Engine and GitHub Copilot are two AI-powered tools designed to make software development faster, easier, and more reliable. Although they operate differently, many teams find that using both in tandem can boost productivity, reduce errors, and streamline workflows. This article explains what GitHub Copilot is, how much GitHub Copilot costs, and whether it’s worth using—then explores how Engine compares and why combining them can be a powerful strategy.
GitHub Copilot is an AI pair programmer that provides real-time code suggestions in your integrated development environment (IDE). Created by GitHub and powered by OpenAI, it’s sometimes called “Copilot GitHub” or simply “Copilot.” Whether you’re curious about what is GitHub Copilot, how to get started, or its integration with editors like IntelliJ and Xcode, the core idea remains the same: GitHub Copilot scans your existing code context and suggests lines, functions, or even entire blocks of code on the fly.
Copilot works best in popular IDEs like Visual Studio Code, JetBrains IntelliJ, and Xcode. By analyzing the context around your cursor, it predicts what you need next—this is also the foundation of GitHub Copilot Chat, which can offer more conversational suggestions.
Getting started is straightforward. After installing the GitHub Copilot extension for your IDE, you simply sign in with your GitHub account. As you type, GitHub Copilot automatically provides suggestions. You can accept them with a simple keystroke or tweak them for your needs. For beginners, it’s like having a coding tutor beside you; for experienced devs, it’s a shortcut to boilerplate or repetitive code.
If you want more advanced features, you can explore GitHub Copilot CLI for command-line code generation, or follow a GitHub Copilot tutorial to dive deeper into specialized workflows. The tool keeps evolving, so expect new capabilities under the “GitHub Copilot X” banner, which hints at future expansions like voice-driven coding and advanced debugging.
One of the most common questions is “Is GitHub Copilot free?” The answer depends on your plan. There's a free version of copilot that is targeted at more casual users with lower usage limits for AI completion and chat requests.
For commercial users, GitHub Copilot pricing typically runs on a monthly subscription. If you’re wondering how much is GitHub Copilot, the standard plan starts at around $10 per month for individuals, while enterprise plans cost more, depending on the size of your team and the features you need, up to $39 per user.
If you ever decide you don’t want the service, you can cancel GitHub Copilot at any time through your GitHub account settings. Students and open source maintainers may qualify for free usage, so be sure to check if you’re eligible.
Larger organizations often explore GitHub Copilot Enterprise or GitHub Copilot business to streamline their teams’ workflows. Enterprise plans offer more security features and can integrate with corporate identity systems. Some businesses pair Copilot with other AI solutions—like Engine—to handle more advanced tasks, such as multi-repository refactoring and complex project management.
GitHub Copilot workspace is an agentic development environment, similar in concept to Devin. It's tightly integrated in GitHub version control and workflows and it a bit like a hosted, more autonomous version of the IDE copilot. One of it's benefits is that you do not need to be at your computer with an IDE to use it as it runs in the cloud.
GitHub Copilot vs. Microsoft Copilot can get confusing. Microsoft Copilot can refer to different AI-driven features across Microsoft 365 products like office and Excel, so it’s slightly different from the code-focused GitHub Copilot. Another frequent comparison is GitHub Copilot vs. ChatGPT. While ChatGPT excels at conversation and research, GitHub Copilot is deeply integrated into coding workflows. Each has distinct strengths; they can complement each other if you want more conversational coding alongside real-time in-editor suggestions.
Now, how does GitHub Copilot compare to Engine? Copilot acts like a coding partner within your IDE, suggesting lines and snippets in real time. Engine, on the other hand, behaves more like a full junior developer—planning tasks, making pull requests, and integrating with your existing project management software. It can tackle multi-repo architectures with minimal back-and-forth.
Some teams see Engine as a GitHub Copilot alternative, but you can also use them together. Copilot can handle immediate code completions, while Engine takes on bigger tasks that require advanced reasoning, operating more like a junior employee. Think of Copilot as the quick helper for everyday coding and Engine as a new software engineering team mate.
Both solutions have a place in modern development pipelines. Copilot’s real-time suggestions can accelerate daily coding, while Engine’s autonomous approach helps with large-scale refactoring or multi-step tasks that Copilot might not handle gracefully. Engine is a better fit for clearing your backlog automatically.
Many developers ask, “Is GitHub Copilot worth it?” or look for a GitHub Copilot review before committing. If you value instant suggestions, inline completions, and a fast feedback loop, the answer is often yes. Meanwhile, businesses with strict security needs or complex workflows might pair Copilot with Engine, or even consider Engine alone. For many, though, the sweet spot is using both to cover everything from quick code snippet generation to robust, multi-step project tasks.
Each platform provides unique benefits. Engine offers planning, autonomous pull requests creation, and deep workflow integrations, while GitHub Copilot focuses on real-time coding suggestions and straightforward pricing tiers.
In the end, you don’t have to choose between them. Using both GitHub Copilot and Engine can give you the best of both worlds: lightning-fast code completions for day-to-day tasks, plus a sophisticated autonomous AI developer with an extra layer of quality control. Whichever route you take—Copilot, Engine, or both—the future of AI-driven coding holds enormous potential to transform the developer experience.