In-IDE code assistants for faster, AWS-aware development
Amazon CodeWhisperer is an AWS-integrated code assistant that generates line- and function-level, context-aware code suggestions and AWS SDK/IAM recommendations directly inside popular IDEs. It is ideal for backend and cloud developers building AWS services who need accurate SDK calls, security-aware prompts, and enterprise governance. Pricing includes a free tier for individuals, a $19/user/month Professional plan, and custom Enterprise options with org controls.
Amazon CodeWhisperer is an AWS code assistant that generates line- and function-level code suggestions directly inside supported IDEs. It produces context-aware completions, recommends correct AWS SDK calls and IAM usage, and flags some security or credential risks. The primary capability is IDE-integrated code generation tailored to AWS APIs and common programming languages. Its key differentiator is AWS-aware recommendations combined with enterprise privacy controls. CodeWhisperer serves backend engineers, cloud developers, and DevOps teams working on AWS. Pricing is accessible via a free tier and paid plans for higher usage and enterprise features (pricing noted below).
Amazon CodeWhisperer is an AI-powered code assistant from AWS introduced for developers who build on Amazon Web Services. Positioned as an in-IDE productivity tool, CodeWhisperer aims to reduce boilerplate and speed feature delivery by generating code snippets, full functions, and API call sequences that match a developer’s local context. AWS emphasizes integration with its SDKs and cloud services so suggestions frequently include AWS-specific best practices, making this tool especially relevant to teams already invested in the AWS ecosystem.
CodeWhisperer’s core feature set centers on context-aware code suggestions that appear inline in editors. It integrates with Visual Studio Code, JetBrains IDEs (IntelliJ family), and Visual Studio, offering completions as you type and whole-function proposals when you comment intent. The tool recognizes many common languages—Java, Python, JavaScript/TypeScript, C#, Go and more—and can surface example usages for AWS SDK calls. It also includes a security-detection capability that highlights potential secrets or risky API usage and suggests remediation. Additional features include a recommendation history, the ability to request alternate suggestions, and IDE-accessible settings to restrict suggestion sources and telemetry collection.
On pricing, Amazon provides a free tier that delivers basic CodeWhisperer usage at no charge for personal or discovery use, while paid tiers unlock higher request quotas and enterprise controls. AWS offers an Individual/Pro paid plan (monthly fee) for heavier individual users and a Business/Enterprise option with custom pricing that adds IAM-based controls, corporate licensing, and broader telemetry & audit features. The free tier covers limited monthly recommendations and basic scanning; paid plans increase recommendation quotas, add priority support, and enable centralized admin controls and VPC-compatible deployment patterns for strict data governance.
Developers and teams use CodeWhisperer in practical workflows like speeding API integrations and reducing boilerplate in new microservices. Example users include a backend engineer implementing AWS Lambda handlers using generated boilerplate and SDK calls, and a DevOps engineer auto-generating infrastructure-as-code snippets with correct IAM scopes. It also assists SREs writing monitoring hooks and QA engineers creating unit-test skeletons. For organizations focused on non-AWS stacks or who prioritize the broadest language-model capability, competitors such as GitHub Copilot may be stronger in non-cloud-specific contexts, while CodeWhisperer excels for AWS-centric development.
Three capabilities that set Amazon CodeWhisperer apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Limited completions per month; no centralized enterprise governance or commercial SLAs | Individual developers evaluating CodeWhisperer at no cost |
| Professional | $19 per user/month | Higher recommendation throughput; personal usage only; no centralized admin controls | Individual professionals needing higher usage and private suggestions |
| Enterprise | Custom | Organization-wide governance, SSO, audit logs, configurable data retention and SLA options | Large organizations needing centralized controls and compliance features |
Copy these into Amazon CodeWhisperer as-is. Each targets a different high-value workflow.
You are a backend engineer assistant. Task: produce a complete Node.js Express route handler that lists objects in a specific S3 prefix using AWS SDK v3 (@aws-sdk/client-s3). Constraints: use async/await, handle pagination (MaxKeys), accept query params bucket and prefix, return JSON with {objects:[{Key,Size,LastModified}]}, include minimal IAM policy snippet granting least privilege for GetObject/ListBucket on that bucket. Output format: 1) full Express route code (copy-paste ready), 2) short IAM policy JSON, 3) one-line usage example (curl). Example: show how handler reads bucket from req.query.
You are a QA-focused code assistant. Task: generate a pytest test file that tests an AWS Lambda Python handler which reads from S3 via boto3 and writes to DynamoDB. Constraints: use pytest and pytest-mock (or moto) mocks, include tests for success, S3 missing object (ClientError), and DynamoDB conditional failure; avoid real AWS calls. Output format: a test file named test_handler.py with imports, three test functions, sample fixture(s), and example mock return values. Example: show how to patch boto3.client('s3').get_object to return a BytesIO body.
You are an IAM policy generator. Task: produce a least-privilege JSON IAM policy for a Lambda that reads specific items from a DynamoDB table and writes processed files to an S3 bucket path. Constraints: parameterize ARNs with placeholders (e.g., {{DDB_TABLE_ARN}} , {{S3_BUCKET_ARN}}), limit DynamoDB actions to Query and GetItem with condition on table name, limit S3 to PutObject on a specific prefix, avoid wildcard resource for DynamoDB attributes. Output format: 1) clean JSON policy document, 2) a 3-line explanation of each statement, 3) one-line example on how to attach the policy to a Lambda execution role using AWS CLI.
You are a DevOps CloudFormation author. Task: produce a YAML AWS::Serverless or AWS::CloudFormation snippet that defines an ECS Fargate service behind an ALB with target group, a scalable target (ApplicationAutoScaling) and a simple target tracking policy based on CPUUtilization. Constraints: accept Parameters for ClusterName, VpcId, SubnetIds, ContainerImage, DesiredCount, and SecurityGroup; retrieve container secrets from SSM Parameter Store; keep resource names generic. Output format: full YAML snippet for resources and a short Parameters section, plus a one-line deploy command using AWS CLI or SAM.
You are a senior cloud architect. Task: produce a step-by-step migration plan (5–8 steps) to move a legacy monolithic REST endpoint (Node/Express) into AWS Lambda + API Gateway, including refactor checkpoints, data-access abstraction, and CI/CD changes. After the plan, provide: 1) a compact example Lambda handler (Node.js) converted from an Express handler, 2) a minimal SAM template snippet to deploy the function and API Gateway, and 3) a rollback/test checklist. Constraints: prioritize minimal downtime, include database connection best-practices (connection pooling or RDS Proxy), and include environment variable handling. Output format: numbered plan, code block for handler, SAM YAML, and a 5-item testing checklist.
You are a security-savvy code reviewer. Task: given codebase snippets (or scan results), identify up to 8 high-priority AWS SDK anti-patterns (hardcoded credentials, overly-broad IAM calls, synchronous blocking calls in Lambdas, missing retries/timeouts, logging sensitive data). For each finding provide: 1) concise description, 2) severity (High/Med/Low), 3) concrete remediation with a code patch (diff-style) or replacement snippet, and 4) one IAM or runtime configuration change to mitigate. Few-shot examples: show a vulnerable example (hardcoded key) and the fixed example (use environment or IAM role). Output format: numbered findings with labeled fields and diffs.
Choose Amazon CodeWhisperer over GitHub Copilot if you prioritize AWS SDK accuracy and enterprise data governance for cloud-native development workflows.
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