Augment Code vs GitHub Copilot: Which is The Future of AI-Powered Code Generation?

AI Developer Tools

With the rapid advancements in large language models (LLMs) over the past few years, we’ve seen a surge in AI-powered developer tools. Since code is essentially text, it’s an ideal use case for LLMs. While there are many tools available to assist with software development, let’s focus on the most impactful ones: code completion and code manipulation tools.

The most well-known tool in this space is GitHub Copilot, which gained immense popularity as one of the first to hit the market. However, there are now several new contenders, including AI code editors like Cursor, Supermaven, and the newly released Augment Code. In this article, we’ll explore and compare Augment Code with GitHub Copilot.

GitHub Copilot

At profiq, we started using GitHub Copilot in 2022, with a company-wide rollout in 2023. When the first version of ChatGPT launched, in the dev world, it was quickly dubbed the “Stack Overflow killer.” However, users still had to switch from their IDE to a browser, which wasn’t ideal. Bringing this functionality directly into the IDE was a game changer. Inline code completion has since become something I can’t imagine working without.

Side Note: If you haven’t tried an IDE AI code completion tool yet, you need to give it a go. You might be hesitant, especially if you’ve used ChatGPT and found its code generation lacking or on par with Stack Overflow results. But trust me, choose one of the tools I’ve mentioned, install it, and use it for a few days. Then, try disabling it—you’ll quickly realize just how helpful those completions are. Even when working with less common languages, these tools can still offer valuable assistance.

It’s not just about inline completions—GitHub Copilot can also generate large chunks of code within your files. Plus, with its chat interface, you can make edits and rewrites effortlessly, which is incredibly useful.

However, when using Copilot to generate larger sections of code, you may find that not all of it is immediately usable, especially in new files. For instance, it might often suggest code using a popular library A, but your project relies on a different one (Library B) for the same functionality. In more flexible languages like JavaScript, where there are countless ways to accomplish the same task, this issue becomes even more pronounced.

Simply put, the challenge you may encounter is the AI tool’s “lack of context.”

The Context

When generating new code, understanding your codebase—including the libraries, coding style, and architectural approach—is crucial to ensuring the generated code is usable.

With GitHub Copilot, you can provide some context, for example, through the chat interface, open files, or other methods. While this can result in better code that aligns more closely with your project, the process remains fairly manual and, at times, tedious.

This is where newer tools like Augment Code shine. You don’t need to explicitly specify additional files or context; Augment automatically takes your entire project into account. As a result, the code completions are far more aligned with your specific setup, significantly reducing the need to modify AI-generated code.

At profiq we have been using Augment Code for the past several months to evaluate it across various teams and I used it personally as well.

Usage of the tool

Using Augment is quite similar to GitHub Copilot. It offers extensions for your favorite IDEs (currently VSC and JetBrains tools). After installing the extension, simply log into your account and allow a moment for Augment to scan and index your project. Shortly after, you’ll start seeing your first code completions. The experience is similar to Copilot, but with few differences:

Context

As mentioned earlier, the code completions are far more accurate, significantly boosting developer velocity.

Speed

The speed of completions is on another level. I hadn’t realized GitHub Copilot was slow until I used Augment—it’s noticeably 2-3 times faster in many cases.

IDE Integration

Currently, Augment plugins support inline completions and a chat interface, allowing you to engage with AI over your code or instruct it to make more intricate changes. However this is one area where GitHub Copilot has an edge in terms of IDE integration. For instance, Copilot offers direct buttons for tasks like generating commit messages or explaining errors or code within the IDE. Moreover, GitHub Copilot’s integration extends beyond IDEs—recently, we’ve seen new features directly embedded into GitHub itself (such as in pull requests), which could greatly enhance the overall developer experience.

Quote from Tomas – Software Engineer at profiq – Healthcare product
When I began refactoring our large end-to-end test suite from Cypress to Playwright, I had the opportunity to use the Augment code assistant, and it made a remarkable difference. What truly impressed me was not only the accuracy of its suggestions but how it adapted to my specific testing approaches and understood the context of our extensive codebase.

What really stood out was how accurately it suggested test IDs for components, even in places I wouldn’t have expected. This level of precision saved me a lot of time.

What initially felt like a month-long task was completed in just a few weeks, thanks to how efficiently Augment helped me navigate through the more challenging parts.

Security aspect

The codebases developers work on are often highly valuable assets. When using AI tools, it’s important to recognize that they may be sending your code to third parties. You might recall incidents from 2023 when GitHub Copilot suggested working API keys.

This highlights the importance of providing thorough training to your engineers and conducting proper due diligence when evaluating the privacy settings of any AI tools you introduce into your organization.

We were pleasantly surprised by Augment’s approach to security. There are no settings or switches you need to enable to boost security—your data is safe by default, never shared or used for training. Another standout feature is tenant separation, ensuring your data is fully isolated. On top of that, Augment offers Proof-of-Possession API Authorization, meaning even developers within your tenant can’t access files unless they already have them on their local machine. The icing on the cake is their SOC 2 Type II certification and other compliance reports. You can read more about Augment’s security practices here.

In the fast-evolving world of AI tools, security often takes a back seat, but with Augment, it feels like one of the core pillars of their product. That’s why, at profiq, we’ve only approved two AI coding tools for production use so far—GitHub Copilot and Augment Code.

Is it worth investing in AI developer tools?

Let’s consider a simple calculation. In an ideal scenario, you or your developers spend around 25 hours a week coding. While measuring productivity gains can be tricky, even a modest estimate of a 20% improvement from using AI tools translates to saving 5 hours per week. That’s significant. Multiply those savings across your entire organization, and the financial impact becomes substantial.

Investing in these tools, whether for personal use or deploying them company-wide, should definitely be on your radar.

If you’re in a leadership position, Augment offers a valuable feature: anonymized usage statistics. This allows you to track how frequently your teams are using the tools, assess how much they are benefiting from them, and determine whether the investment is paying off.

Side Note: If you’re in a leadership role, it’s crucial to implement AI coding tools alongside clear guidelines and best practices for their use. While these tools can significantly boost team productivity, you also need to consider potential downsides—such as low-quality code, code bias, increased bugs, or added pressure on PR reviews due to higher code output. These factors are important to manage, but they’re also a topic worth exploring in a separate article.

Final comparison

GitHub Copilot has had a strong head start, offering polished plugins with more features and broader IDE support. Additionally, its chat interface excels at casual “conversation,” making it great for brainstorming ideas or explaining bugs. However, when dealing with more complex codebases, Copilot’s code generation often falls short. You frequently need to provide extensive instructions and context through the chat interface to get results that truly align with your project.

This is where Augment Code truly excels. One could say, “Context is king,” and Augment proves it. While its completions aren’t always perfect, they consistently align more closely with your project compared to Copilot’s output—often without the need to manually input additional context via a chat interface.

During our testing, we did encounter a few hiccups—occasionally, completions wouldn’t appear for a specific file, or we had to restart the IDE to get the plugin running again. However, as a “beta” customer, these minor issues were expected.

The AI developer tools landscape is evolving at an incredible pace. It’s entirely possible that Copilot will catch up to these newer tools, and I’m excited to see what future innovations it may bring.

A few months ago, I would have said, “I can’t imagine coding without Copilot.” Today, I find myself saying, “I can’t imagine coding without a context-aware AI tool like Augment Code.”