Amazon Q Developer

What is Amazon Q Developer?

Amazon Q Developer is AWS’s AI coding assistant for people who build and run software, especially inside the AWS ecosystem. It grew out of CodeWhisperer, AWS’s earlier autocomplete tool, and AWS expanded it into something broader: inline code suggestions, chat inside the IDE, security scanning, code reviews, test generation, CLI help, and agents that can take on multi-step development work. In practice, it sits in places developers already use, like VS Code, JetBrains IDEs, Visual Studio, the AWS console, the CLI, Slack, Teams, and GitHub.

AWS built it to answer a very specific problem. A lot of development work is not the glamorous part of writing new features. It is reading unfamiliar code, wiring up infrastructure, upgrading old Java apps, fixing security findings, writing tests, and remembering the exact syntax for cloud services and command-line tools. Amazon Q Developer tries to reduce that friction, with a strong bias toward AWS-native workflows. If your team works heavily with CloudFormation, CDK, Lambda, IAM, VPCs, or AWS operations, that bias is often the reason to look at it in the first place.

We found that Amazon Q Developer is most compelling for teams already invested in AWS, and less distinctive for teams that are mostly provider-agnostic. AWS points to strong usage signals from customers like BT Group and National Australia Bank, which reported multiline suggestion acceptance rates of 37 percent and 50 percent respectively. Those numbers matter because they suggest developers are not just trying the tool, they are actually keeping a meaningful share of what it writes.

Key Features

• Inline code suggestions: Amazon Q Developer generates code as you type, from single lines to whole functions, across more than 25 languages. AWS says customers like BT Group accepted 37 percent of multiline suggestions, and National Australia Bank reported 50 percent, which is a useful signal that the output is often close enough to keep without major rewriting.

• Agentic coding: Instead of only suggesting snippets, Amazon Q Developer can plan and execute multi-file tasks from a natural language request. AWS describes workflows where a developer asks for a feature, reviews a generated plan, then lets Q update files, run tests, and return a diff, which changes the tool from autocomplete into something closer to a junior pair programmer.

• Unit test generation: Developers can ask Amazon Q Developer to generate tests, including with the /test workflow AWS introduced for IDE use. This matters most in teams where test coverage lags behind feature work, because the bottleneck is often not knowing tests are needed, it is finding time to write them.

• Security scanning with suggested fixes: Amazon Q Developer scans code for issues like exposed credentials and other common vulnerabilities, then recommends remediations. AWS benchmark data showed 84.7 percent precision and 100 percent recall on its OWASP Top Rules for Java benchmark, which is notable because false positives are what usually make developers ignore security tools.

• Code reviews in GitHub: In GitHub preview, Amazon Q Developer can review pull requests automatically and leave threaded findings. For teams already buried in PR queues, this can catch obvious issues before a human reviewer spends time on them.

• Java and.NET modernization: Amazon Q Developer includes transformation tools for upgrading older applications, especially Java upgrades such as Java 8 to Java 17. AWS prices this by lines of code, which tells you something important: this is meant for real migration work, not just toy refactors.

• AWS-aware infrastructure help: It supports CloudFormation, Terraform, and AWS CDK, and it is unusually strong at generating AWS infrastructure code from plain English. If you ask for a VPC setup, IAM policy pattern, or SageMaker pipeline, it has more built-in context than general coding assistants usually do.

• CLI assistance: Amazon Q Developer helps with command-line work for more than 250 CLI tools and can translate natural language into shell commands. For developers who live in terminals and constantly forget exact flags, this saves a surprising amount of context switching.

• AWS Console chat: Inside the AWS console, you can ask questions about services, architecture, costs, and resources. This is useful because it keeps the conversation next to the actual AWS account context, rather than forcing developers to jump to docs or a separate chatbot.

• Slack and Microsoft Teams integration: Teams can ask Amazon Q Developer about AWS issues from chat tools they already use. This is more practical for ops and platform teams than for pure application coding, because incident coordination often already happens in Slack or Teams.

• Large context window: AWS positions Amazon Q Developer with up to a 200,000-token context window, larger than the 128,000-token standard context often cited for GitHub Copilot. Bigger context does not solve every codebase problem, but it helps when working across large files or trying to keep more project state in view.

• Organization-specific customization: Teams can train private customizations on internal repositories or S3-stored code for Java, JavaScript, TypeScript, and Python. That matters because many developers dislike AI coding tools not because they are wrong, but because they are wrong in the same generic way every time.

Use Cases

Amazon Q Developer’s clearest use case is AWS-heavy application development. Teams building cloud-native software can describe infrastructure in plain language and get CDK, CloudFormation, or Terraform output that already reflects common AWS patterns. AWS shows examples around infrastructure requests like VPC-only SageMaker pipelines with GPU access, where the value is not just speed but remembering all the networking and security details that people often miss when writing from scratch.

A second strong use case is modernization. AWS has pushed Amazon Q Developer hard around Java upgrades and technical debt cleanup, and that focus feels grounded in a real pain point. Enterprises often have aging Java applications that nobody wants to touch because upgrades are slow, risky, and spread across too many files. Amazon Q Developer can inspect the codebase, identify compatibility issues, propose changes, and help move workloads from Java 8 to Java 17. For teams staring at years of deferred maintenance, that is more valuable than another autocomplete tool.

There is also a practical security story here. Amazon Q Developer scans code for vulnerabilities and suggests fixes inside the developer workflow, not as a separate security program months later. AWS’s benchmark numbers, including 84.7 percent precision and 100 percent recall on its OWASP Top Rules for Java benchmark, suggest this is one of the more serious parts of the product. For developers, the difference is simple: a warning with a one-click fix gets handled, a PDF from a security team often does not.

The customer evidence AWS shares is still mostly productivity oriented, but useful. BT Group reported a 37 percent acceptance rate on multiline suggestions. National Australia Bank reported 50 percent. Those are not vanity metrics if you read them carefully. They suggest the tool is useful in day-to-day coding, not just in demos. AWS also cites organizations using it to speed up feature delivery, write tests, and reduce repetitive work across projects, though the most concrete stories tend to be strongest in AWS-centric engineering teams rather than general software shops.

Strengths and Weaknesses

Strengths:

• It is genuinely stronger in AWS work than general coding assistants. When developers are writing CloudFormation, CDK, IAM policies, or asking architecture questions inside the AWS console, Amazon Q Developer has a home-field advantage. GitHub Copilot is broader, but Q often feels more opinionated and useful when the problem is specifically “how do I build this on AWS?”

• The pricing is simple for teams comparing it with Copilot. At $19 per user per month for Pro, it lands at the same headline price as GitHub Copilot Business. AWS also includes IP indemnity in that tier, which matters for companies that would otherwise need legal review before rolling out AI-generated code widely.

• The modernization story is more concrete than most competitors. A lot of coding assistants talk about productivity in general terms. Amazon Q Developer has a more specific pitch around Java upgrades, code transformation, and technical debt reduction. If your backlog includes “modernize this old enterprise app,” that is more relevant than better autocomplete.

• Security scanning looks like a serious feature, not filler. AWS published benchmark numbers that show strong precision and recall, and the suggested remediations are integrated into the coding workflow. That gives it more practical value than tools that only flag issues without helping fix them.

• It shows up in more AWS-adjacent surfaces than most rivals. IDEs, CLI, AWS console, Slack, Teams, and GitHub all matter. For ops and platform teams, that spread means Amazon Q Developer can help outside the editor, which is where a lot of cloud work actually happens.

Weaknesses:

• Outside AWS, it is easier to ask why you would choose it over GitHub Copilot. If your team is multi-cloud, mostly on Azure or GCP, or just writing application code without much infrastructure work, Amazon Q Developer loses some of its identity. In those cases, Copilot’s broader ecosystem and model options may be more appealing.

• The agent experience is promising, but still needs supervision. AWS’s SWE-Bench results are strong, but not magical. Multi-step feature work still needs review, and for business-critical code, teams should expect to validate outputs carefully rather than treating the agent as autonomous.

• Customization support is narrower than it sounds. Private customization is useful, but today it covers Java, JavaScript, TypeScript, and Python. If your internal standards live mostly in other languages, you do not get the full benefit.

• Very large, multi-repo organizations may still need something else. Amazon Q Developer can work with a large context window, but that is not the same as understanding dozens or hundreds of repositories at an architectural level. Teams with sprawling enterprise codebases may find it helpful inside a repo, but not sufficient across the whole estate.

• The free tier is generous enough to try, but limited enough to hit quickly. Fifty agentic requests per month and 1,000 transformation lines of code is enough for evaluation. It is not enough for a team to rely on heavily, especially if developers start using the chat and agent features daily.

Pricing

• Free: $0 Includes 50 agentic requests per month and 1,000 lines of code per month for transformations. This is enough to test the IDE experience, try chat, and get a feel for the agent, but regular users will likely run into limits quickly.

• Pro: $19/user/month Includes unlimited agentic requests, subject to throttling, plus 4,000 lines of code per month for transformations per user. AWS also includes IP indemnity, admin controls, and IAM Identity Center support here, which makes Pro the real business tier even though the price matches GitHub Copilot Business.

• Transformation overage: $0.003 per line of code This matters if you plan to use Amazon Q Developer for Java modernization or larger migration projects. AWS pools transformation line allowances across Pro users at the payer account level, so 10 Pro users means 40,000 included lines per month before overages.

In practice, most individual developers will compare the Pro plan directly with GitHub Copilot Business because both sit at $19. The hidden gotcha is not the seat price, it is transformation usage if you adopt the modernization features seriously. If your team only wants chat, autocomplete, and reviews, the seat cost is easy to understand. If you want Q to help modernize large applications, watch the line-of-code billing carefully.

Alternatives

GitHub Copilot GitHub Copilot is the obvious alternative, and for many teams it is the default one. It serves a broader audience than Amazon Q Developer, especially teams that are not deeply tied to AWS. Copilot also benefits from GitHub’s central place in the developer workflow and its support for multiple frontier models. We think Copilot is the safer choice for general software teams, while Amazon Q Developer is the more compelling choice for AWS-native teams that want infrastructure help, console integration, and modernization features.

Claude Claude is not a direct IDE-first competitor in the same way, but many developers use it as a coding partner for reasoning-heavy work. It tends to shine when the task is understanding a system, discussing tradeoffs, or carefully planning a complex refactor. Teams may choose Claude over Amazon Q Developer when they want better long-form thinking and are less concerned about AWS-specific integration.

ChatGPT ChatGPT is often used as the broadest general-purpose coding assistant, especially for prototyping, debugging ideas, and quick explanations. Compared with Amazon Q Developer, it is less tied to a development environment and less specialized for AWS workflows. Someone might choose ChatGPT if they want flexibility across many kinds of tasks, but it usually requires more manual copy-paste and less workflow integration.

Augment Code Augment Code is aimed more squarely at larger engineering organizations that need deep understanding across many repositories. If your real problem is not “help me write this function” but “help me understand how 100 repos fit together,” Augment is closer to that need. Amazon Q Developer is stronger when the work is inside AWS and inside the active repo. Augment is stronger when the challenge is organizational scale and architecture sprawl.

Google’s agentic coding tools Google’s newer coding agents are still evolving, and they appeal most to teams that want to experiment with agent-first development. Compared with Amazon Q Developer, they are less anchored in a cloud operations workflow today. Teams already committed to AWS usually have a clearer path with Q. Teams that want to explore newer agent experiences may still test Google’s tools alongside it.

FAQ

What is Amazon Q Developer used for?

It helps developers write code, review code, generate tests, scan for security issues, modernize old applications, and work with AWS infrastructure. It is most useful for teams already building on AWS.

Is Amazon Q Developer the same as CodeWhisperer?

Not exactly. Amazon Q Developer is the broader product that grew out of CodeWhisperer. It includes code suggestions, but also chat, agents, reviews, security scanning, and modernization tools.

How do I get started?

Install the extension in VS Code, JetBrains, Visual Studio, or another supported environment, then sign in with AWS Builder ID or IAM Identity Center. The free tier is enough to try the main experience without needing a paid plan.

How long does it take to set up?

For an individual developer, setup usually takes a few minutes. Enterprise rollout takes longer because teams often connect identity, permissions, and admin policies through AWS.

Does Amazon Q Developer work outside AWS?

Yes, it supports many common programming languages and general coding tasks. Still, its biggest advantage shows up when your work involves AWS services, infrastructure, or operations.

Which IDEs does it support?

AWS supports Visual Studio Code, JetBrains IDEs, Visual Studio, and Eclipse in preview. It also appears in the CLI, AWS console, GitHub, Slack, and Microsoft Teams.

How much does Amazon Q Developer cost?

There is a free tier at $0 and a Pro tier at $19 per user per month. Transformation work can also incur overage charges at $0.003 per line of code after included limits.

Is there a free version?

Yes. The free tier includes 50 agentic requests per month and 1,000 lines of code per month for transformations. It is good for testing, but frequent users will likely need Pro.

Can Amazon Q Developer review pull requests?

Yes, in GitHub preview it can automatically review pull requests and leave findings. That is helpful for catching obvious issues before a teammate spends time reviewing.

Can it generate unit tests?

Yes. Amazon Q Developer can generate tests as part of its agent workflows, including through the /test experience AWS introduced in the IDE.

Is Amazon Q Developer better than GitHub Copilot?

It depends on your environment. We think Amazon Q Developer is stronger for AWS-heavy teams and modernization work, while GitHub Copilot is often stronger for broader, provider-agnostic development.

Is Amazon Q Developer safe for enterprise use?

AWS includes enterprise controls like IAM Identity Center support, admin features, and IP indemnity in Pro. Teams should still review generated code carefully and confirm that the product fits their internal security and compliance requirements.