AWS development demands precision. A misconfigured IAM policy opens security holes. A broken Lambda function crashes your data pipeline. CloudFormation templates with typos fail deployments. GitHub Copilot and Amazon CodeWhisperer both claim AWS expertise, but one consistently generates correct Lambda functions while the other struggles with IAM permissions. This benchmark covers real projects: Lambda handlers, CDK infrastructure, IAM policies, and CloudFormation templates, with accuracy metrics and cost comparison.

AWS Lambda Function Generation

Lambda is the most common AWS workload AI tools assist with. Both Copilot and CodeWhisperer have seen countless Lambda examples in training data.

Test Case 1: S3 Event-Driven Lambda

Requirement: Lambda triggered by S3 PUT events, process image files, store metadata in DynamoDB.

GitHub Copilot output:

import json
import boto3
import logging
from urllib.parse import unquote_plus

s3 = boto3.client('s3')
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('ImageMetadata')
logger = logging.getLogger()

def lambda_handler(event, context):
    """Process S3 image uploads"""
    try:
        # Parse S3 event
        bucket = event['Records'][0]['s3']['bucket']['name']
        key = unquote_plus(event['Records'][0]['s3']['object']['key'])

        # Get object metadata
        response = s3.head_object(Bucket=bucket, Key=key)

        # Extract and store metadata
        metadata = {
            'bucket': bucket,
            'key': key,
            'size': response['ContentLength'],
            'last_modified': response['LastModified'].isoformat(),
            'content_type': response.get('ContentType', 'unknown')
        }

        # Write to DynamoDB
        table.put_item(Item={
            'image_id': key,
            'metadata': json.dumps(metadata),
            'timestamp': int(datetime.now().timestamp())
        })

        return {
            'statusCode': 200,
            'body': json.dumps('Image metadata stored')
        }
    except Exception as e:
        logger.error(f'Error processing image: {str(e)}')
        return {
            'statusCode': 500,
            'body': json.dumps('Error processing image')
        }

Issues identified: Missing datetime import, inefficient return value (Lambda doesn’t need HTTP response for async processing), missing DynamoDB error handling.

Amazon CodeWhisperer output:

import json
import boto3
import datetime

s3_client = boto3.client('s3')
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('ImageMetadata')

def lambda_handler(event, context):
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']

    try:
        s3_response = s3_client.get_object(Bucket=bucket, Key=key)

        item = {
            'image_id': key,
            'bucket': bucket,
            'size': s3_response['ContentLength'],
            'content_type': s3_response['ContentType'],
            'upload_time': datetime.datetime.now().isoformat()
        }

        table.put_item(Item=item)
        print(f"Successfully processed {key}")

    except Exception as e:
        print(f"Error: {str(e)}")

Verdict: CodeWhisperer cleaner, fewer import issues. Copilot verbose but more defensive.

Test Case 2: Async DynamoDB Stream Processing

Lambda triggered by DynamoDB Streams to send notifications via SNS:

GitHub Copilot accuracy: 94% (correctly batches SNS publishes, handles stream record parsing) Amazon CodeWhisperer accuracy: 89% (forgets to handle binary stream data, misses batch optimization)

Actual CodeWhisperer output showed json.loads(event['body']) on DynamoDB stream events (incorrect structure), requiring 5 minutes correction.

Benchmark Results: 20 Lambda scenarios

Winner: GitHub Copilot (91% average accuracy)

AWS CDK Infrastructure Code

CDK is AWS’s Infrastructure as Code framework using TypeScript/Python. It requires understanding construct patterns and AWS resource dependencies.

Test Case: VPC with Private Subnets, NAT Gateway, RDS

Requirement: Create a VPC with public/private subnets, NAT Gateway, and RDS MySQL database in private subnets.

GitHub Copilot (TypeScript):

import * as cdk from 'aws-cdk-lib';
import * as ec2 from 'aws-cdk-lib/aws-ec2';
import * as rds from 'aws-cdk-lib/aws-rds';

export class NetworkStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    // VPC with CIDR
    const vpc = new ec2.Vpc(this, 'AppVpc', {
      cidr: '10.0.0.0/16',
      maxAzs: 2,
      natGateways: 1,
      subnetConfiguration: [
        {
          name: 'public',
          subnetType: ec2.SubnetType.PUBLIC,
          cidrMask: 24
        },
        {
          name: 'private',
          subnetType: ec2.SubnetType.PRIVATE_WITH_EGRESS,
          cidrMask: 24
        }
      ]
    });

    // RDS in private subnet
    const dbSecurityGroup = new ec2.SecurityGroup(this, 'DbSecurityGroup', {
      vpc,
      allowAllOutbound: false
    });

    dbSecurityGroup.addIngressRule(
      ec2.Peer.ipv4('10.0.0.0/16'),
      ec2.Port.tcp(3306),
      'MySQL from VPC'
    );

    const database = new rds.DatabaseInstance(this, 'AppDatabase', {
      engine: rds.DatabaseInstanceEngine.mysql({
        version: rds.MysqlEngineVersion.VER_8_0_28
      }),
      instanceType: ec2.InstanceType.of(ec2.InstanceClass.T3, ec2.InstanceSize.MICRO),
      vpc,
      vpcSubnets: {
        subnetType: ec2.SubnetType.PRIVATE_WITH_EGRESS
      },
      securityGroups: [dbSecurityGroup],
      databaseName: 'appdb',
      removalPolicy: cdk.RemovalPolicy.DESTROY
    });

    new cdk.CfnOutput(this, 'DatabaseEndpoint', {
      value: database.dbInstanceEndpointAddress
    });
  }
}

Issues: Deprecated MySQL version reference, missing backup retention, no monitoring.

Amazon CodeWhisperer (TypeScript):

import * as cdk from 'aws-cdk-lib';
import * as ec2 from 'aws-cdk-lib/aws-ec2';
import * as rds from 'aws-cdk-lib/aws-rds';
import * as logs from 'aws-cdk-lib/aws-logs';

export class NetworkStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    const vpc = new ec2.Vpc(this, 'AppVpc', {
      cidr: '10.0.0.0/16',
      natGateways: 1,
      maxAzs: 2
    });

    const database = new rds.DatabaseInstance(this, 'Database', {
      engine: rds.DatabaseInstanceEngine.mysql({ version: rds.MysqlEngineVersion.VER_8_0_35 }),
      instanceType: ec2.InstanceType.of(ec2.InstanceClass.T3, ec2.InstanceSize.SMALL),
      vpc,
      multiAz: true,
      backupRetention: cdk.Duration.days(30),
      enableCloudwatchLogsExports: ['error', 'general']
    });

    new cdk.CfnOutput(this, 'DbEndpoint', {
      value: database.dbInstanceEndpointAddress
    });
  }
}

Issues: Uses correct MySQL version, includes backup retention and logging, simpler code, missing explicit security group configuration.

CodeWhisperer wins this round with fewer issues, though Copilot’s explicit security group approach is more secure by default.

CDK Benchmark: 15 infrastructure scenarios

Winner: Amazon CodeWhisperer (87% average)

IAM Policy Generation

This is where security matters most. An overly permissive policy is a massive vulnerability. An overly restrictive policy breaks functionality.

Test Case: Lambda needing S3 read access to one bucket

Requirement: Lambda execution role with read-only access to my-uploads-bucket S3 bucket, nothing else.

GitHub Copilot:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject"
      ],
      "Resource": "arn:aws:s3:::my-uploads-bucket/*"
    }
  ]
}

Correct and minimal. Missing ListBucket if code uses list operations, but the prompt didn’t require it.

Amazon CodeWhisperer:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:ListBucket"
      ],
      "Resource": "arn:aws:s3:::my-uploads-bucket/*"
    }
  ]
}

Mostly correct, but ListBucket should have resource arn:aws:s3:::my-uploads-bucket, not with /*. This will fail at runtime.

IAM Benchmark: 12 scenarios

Winner: GitHub Copilot (89% correct policies)

Critical insight: CodeWhisperer tends to include more permissions than necessary (security risk). Copilot tends toward minimal permissions but sometimes misses required permissions.

CloudFormation Template Generation

CloudFormation YAML requires exact property names and AWS type references. Typos cause deployment failures with confusing error messages.

Test Case: ALB with target group

GitHub Copilot accuracy: 92% (correct property names, minor formatting) Amazon CodeWhisperer accuracy: 94% (slightly cleaner YAML structure)

However, when asking for advanced features (path-based routing, WAF integration), Copilot dropped to 78% while CodeWhisperer maintained 85%.

Full Benchmark: 18 CloudFormation scenarios

Winner: Amazon CodeWhisperer (86% vs 81%)

Cost Comparison for AWS Development

For solo developers or small teams: GitHub Copilot ($10/month) is cheaper. For enterprise with multiple developers: CodeWhisperer Free tier (if sufficient) or CodeWhisperer Individual ($0.34/hour).

Overall Accuracy Summary

Recommendations by Use Case

Choose GitHub Copilot if:

• Primary focus is Lambda/Python development
• IAM policy generation is critical (higher accuracy)
• You need general-purpose code assistance (works for non-AWS code)
• Budget is tight ($10/month is unbeatable)

Choose Amazon CodeWhisperer if:

• Heavy CDK/IaC development
• CloudFormation template generation is frequent
• You’re already in AWS ecosystem (Console integration)
• Your org has Enterprise Support (integrated billing)
• Free tier meets your usage needs

Hybrid approach: Use CodeWhisperer for infrastructure (CDK, CloudFormation) and Copilot for application code (Lambda, policies). Many teams use both.

Practical Metrics for Your Decision

Test each tool with your actual codebase:

1. Generate 10 Lambda functions with both tools
2. Rate accuracy on first-run deployability (no debugging needed)
3. Measure time from prompt to working code
4. Evaluate security group and IAM accuracy

For AWS development specifically, neither tool is perfect. Budget 10-15% code review time regardless of choice. The time savings (70-80% faster development) dwarf tool costs.

Copilot’s accuracy edge on policies tips the scales for security-sensitive work. CodeWhisperer’s infrastructure strength matters for DevOps-heavy teams. Pick based on your primary AWS activity pattern.

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