AWS Deploy AI - AI-Powered AWS Deployment Platform (Monorepo)

🏗️ Monorepo Architecture

This monorepo contains two main packages:

• @aws-deploy-ai/mcp-server: Model Context Protocol server with AWS Bedrock integration
• @aws-deploy-ai/frontend: Next.js web interface for the deployment platform

🚀 Quick Start

1. Environment Setup

# Copy environment variables
cd packages/frontend
cp .env.local.example .env.local

2. GitHub OAuth Setup

1. Go to GitHub Settings > Developer Settings > OAuth Apps
2. Create a new OAuth App with:
   • Application name: AWS Deploy AI
   • Homepage URL: http://localhost:3000
   • Authorization callback URL: http://localhost:3000/api/auth/github/callback
3. Copy the Client ID and Client Secret to your .env.local file

3. Start Development

# Install all dependencies and build MCP server
npm run install-all

# Start development environment (both packages)
npm run dev

# Access the web interface at http://localhost:3000

4. Connect GitHub

1. Click "Connect GitHub" on the main page
2. Authorize the application
3. Your repositories will be automatically loaded

🚀 Deploy websites and applications to AWS using simple, natural language prompts

No cloud expertise required. Just describe what you want, and watch your infrastructure come to life.

---

🌟 Features

🤖 AI-Powered Deployment

• Natural Language Processing: Describe your deployment in plain English
• Intelligent Infrastructure Recommendations: AI analyzes your needs and suggests optimal AWS services
• Cost Optimization: Automatic selection of cost-effective resources
• Smart Defaults: Production-ready configurations out of the box

☁️ Comprehensive AWS Integration

• Static Websites: S3 + CloudFront for lightning-fast static sites
• Single Page Applications: Optimized SPA deployment with proper routing
• Serverless APIs: Lambda functions with API Gateway integration
• Full-Stack Applications: Complete infrastructure orchestration
• Custom Domains: Automatic SSL certificate provisioning and DNS setup

📊 Real-Time Monitoring

• Live Deployment Progress: Watch your infrastructure being created in real-time
• Cost Tracking: Transparent cost estimates and monitoring
• Resource Management: View and manage all deployed resources
• Health Monitoring: Automatic health checks and alerts

🎯 Developer-Friendly

• Model Context Protocol (MCP): Extensible architecture for easy integration
• TypeScript: Full type safety throughout the codebase
• Modern Stack: Next.js frontend with Tailwind CSS
• Comprehensive Logging: Detailed deployment logs and error tracking

---

🚀 Quick Start

Prerequisites

# Node.js 18+ required
node --version  # Should be 18.0.0 or higher

# AWS CLI configured
aws configure
# OR set environment variables:
# AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION

# Ensure AWS Bedrock access is enabled in your region

Installation

1. Clone the repository

git clone https://github.com/DheerajShrivastav/aws-deploy-ai.git
cd aws-deploy-ai

2. Set up the MCP Server

cd mcp-server
npm install
cp .env.example .env
# Edit .env with your AWS credentials
npm run build

3. Set up the Frontend (Optional)

cd ../frontend
npm install
npm run dev  # Runs on http://localhost:3000

4. Start the MCP Server

cd ../mcp-server
npm run dev  # For development
# OR
npm start    # For production

---

💻 Usage Examples

Basic Static Website

"Deploy my portfolio website with my resume and projects"

React Application

"Deploy my React app with custom domain myportfolio.com and SSL certificate"

Blog Site

"Create a blog website with fast loading and SEO optimization"

E-commerce Store

"Deploy my online store with product catalog and payment processing"

API Backend

"Set up a serverless API for my mobile app with database integration"

---

🏗️ Architecture

System Overview

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │   MCP Server    │    │   AWS Services  │
│   (Next.js)     │────│   (TypeScript)  │────│   (Auto-Deploy) │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                        │                        │
         │              ┌─────────────────┐                │
         └──────────────│  AI Interpreter │────────────────┘
                        │ (AWS Bedrock)   │
                        └─────────────────┘

Core Components

🧠 AI Interpreter Service

• Parses natural language deployment requests
• Generates infrastructure requirements
• Provides cost estimates and recommendations
• Powered by AWS Bedrock (Claude 3) for intelligent analysis

⚙️ AWS Service Orchestration

• S3 Service: Static website hosting and asset storage
• CloudFront Service: Global CDN distribution
• Lambda Service: Serverless function deployment
• IAM Service: Automated security policy creation
• Route53 Service: DNS and domain management

🔄 Deployment Workflow Engine

• State machine for deployment orchestration
• Real-time progress tracking
• Error handling and rollback capabilities
• Resource lifecycle management

---

🛠️ Configuration

Environment Variables

Create a .env file in the mcp-server directory:

# AWS Configuration
AWS_REGION=us-east-1
AWS_ACCESS_KEY_ID=your_access_key_here
AWS_SECRET_ACCESS_KEY=your_secret_key_here

# AWS Bedrock Configuration
BEDROCK_MODEL_ID=anthropic.claude-3-sonnet-20240229-v1:0
BEDROCK_REGION=us-east-1

# Deployment Configuration
DEFAULT_BUCKET_PREFIX=aws-deploy-ai
DEFAULT_CLOUDFRONT_PRICE_CLASS=PriceClass_100

# Logging Configuration
LOG_LEVEL=info
LOG_FILE=./logs/aws-deploy-ai.log

# Security Configuration
ENCRYPTION_KEY=your_encryption_key_here

AWS IAM Permissions

Your AWS user/role needs the following permissions:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:*",
        "cloudfront:*",
        "lambda:*",
        "iam:CreateRole",
        "iam:AttachRolePolicy",
        "iam:PassRole",
        "route53:*",
        "acm:*",
        "apigateway:*",
        "logs:*"
      ],
      "Resource": "*"
    }
  ]
}

---

📡 API Reference

MCP Tools

deploy-website

Deploy a website or application to AWS.

Parameters:

• prompt (string): Natural language description of what to deploy
• projectName (string, optional): Name of the project
• customDomain (string, optional): Custom domain name
• environment (string, optional): deployment environment (development/staging/production)

Example:

await mcpClient.callTool('deploy-website', {
  prompt: 'Deploy my React portfolio with contact form',
  projectName: 'my-portfolio',
  customDomain: 'johnsmith.dev',
  environment: 'production',
})

get-deployment-status

Check the status of a deployment.

Parameters:

• deploymentId (string): The deployment ID to check

analyze-deployment

Analyze deployment requirements without deploying.

Parameters:

• prompt (string): Deployment requirements to analyze

get-cost-estimate

Get detailed cost estimates for a deployment.

Parameters:

• prompt (string): Deployment requirements for cost estimation

---

🎯 Supported Project Types

Project Type	Description	AWS Services Used
Static Website	HTML, CSS, JS files	S3, CloudFront, Route53
Single Page App	React, Vue, Angular apps	S3, CloudFront, Route53
Blog	Content-focused websites	S3, CloudFront, Lambda
E-commerce	Online stores	S3, CloudFront, Lambda, DynamoDB
API Backend	RESTful APIs	Lambda, API Gateway, DynamoDB
Full-Stack App	Complete web applications	All services
Documentation	Documentation sites	S3, CloudFront
Portfolio	Personal/professional portfolios	S3, CloudFront, Route53

---

💰 Cost Estimation

Typical Monthly Costs

Project Type	Small Traffic	Medium Traffic	High Traffic
Static Website	$2-5	$10-25	$50-100
SPA	$3-8	$15-35	$75-150
API Backend	$5-15	$25-75	$100-300
Full-Stack App	$10-30	$50-150	$200-500

Costs include S3 storage, CloudFront distribution, Lambda execution, and data transfer. Actual costs may vary based on usage patterns.

Cost Optimization Features

• Intelligent Resource Sizing: AI selects optimal resource configurations
• Free Tier Utilization: Maximizes use of AWS free tier benefits
• Caching Optimization: CloudFront caching reduces data transfer costs
• Serverless-First: Uses cost-effective serverless services when possible

---

🔧 Development

Project Structure

aws-deploy-ai/
├── mcp-server/                 # MCP Server (TypeScript)
│   ├── src/
│   │   ├── main.ts            # Server entry point
│   │   ├── services/          # AWS service integrations
│   │   ├── tools/             # MCP tool implementations
│   │   ├── types/             # TypeScript type definitions
│   │   └── utils/             # Utility functions
│   ├── package.json
│   └── tsconfig.json
│
├── frontend/                   # Next.js Frontend (Optional)
│   ├── src/app/               # Next.js App Router
│   ├── components/            # React components
│   ├── package.json
│   └── tailwind.config.js
│
└── docs/                      # Documentation
    ├── api.md                 # API documentation
    ├── deployment-guide.md    # Deployment guide
    └── examples/              # Usage examples

Running Tests

# MCP Server tests
cd mcp-server
npm test

# Frontend tests
cd frontend
npm test

Building for Production

# Build MCP Server
cd mcp-server
npm run build

# Build Frontend
cd frontend
npm run build

---

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes
4. Add tests for new functionality
5. Run tests: npm test
6. Commit changes: git commit -m 'Add amazing feature'
7. Push to branch: git push origin feature/amazing-feature
8. Open a Pull Request

---

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

---

🙏 Acknowledgments

• Model Context Protocol (MCP): For providing the extensible server framework
• AWS Bedrock: For powering the intelligent deployment analysis with Claude 3
• AWS: For providing the robust cloud infrastructure platform
• Next.js & Tailwind CSS: For the beautiful and responsive frontend

---

📞 Support

• Documentation: Full Documentation
• GitHub Issues: Report Bugs or Request Features
• Discussions: Community Discussions

---

Made with ❤️ for developers who want to focus on building, not configuring infrastructure.

Deploy with AI. Scale with AWS. Build without limits.