Amazon Q Developer is AWS's AI coding assistant. It does inline completion, an in-IDE chat, multi-file feature agents (/dev), managed code upgrades (/transform), unit-test and documentation generation, and security scanning — across VS Code, JetBrains, Visual Studio, the CLI, and the AWS console. This guide covers every feature, the supported IDEs and languages, Free vs Pro pricing, how it stacks up against GitHub Copilot and Cursor, and how to set it up for a whole team.
Amazon Q Developer is the coding half of the Amazon Q family — an AI assistant aimed at the full software lifecycle, not just autocomplete.
Amazon Q Developer is AWS's generative-AI assistant for software development. It is the successor to and superset of what AWS previously shipped as CodeWhisperer: it kept the inline code suggestions and added a chat interface, autonomous agents, code transformation, test and documentation generation, and security scanning. If you remember CodeWhisperer, Q Developer is that capability folded into the broader Amazon Q brand and substantially expanded.
It is built on Amazon Bedrock, so it inherits AWS's managed-inference data posture. The distinction that matters: Q Developer is a finished product where AWS manages the model for you, whereas Amazon Bedrock gives you raw model-level API access to build your own tools. You do not choose the model inside Q Developer; you choose the tier and configure how your team uses it.
Q Developer is one of two products under the Amazon Q name. Its sibling, Amazon Q Business, is an enterprise assistant that answers questions over your company data and is not a coding tool. If you landed here unsure which you need, the parent overview — Amazon Q: the complete guide — disambiguates the family.
The mental model: think of Q Developer less as "autocomplete with AWS branding" and more as "an AI teammate that can complete code as you type, answer questions about your codebase and your AWS account, take on a scoped feature, upgrade a legacy codebase, and flag security issues — without leaving your editor."
Q Developer is a bundle of distinct capabilities. Here is what each one does and when you reach for it.
It helps to group the features into three layers: (1) in-the-flow assistance while you type, (2) agents that take on a whole task, and (3) quality and safety tooling. The agentic layer — /dev and /transform — is what most distinguishes Q Developer from a pure completion tool.
Q Developer meets engineers where they already work — across the major IDE families, the terminal, and the AWS console.
IDEs. Amazon Q Developer is available as an extension/plugin for Visual Studio Code, the JetBrains family (IntelliJ IDEA, PyCharm, WebStorm, GoLand, Rider, and others), Visual Studio, and Eclipse. In each, you get both inline completion and the chat experience.
Command line. The Q Developer CLI agent (on macOS and Linux) completes commands, explains errors, and can translate a natural-language request into the right shell command — useful for ops and for anyone who lives in the terminal.
AWS Management Console & chat. Inside the console, Q Developer is an always-available assistant for AWS questions and troubleshooting. It is also reachable from Slack and Microsoft Teams via AWS Chatbot, so you can ask about your AWS resources without switching tools.
Languages. Q Developer supports 15+ programming languages. Depth is strongest in Python, Java, JavaScript/TypeScript, C#, and Go, with solid support for Rust, Ruby, PHP, Kotlin, C/C++, SQL, shell, and infrastructure-as-code (CloudFormation, CDK, Terraform). As with any AI assistant, suggestion quality tracks how well-represented a language and framework are.
| Surface | Inline completion | Chat | Agents (/dev, /transform) | Notes |
|---|---|---|---|---|
| VS Code | Yes | Yes | Yes | Most complete experience |
| JetBrains (IntelliJ, PyCharm, etc.) | Yes | Yes | Yes | Broad JetBrains family coverage |
| Visual Studio | Yes | Yes | Partial | Core completion + chat; some agent features evolving |
| Eclipse | Yes | Yes | Partial | Newer integration; check feature parity |
| CLI (macOS/Linux) | Command completion | Yes | n/a | Natural-language → shell; error explanation |
| AWS Console | n/a | Yes | n/a | AWS-aware help & troubleshooting |
Two tiers, one per-seat decision. The figures below are representative as of 2026 — confirm current rates on the AWS pricing page.
Free tier. Individual developers can use Q Developer at no cost with a Builder ID — no AWS account required. The Free tier includes inline completion and chat, plus a capped monthly allowance of the heavier features (agent interactions such as /dev and /transform, and security scans). It is genuinely usable for solo work and for evaluating the product.
Pro tier. At roughly $19 per user per month, Pro raises the limits and adds the things teams need: centralized license management and access control through IAM Identity Center, administrative policy controls, customization to your private codebase, higher caps on agent interactions and security scanning, and the enterprise data-handling terms (your content is not retained or used to train the model). Pro is billed per active subscribed user.
Usage beyond the seat. Some high-volume agentic work — large code transformations in particular — can incur additional usage-based charges on top of the Pro seat, depending on the scale of the job. For most teams the predictable cost is simply seats × monthly price; model the transformation usage separately if you plan a big migration program.
| Capability | Free | Pro (~$19/user/mo) |
|---|---|---|
| Inline completion + chat | Yes | Yes |
| Agents (/dev, /transform) | Capped per month | Higher limits |
| Security scanning | Capped per month | Higher limits |
| Customization to private code | No | Yes |
| Org license mgmt (IAM Identity Center) | No | Yes |
| Admin policy controls | No | Yes |
| Content not retained / not used to train | Differs — check settings | Yes |
The three tools most teams weigh against each other. A brief, honest read — the deep coding head-to-head lives on the dedicated comparison page.
GitHub Copilot is the market leader, tightly integrated with GitHub (pull requests, Codespaces) and now multi-model with a large IDE ecosystem and the broadest mindshare. Cursor is an AI-first editor (a fork of VS Code) prized for fast, agentic, whole-codebase editing and a slick UX, popular with developers who want the most aggressive AI workflow. Amazon Q Developer differentiates on AWS awareness, managed /transform upgrades, security scanning in-editor, and being governed inside your AWS account.
The decision usually comes down to gravity. If your team lives in GitHub and wants the safest, most widely adopted choice, Copilot is the default. If you want the most cutting-edge agentic editing experience and are happy to switch editors, Cursor is compelling. If your stack is AWS-heavy, you value managed legacy-code upgrades, and you want the assistant governed by the same IAM and account controls as the rest of your infrastructure, Q Developer is the natural fit.
For the detailed feature-by-feature breakdown against Copilot specifically — completion quality, agents, pricing, enterprise controls — see Amazon Q vs GitHub Copilot.
Rolling Q Developer Pro out to a team is a short, well-trodden path through IAM Identity Center. Here is the shape of it.
For a larger rollout — wiring SSO cleanly, defining customization scope across many repos, setting policy guardrails, and driving adoption with measurement — this is exactly the kind of developer-tooling engagement CloudRoute routes to a vetted AWS partner, with AWS credits offsetting the surrounding AWS spend. See the next section.
For engineering leaders and security teams, this is the gating question. The short answer differs by tier.
On the Pro tier, your content — the code you write, your prompts, and your customization data — is not used to train the underlying foundation models and is handled under AWS's enterprise data terms. Customizations built on your private code are isolated to your organization and are not shared with other customers. Because Q Developer runs on Amazon Bedrock inside AWS's managed environment, the same data-boundary and regional guarantees that apply to Bedrock apply here.
On the Free tier, the data settings differ: by default some content may be used to improve the service unless you opt out, so individual users who care about this should review and adjust the data-sharing setting. For any team handling proprietary or regulated code, the Pro tier is the right choice precisely because of these stronger, default data-handling terms.
IP and licensing. The code-references feature flags suggestions that closely match public training data so you can decide whether to accept them and how to handle attribution/licensing — a meaningful control for organizations worried about inadvertently incorporating restrictively licensed code.
Auditability. Administrative controls and AWS logging let security teams see how Q is configured and used across the organization, so it can be governed with the same tooling already applied to the rest of the AWS estate.
On Pro, Amazon Q Developer does not use your code or prompts to train its models, and customizations stay isolated to your org. On Free, review the data-sharing setting and opt out if needed. Regulated teams should standardize on Pro.
Step 1 — Install. Add the Amazon Q extension from your IDE's marketplace (VS Code, JetBrains, Visual Studio, or Eclipse). For the CLI, install the Q command-line tool on macOS or Linux.
Step 2 — Sign in. Use a free Builder ID to start on the Free tier immediately — no AWS account needed. For Pro, sign in with the corporate identity your admin set up in IAM Identity Center.
Step 3 — Try inline completion. Open a file and start typing or write a descriptive comment; accept Q's suggestion to feel the loop.
Step 4 — Open chat and ask. Use the Q panel to explain a function, refactor a selection, or ask an AWS question. Then try an agent: /dev for a scoped feature, or /transform on a sample project to see a managed upgrade.
Step 5 — Decide on Pro. If you hit the Free-tier caps, want private-code customization, need SSO-managed licenses, or require the stronger data-handling terms, move the team to Pro through IAM Identity Center.
When the goal is a measured, organization-wide rollout — not just one developer experimenting — CloudRoute connects you to a vetted AWS partner who handles the setup and adoption, funded by AWS credits.
A scannable read across the three AI coding tools teams most often shortlist. Pricing is representative for 2026 and varies by tier/region — confirm on each vendor's page.
| Dimension | Amazon Q Developer | GitHub Copilot | Cursor |
|---|---|---|---|
| Form factor | IDE extension + CLI + AWS console | IDE extension + GitHub | Standalone AI-first editor (VS Code fork) |
| Standout strength | AWS awareness + /transform upgrades + security scan | Ecosystem, adoption, GitHub & PR integration | Aggressive whole-codebase agentic editing |
| Agents | /dev (feature), /transform (upgrades) | Copilot agents & coding agent | Composer / agent editing |
| Free tier | Yes (capped) | Limited free for individuals | Limited free tier |
| Paid (per user/mo) | ~$19 (Pro) | ~$10–$39 (Pro/Business/Enterprise) | ~$20 (Pro) |
| Code not used to train | Yes (Pro) | Yes (Business/Enterprise) | Yes (privacy mode) |
| Best for | AWS-heavy teams; legacy upgrades; governed in-account | GitHub-centric teams wanting the safe default | Devs wanting the most cutting-edge AI editor |
Situation: Two problems at once. First, a large Java 8 monolith was blocking them: the upgrade to a modern LTS had been deferred for years because nobody could spare the months it would take by hand. Second, they wanted an AI coding assistant standardized across the team but were nervous about proprietary code being used to train a third-party model. They had evaluated tools individually but had no managed rollout, no SSO integration, and no plan to measure impact.
What CloudRoute did: Routed within a day to an AWS partner with developer-productivity and migration experience. CloudRoute helped the partner secure AWS credits to fund the engagement — a Bedrock/GenAI proof-of-concept pool for the pilot, with Activate Portfolio credits covering the broader AWS spend. The partner deployed Amazon Q Developer Pro org-wide via IAM Identity Center (corporate SSO, group-based seats, policy controls), scoped customization to approved internal repos, then ran /transform across the Java monolith in tracked batches and stood up acceptance-rate and time-to-merge dashboards.
Outcome: The Java LTS upgrade that had been stalled for years was completed in weeks with /transform handling the bulk of the mechanical changes and engineers reviewing. Accepted-suggestion rate stabilized in the healthy range across the team, and proprietary code stayed out of model training under the Pro terms. Roughly the first $35K of AWS consumption across the pilot and rollout was credit-funded. CloudRoute’s commission was paid by the partner from AWS’s engagement funding — the customer paid $0 to CloudRoute.
rollout window: ~6 weeks · engineers: ~70 · credit-funded AWS spend: ~$35K · cost to customer: $0
CloudRoute routes you to a vetted AWS partner who handles setup, SSO, customization, and adoption — and helps secure AWS credits to fund the engagement. Customer pays $0. AWS funds it.