, I would have been intrigued by CodeWhisperer’s code suggestions for AWS APIS when building AWS applications, and perhaps this will be one of the major catalysts for Copilot users to make the switch.

Wrapping Up

We’ve now explored the question of GitHub Copilot vs Amazon CodeWhisper to help you figure out which is the best AI coding assistant for you in 2024. 

If you want the TL-DR, they’re both excellent AI coding assistants with many of the same strong features, but the ultimate decision really needs to come down to your development style, budget, preferred language, and most valued features.

And while GitHub Copilot is the most popular choice among developers right now, it's really not that simple.

If you want a totally free option, CodeWhisperer is the way to go. Then again, if you want a chatbot feature, you can now choose between GitHub Copilot and CodeWhisperer with the added integration of Amazon Q. Equally, if you want to develop AWS apps, again, CodeWhisperer makes the most sense.

You can see where this is going! The right choice comes down to your needs. 

Ultimately, if you code in a popular language and want the general features of an AI coding assistant, either option will be a great addition to your workflow. That said, we think it’s really important to give them both a try to see if one really resonates with you.

Whichever AI coding assistant you choose to add to your workflow, we hope you’ve found this article helpful.

Happy coding!

Are you new to development and ready to use an AI coding assistant as your personal tutor? Check out:

Dr. Anglea Yu's 100 Days of Code: The Complete Python Bootcamp for 2024

Frequently Asked Questions

1. What Are AI Code Assistants?

An AI coding assistant is an AI-powered tool designed to help you write, review, debug, and optimize code. AI coding assistants are also a subset of the broader category of AI development tools.

2. Will AI Replace Programmers?

In our opinion, AI will not replace programmers but will continue to be one of the most important technologies that developers will need to work in harmony with.

This includes using AI coding assistants to enhance productivity and free up time for complex programming challenges that are beyond the scope of AI. That said, the democratization of AI also means that programmers need to work hard to develop their skills to remain competitive.

3. Which Is The Best AI Coding Assistant?

This depends on several factors like your preferred coding language, favorite IDE, and data privacy requirements. If you’re looking for the most popular AI assistant today, this is probably GitHib CoPilot, but we’d highly recommend considering CodeWhisperer.

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