Overview
Amazon Q Developer is a generative AI coding assistant launched by Amazon Web Services (AWS), designed to accelerate the entire software development lifecycle. It deeply integrates with over 200 AWS services, enabling developers to efficiently code, debug, and optimize architectures within code editors, command lines, and even the AWS Management Console. Built on AWS's extensive technical expertise, Amazon Q Developer offers industry-leading multi-line code completion acceptance rates and comprehensive features ranging from code generation to security scanning and architectural recommendations.
Across the AWS ecosystem, the assistant's reach has continued to broaden: AWS Agent Registry became generally available on 2026-08-31 as a unified, searchable and governed catalog for managing agents, tools, skills and custom resources across an organization, while Amazon SageMaker HyperPod added managed Ray support on Amazon EKS on 2026-08-24, with the ability to create and monitor Ray clusters and connect JupyterLab and Code Editor notebooks to live clusters.
Key Features
- Intelligent Code Completion and Generation: Provides multi-line code suggestions based on generative AI, with the industry's highest code acceptance rate (according to BT Group report), significantly boosting coding efficiency.
- Deep Integration with AWS Services: Natively supports over 200 AWS services, allowing direct invocation of AWS APIs, generation of cloud resource code, and optimization of cloud costs and resources within IDEs or command lines.
- Full Lifecycle Automation Agent: Equipped with agent capabilities, it can autonomously perform tasks such as feature implementation, documentation writing, testing, code review, refactoring, and software upgrades, covering the entire development process. AWS Agent Registry, generally available as of 2026-08-31, provides a unified, searchable and governed catalog for managing the agents, tools, skills and custom resources across an organization.
- Security Scanning and Remediation Suggestions: Automatically scans code for security vulnerabilities and potential issues, providing remediation suggestions to help developers build more secure applications.
- Architecture and Cost Optimization Guidance: Integrated into the AWS Management Console and supports Microsoft Teams and Slack, offering cloud architecture recommendations, cost optimization plans, and resource management guidance.
- Multi-IDE and Command Line Support: Provides plugins for major IDEs such as VS Code, JetBrains, Visual Studio, and Eclipse, as well as command-line tools, flexibly adapting to different development environments.
- Managed Ray Support on SageMaker HyperPod: As of 2026-08-24, Amazon SageMaker HyperPod offers managed Ray support on Amazon EKS, allowing users to create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, and get out-of-the-box observability.
Use Cases
- Quickly build and deploy applications in the AWS cloud environment, leveraging code completion and AWS service integration to enhance development efficiency
- Perform security scanning, refactoring, and upgrades on existing code to ensure code quality and security
- Reduce cloud resource costs and optimize system design through architecture recommendations and cost optimization features
- Obtain AWS resource management and architecture guidance directly in Slack or Teams during team collaboration
- Manage agents, tools, skills and custom resources at scale through the generally available AWS Agent Registry (2026-08-31)
- Create and monitor Ray clusters on Amazon SageMaker HyperPod and connect JupyterLab and Code Editor notebooks to live clusters (2026-08-24)
Pros
- Deeply integrated with the AWS ecosystem, highly friendly for cloud developers
- Industry-leading multi-line code completion acceptance rate, boosting coding speed
- Covers the entire development lifecycle, providing a one-stop solution from coding to deployment optimization
- Supports multiple IDEs and command lines, flexibly adapting to different workflows
- Offers security scanning and architecture recommendations, enhancing application reliability and cost-effectiveness
- Backed by an expanding AWS tooling landscape, including the generally available AWS Agent Registry (2026-08-31) and managed Ray support on SageMaker HyperPod (2026-08-24)
Pricing
Adopts a Freemium model, offering a Free Tier for developers to get started quickly, with advanced features requiring a paid subscription. For specific pricing, please refer to the AWS official website.
Summary
Amazon Q Developer is best suited for teams and individuals engaged in cloud development within the AWS ecosystem. Its core advantages lie in deep integration with AWS services, industry-leading multi-line code completion acceptance rates, and automation capabilities covering the entire development lifecycle, significantly improving development efficiency, code security, and cloud resource management effectiveness. Its surrounding AWS tooling keeps expanding as well, from the generally available AWS Agent Registry (2026-08-31) for governed management of agents, tools and skills, to managed Ray support on Amazon SageMaker HyperPod (2026-08-24).
Version History
- Run multi-node inference workloads using Ray LLM Deep Learni (2026-10-12): 了解如何在Amazon EKS上使用多节点Ray LLM Deep Learning Containers，将一个大型语言模型跨两个GPU节点部署，并在一个兼容OpenAI的
- 跨团队共享GPU集群，并实现隔离与公平性 (2026-10-08): A reference architecture for securely sharing one Amazon SageMaker HyperPod EKS cluster across multiple teams, using AWS IAM Identity Center for authentication, per-team SageMaker Domains and Kubernet
- Beyond hours saved: Building the business case for agentic a (2026-10-07): RPA时代的ROI模型忽略了代理式自动化所创造的大部分价值。本文为AI卓越中心负责人提供了一个框架，用于衡量代理在节省时间、异常处理等方面的全部价值。
- 负责任的人工智能治理：AWS如何将客户定位于人工智能 (2026-10-06): AWS投资于帮助客户与国际负责任AI治理标准保持一致的工具。在这篇文章中，我们探讨AI系统影响评估：它是什么，以及它如何改善企业
- 在Amazon Quick中降级用户角色 (2026-10-05): Amazon Quick不提供直接从控制台将用户从Admin或Author降级为Reader的路径。本文介绍两种可靠方法：手动删除并重新创建的方法以及AWS CLI
- Enhance industrial safety AI with synthetic data on Amazon (2026-09-17): Learn how to build a synthetic data augmentation pipeline on Amazon SageMaker AI and Amazon Rekognition that generates photo-realistic, auto-labeled training images for industrial safety AI. This appr
- Boost HCLS AI reasoning with open source agent skills (2026-09-16): AI agents built on foundation models frequently misuse healthcare and life sciences decision frameworks, citing the right guidelines but applying them incorrectly. This post shares 38 open source agent skills covering
- Build an AI-powered product tagging system with Amazon SageM (2026-09-15): Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable r
- Integrating Outlook with Amazon Quick for AI-powered email a (2026-09-03): Integrate Microsoft Outlook with Amazon Quick to automate email management, calendar scheduling and workflow coordination. This article covers the end-to-end setup and demonstrates automation scenarios.
- Modernizing and scaling support operations with generative A (2026-09-02): Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and u
- From theory to delivery: How Atos upskilled 400 engineers in (2026-09-01): When Atos set out to upskill 400 engineers in agentic AI, hands-on learning was the missing ingredient. Over three days, engineers built multi-agent systems on AWS through an AI League event. This pos
- Manage agents, tools and skills at scale with AWS Agent Regi (2026-08-31): AWS Agent Registry is now generally available: a unified, searchable and governed catalog for managing the agents, tools, skills and custom resources across your organization. This article explains what Registry is
- Batch write and discover records in Amazon SageMaker Feature (2026-08-28): Amazon SageMaker Feature Store now supports two new APIs: BatchWriteRecord writes up to 25 records across multiple feature groups in a single call, and ListRecords enumerates record identifiers within
- Governed reports with Amazon Quick Desktop and Amazon FSx fo (2026-08-25): Build a governed weekly reporting workflow with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP. An Amazon S3 access point exposes an approved folder to a Quick knowledge base, and a custom skill
- Introducing new Ray capabilities on SageMaker HyperPod (2026-08-24): Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, get out-of-the-box observability.