GitHub Copilot and Amazon Q Developer: Which Fits Your Coding Work?
GitHub Copilot and Amazon Q Developer both offer AI-assisted coding in your IDE, but they aim at different workflows. Here is how they compare on editor support, security scanning and cloud integration.
Updated 7 min read
GitHub Copilot and Amazon Q Developer are both AI coding assistants with inline completion, a chat interface and support for many programming languages. The key difference is where each one focuses. Copilot integrates with GitHub and works across a wide range of editors on any stack. Amazon Q Developer is built for developers working within AWS, with native console access, CDK support and built-in security scanning. Tools like fenbs let your team record its project tasks, coding standards and decisions outside any one AI app, so the same context travels with you whether you use one tool, the other, or both.
| Tool | Best fit | Standout feature | AWS integration |
|---|---|---|---|
| GitHub Copilot | Teams on any cloud or none, working within the GitHub ecosystem | Tight pull request and GitHub Actions integration; broad editor support | General AWS knowledge; no native AWS Console or CDK connection |
| Amazon Q Developer | Teams whose daily work involves AWS services, Lambda, CDK or CloudFormation | Built-in security scanning; native AWS Console chat | AWS Console, CDK and CloudFormation suggestions built in |
| fenbs | Recording team tool choices, coding rules and project context that every connected assistant reads | Tasks, Decisions and rules, lessons and Where we left off outside any single IDE or model | Works alongside either tool over MCP |
What each tool does
GitHub Copilot sits in your editor and suggests completions as you type, drawing on the code in your current file and, in some integrations, across your repository. Copilot Chat answers questions, explains code and requests changes in plain language. More recently, the Copilot cloud agent can take on multi-step tasks across a pull request. Some plans let you choose which underlying model powers the suggestions.
Amazon Q Developer was previously known as Amazon CodeWhisperer before rebranding. Like Copilot, it offers real-time completions and a chat assistant in the editor. It also connects directly to the AWS Console, so you can ask questions about your live infrastructure, get help writing IAM policies, or debug CloudFormation errors without leaving the browser. Its security scanning runs across a project and flags potential vulnerabilities in a wide range of languages.
IDE and editor support
Both tools support VS Code and JetBrains IDEs, covering the majority of developers. GitHub Copilot also works in Visual Studio, Neovim and several other editors. Amazon Q Developer supports Visual Studio and AWS Cloud9, and it ships as part of the AWS Toolkit extension. If your team already uses the AWS Toolkit, Q Developer adds to an existing workflow rather than requiring a separate installation.
The useful question is not which editor list is longer, but which editors your team actually uses every day. A team spread across VS Code, Neovim and JetBrains will find Copilot covers the full set more reliably. A team working primarily in IntelliJ IDEA on AWS workloads is well served by either.
Language and framework support
Both tools support a broad range of programming languages including Python, TypeScript, JavaScript, Java, Go, C# and many others. Amazon Q Developer layers on specialized knowledge of AWS SDKs. When your code imports an AWS library, Q Developer can suggest current method signatures and common patterns for that service, which reduces the time you spend checking documentation.
For general-purpose development that is not tied to AWS services, the language support is comparable. Check each tool's documentation for the specific languages they currently cover, as both are updated regularly and the list grows.
Security scanning
Amazon Q Developer includes project-wide security scanning as a built-in feature. It can check an open codebase for a range of vulnerabilities, not just the file you are currently editing. GitHub Copilot provides code analysis features in certain plans; visit the GitHub Copilot documentation (opens in a new tab) to see what scanning is available for your plan today, as the offering evolves.
If your team needs integrated security scanning without a separate tool subscription, Amazon Q Developer has the stronger built-in story. Record any scanning decisions your team agrees on in fenbs — which patterns to treat as errors, which to accept with a comment — so the next developer on the project can find those rules without asking.
AWS Console and infrastructure work
The AWS Console integration is one of the clearest differentiators. Amazon Q Developer runs inside the Console itself, meaning you can ask about the state of a running service, request a policy document or work through a CloudFormation error all from the same browser window where you manage your infrastructure. That removes the context-switch between editor and console.
GitHub Copilot does not have a native AWS Console connection. It has general knowledge of AWS services and can help you write code that uses AWS SDKs, but it operates through your editor, not through AWS itself. For teams that spend most of their time in application code that happens to deploy to AWS, this distinction rarely comes up. For infrastructure engineers, it matters more.
For teams that move between tools — writing code in VS Code with Copilot and then switching to the AWS Console with Q Developer — keeping your project tasks, decisions and Where we left off in fenbs means neither session starts from zero. Read more about keeping your context portable on the AI models topic hub.
Keep project context when you use more than one assistant
Connect your AI tools to fenbs and they all read the same project record before they start. Coding standards go in Decisions and rules. Current tasks and Where we left off travel between sessions, so you never rebuild the project context when you move from one tool to the other. Connect from /integrations.
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Team plans and enterprise access
Both tools offer team and enterprise plans with centralized billing, policy controls and audit features. Visit the GitHub Copilot pricing page (opens in a new tab) and the Amazon Q Developer pricing page (opens in a new tab) for current plan details, including what is included for your team size. Do not rely on a third-party summary for plan limits or features, as both change regularly.
Enterprise plans for both typically add organization-wide policy management and data handling commitments. If your organization has specific requirements around where code is sent for processing, check each vendor's enterprise and security documentation directly.
How to do this with fenbs
Use fenbs to record your team's AI tool choice, the reasons behind it and the standards you agreed on for AI-generated code. Every developer and every connected assistant can then read those rules before working on the project.
- Create a free account and add your project to fenbs.
- Add the current tasks and any notes about the state of the codebase.
- Write your team's coding rules in Decisions and rules: which AI tools are approved, how AI-generated code should be reviewed, and any patterns you want assistants to avoid.
- Connect GitHub Copilot, Amazon Q Developer or both from /integrations. Both connect over MCP with a browser sign-in.
- Before each session, ask your assistant to read the project context and rules. Before it stops, ask it to save progress and any new decisions.
Which fits your workflow?
Choose GitHub Copilot if your team works across a mix of editors and operating systems, is not primarily building on AWS, and wants tight integration with GitHub Pull Requests and the GitHub cloud agent. Its broad editor support and existing GitHub ecosystem connections make it a strong default for teams that are not AWS-first.
Choose Amazon Q Developer if AWS is the center of your daily work. Writing Lambda functions, CDK stacks, CloudFormation templates or spending significant time in the AWS Console makes the native integration and security scanning worth more than they would be to a general-purpose team.
Using both is reasonable for teams with mixed responsibilities. For a broader view of what is available, see GitHub Copilot alternatives or explore how the GitHub Copilot cloud agent handles longer tasks. For advice on coordinating several AI tools, orchestrating coding agents covers the patterns that help.