If you’ve spent time with Amazon Q Developer, you’ve probably noticed one thing — it only really shines when your world revolves around AWS. And that’s fine if your infrastructure, stack, and team are all-in on Amazon’s ecosystem. But if you’re building across multiple clouds or want a coding assistant that adapts to your setup rather than the other way around, you’ll likely want more flexibility.
This post walks through several alternatives to Amazon Q Developer that give you the same (or better) capabilities without the ecosystem lock-in. Let’s start with a quick refresher on what Q actually does.
What Amazon Q Developer Is (and Isn’t)
Amazon Q Developer is AWS’s generative coding assistant designed to help developers build and modernize applications on AWS infrastructure. It offers real-time code suggestions, helps with Java and .NET upgrades, and integrates directly with AWS services. You can ask it about architecture choices, cost optimization, or security best practices, and it’ll respond in the context of your cloud environment.
In other words, it’s the AI layer on top of AWS development — an assistant that understands S3, EC2, IAM, and the rest of the alphabet soup that makes up a typical AWS workload.
Key features
Code suggestions and context-aware chat inside your IDE
Automated refactoring and legacy upgrades for common frameworks
AWS-aware recommendations for cost, security, and performance
Autonomous features that can modify or extend your code
Pricing
Amazon Q offers a free tier that includes 50 agentic requests per month and up to 1,000 transformed lines of code. The Pro tier is $19 per user per month and raises the limit to 4,000 transformed lines per user (pooled at the account level). Each additional line costs $0.003. Enterprise pricing adds governance and AWS integration options.
It’s a capable tool if you live in AWS. But that dependency is exactly where it starts to feel limiting.
Why You Might Want an Alternative
Amazon Q is built for one purpose: making AWS development faster. That focus means trade-offs:
It’s most useful when your code and infrastructure are already running on AWS.
AWS controls the underlying model and routing logic — you can’t swap providers or fine-tune behavior.
Costs can climb quickly when you layer Q on top of other AWS services.
It supports major IDEs, including VS Code, JetBrains, Visual Studio, and Eclipse, as well as deep integration with the AWS Management Console. Earlier versions emphasized Cloud9, but recent updates have brought near-parity across mainstream editors.
Its guidance is often AWS-specific, making it less relevant to developers working across different frameworks or environments.
If you want a tool that fits your workflow rather than your cloud vendor’s roadmap, there are several stronger, more flexible options.
Amp by Sourcegraph
Sourcegraph Amp takes a very different approach. Instead of limiting itself to one ecosystem, Amp is designed to work wherever your code lives. It’s built by the team behind Sourcegraph’s Code Search and Cody, and its goal is to make AI-assisted development collaborative and contextually aware.
Amp doesn’t rely on AWS integration at all. It can orchestrate multiple agents to handle larger tasks, share chat threads across teams, and automatically route requests to whichever model its backend deems most effective for that context (developers don’t manually select models). The result feels less like a plugin and more like a platform.
Its free tier is ad-supported, but the ads are developer-focused, making free use practical for small teams or individual engineers.
How it compares:
Where Amazon Q is deeply tied to AWS, Amp is cloud-agnostic. It scales with your workload, not your subscription tier, and the conversation-sharing feature gives teams a persistent memory that Q doesn’t offer.
GitHub Copilot
GitHub Copilot is still the most widely used coding assistant, and for good reason. It integrates naturally into existing developer workflows and doesn’t ask you to change how you work. Owned by Microsoft, it runs on the same foundation as OpenAI’s GPT models, but GitHub also integrates Anthropic models and its own routing logic depending on the task.
Copilot works in most popular IDEs, like VS Code, JetBrains, and Visual Studio, and now includes Copilot Workspace, a feature that lets you describe a task, see a plan, and apply the resulting code changes directly in your editor.
Pricing is straightforward: the Pro plan costs $10 per user per month, and Pro+ costs $39 per user per month with expanded usage quotas. A free tier is available for students and open-source maintainers.
How it compares:
Copilot is broader but less specialized. It won’t know your AWS architecture the way Q does, but it works anywhere. If your workflow already revolves around GitHub, it’s a natural fit.
Cursor
Cursor rethinks what an IDE can be when AI is built in from the start. It’s a fork of VS Code that adds deep context awareness and multi-model reasoning. Cursor indexes your entire codebase so that when you ask it to “add caching” or “refactor this function,” it understands the relationships between files and can make project-wide changes.
Developers can connect their own API keys for models from OpenAI, Anthropic, or Google, which means you aren’t locked into one provider.
Cursor’s pricing starts with a free plan, then scales to Pro ($20/month), Pro+ ($60/month), and Ultra ($200/month) tiers that raise context limits and request volumes.
How it compares:
Amazon Q integrates with your existing IDE; Cursor is your IDE. It’s the better option if you want an AI-native environment with full-project understanding and control over which model powers it.
Tabnine
Tabnine was one of the first coding assistants to emphasize privacy and enterprise control. It runs on models trained entirely on permissive open-source code and supports deployment in air-gapped or on-premises environments. For teams in regulated industries, that’s a major advantage.
Tabnine integrates with all major editors, including VS Code, JetBrains, and Visual Studio. It also supports organization-specific model training so your team’s private codebase can drive better completions.
The company offers a free individual plan and paid tiers for teams and enterprises that need custom deployment or governance.
How it compares:
Tabnine trades cloud integration for data ownership. It’s ideal for teams that prioritize privacy or operate under strict compliance requirements. Amazon Q, by contrast, requires AWS connectivity and stores context in the cloud.
Windsurf by Codeium
Windsurf is a full IDE originally developed under the Codeium brand. The company later rebranded around Windsurf, and in mid-2025, Cognition Labs — the team behind the autonomous engineer project Devin — acquired what remained of the Codeium business after an earlier partial acquisition by Google. The result combines Cognition’s reasoning systems with Windsurf’s AI-native editor.
Windsurf’s Cascade system chains multiple agents together to complete multi-step tasks like generating tests, fixing errors, or restructuring codebases. It runs as a standalone editor or a VS Code extension and doesn’t require configuration or API keys.
It’s free for individual developers, with enterprise plans available for teams that need collaboration and policy controls.
How it compares:
Windsurf focuses on end-to-end automation, while Amazon Q focuses on AWS-specific assistance. If you want an IDE that can operate independently of your cloud stack and handle larger workflows, Windsurf is worth testing.
Final Thoughts
Amazon Q Developer is a strong option for anyone deeply invested in AWS. Its context awareness and built-in security and performance insights make it uniquely valuable for that environment. But its narrow focus also limits how far you can take it outside AWS.
If your work spans multiple stacks or clouds, or you want to keep control over which models you use, tools like Sourcegraph Amp, GitHub Copilot, Cursor, Tabnine, and Windsurf give you more flexibility.
Each has its own trade-offs: Amp emphasizes collaboration, Copilot offers ubiquity, Cursor provides deep context, Tabnine focuses on privacy, and Windsurf aims for autonomy. But all of them share one important trait: they work wherever your code does. When choosing your assistant, it usually makes the most sense to look for one that scales with your workflow, not with your vendor’s ecosystem.
Top comments (1)
I have been using Claude Code and OpenCode since June, and I think they are better alternatives than all the IDEs or IDE plugins you mentioned.