☁️ Cloud Terraform Generator

AI-Powered Infrastructure-as-Code Generator using Google Gemini API

Generate production-ready Terraform code for AWS, Azure, and GCP in seconds! This tool leverages Google's Gemini AI to create complete, well-structured Terraform configurations based on your requirements.

🌟 Features

• 🎯 Multi-Cloud Support: Generate Terraform code for AWS, Azure, and GCP
• 🤖 AI-Powered: Uses Google Gemini API for intelligent code generation
• 📦 Complete Structure: Generates all necessary Terraform files
• ✨ Best Practices: Follows Terraform and cloud provider best practices
• 🔒 Security-First: Includes security considerations and recommendations
• 📱 Single File: Entire application in one HTML file - no installation needed
• 💾 Export Ready: Copy individual files or download complete bundle
• 🎨 Modern UI: Beautiful, responsive interface with cloud-themed design

📋 Generated Files

The tool generates a complete Terraform project structure:

terraform-project/
├── provider.tf              # Cloud provider configuration
├── variables.tf             # Input variables with descriptions
├── main.tf                  # Main resource definitions
├── outputs.tf               # Output values
├── terraform.tfvars.example # Example variable values
└── README.md                # Project documentation

🚀 Quick Start

Prerequisites

1. Gemini API Key (Free)

   • Visit Google AI Studio
   • Sign in with your Google account
   • Click "Create API Key"
   • Copy the generated key

2. Web Browser

   • Any modern browser (Chrome, Firefox, Safari, Edge)

Usage

1. Open the Tool

   # Simply open the HTML file in your browser
   open terraform-generator.html
   # or double-click the file

2. Enter Your API Key

   • Paste your Gemini API key in the first field

3. Select Cloud Provider

   • Click on AWS, Azure, or GCP card

4. Specify Services

   Examples:
   AWS:   ec2, rds, s3, vpc, lambda
   Azure: vm, sql-database, storage-account, vnet
   GCP:   compute-instance, cloud-sql, storage-bucket, vpc
   

5. Add Requirements (Optional)

   Examples:
   - Use t3.micro for EC2 instances
   - Enable encryption at rest
   - Deploy in us-east-1 region
   - Add tags for production environment
   - Use multi-AZ for RDS
   

6. Generate Code

   • Click "🚀 Generate Terraform Code"
   • Wait for AI to generate your infrastructure code
   • Review, copy, or download the generated files

💡 Example Use Cases

Example 1: Basic AWS Infrastructure

Input:

• Cloud: AWS
• Services: ec2, vpc, security-group
• Requirements: t3.micro instance, allow SSH from my IP

Output: Complete Terraform code with VPC, subnet, security group, and EC2 instance

Example 2: Azure Web Application

Input:

• Cloud: Azure
• Services: app-service, sql-database, storage-account
• Requirements: Standard tier, West Europe region, backup enabled

Output: Full Azure infrastructure for web app with database and storage

Example 3: GCP Data Pipeline

Input:

• Cloud: GCP
• Services: compute-instance, cloud-sql, storage-bucket, pubsub
• Requirements: n1-standard-2, PostgreSQL 14, us-central1

Output: Complete GCP data infrastructure with compute, database, and messaging

🎯 Supported Services

AWS

• Compute: EC2, Lambda, ECS, EKS, Auto Scaling
• Storage: S3, EBS, EFS
• Database: RDS, DynamoDB, ElastiCache, Redshift
• Networking: VPC, Route53, CloudFront, API Gateway
• Security: IAM, KMS, Secrets Manager, WAF

Azure

• Compute: Virtual Machines, App Service, AKS, Functions
• Storage: Storage Account, Blob Storage, File Storage
• Database: SQL Database, Cosmos DB, MySQL, PostgreSQL
• Networking: Virtual Network, Load Balancer, Application Gateway
• Security: Key Vault, Active Directory, Security Center

GCP

• Compute: Compute Engine, GKE, Cloud Functions, Cloud Run
• Storage: Cloud Storage, Persistent Disk
• Database: Cloud SQL, Firestore, Bigtable, Spanner
• Networking: VPC, Cloud Load Balancing, Cloud CDN
• Security: IAM, KMS, Secret Manager

🛠️ Technical Details

Architecture

User Input → Gemini API → AI Processing → Terraform Code Generation → Structured Output

Technology Stack

• Frontend: HTML5, CSS3, Vanilla JavaScript (ES6+)
• AI Model: Google Gemini (2.0-flash, 1.5-flash, 1.5-pro)
• Fonts: Inter (UI), JetBrains Mono (Code)
• API: Google Generative AI SDK

Model Fallback Strategy

The tool attempts multiple Gemini models in order:

1. gemini-2.0-flash-exp (Latest experimental)
2. gemini-2.0-flash (Latest stable)
3. gemini-1.5-flash (Fast and efficient)
4. gemini-1.5-pro (Most capable)

If one model fails, it automatically tries the next one.

📖 Best Practices

Input Tips

1. Be Specific: Include instance types, regions, and sizing requirements
2. Use Standard Names: Stick to official service names (e.g., "ec2" not "virtual machine" for AWS)
3. Add Context: Mention environment (dev/staging/prod) for appropriate configurations
4. Security First: Request encryption, IAM roles, and security groups explicitly

Output Usage

1. Review Generated Code: Always review before applying
2. Customize Variables: Adjust terraform.tfvars.example for your environment
3. Test in Dev First: Deploy to development environment before production
4. Version Control: Commit generated code to Git
5. State Management: Configure remote state (S3, Azure Storage, GCS)

🔒 Security Considerations

• API Key: Your Gemini API key is used only in your browser, never stored
• Generated Code: Review security groups, IAM policies, and encryption settings
• Secrets: Never commit terraform.tfvars with real credentials to Git
• State Files: Use remote state with encryption and access controls

🐛 Troubleshooting

Common Issues

Issue: "All models failed" error

• Solution: Check your API key is valid and has quota remaining
• Visit Google AI Studio to verify

Issue: Generated code has syntax errors

• Solution: Try regenerating with more specific requirements
• Simplify your service list and add requirements one at a time

Issue: Missing services in output

• Solution: Use official service names (check supported services list)
• Be explicit about all required resources

Issue: Code doesn't match cloud provider

• Solution: Ensure you selected the correct cloud provider card
• Verify service names are appropriate for that provider

📚 Learning Resources

Terraform

• Terraform Documentation
• Terraform Registry
• HashiCorp Learn

Cloud Providers

• AWS Terraform Provider
• Azure Terraform Provider
• GCP Terraform Provider

Gemini API

• Google AI Studio
• Gemini API Documentation

🤝 Contributing

Contributions are welcome! Here are some ideas:

• Add support for more cloud providers (Oracle Cloud, DigitalOcean)
• Implement Terraform module generation
• Add syntax highlighting for code blocks
• Create preset templates for common architectures
• Add validation for generated code
• Implement direct GitHub export

📝 License

MIT License - feel free to use this tool for personal or commercial projects.

🙏 Acknowledgments

• Google Gemini: For providing the AI capabilities
• HashiCorp: For creating Terraform
• Cloud Providers: AWS, Azure, and GCP for their excellent documentation

📞 Support

• Issues: Found a bug? Open an issue on GitHub
• Questions: Need help? Check the troubleshooting section
• Feature Requests: Have an idea? Submit a feature request

🗺️ Roadmap

• Add Terraform Cloud integration
• Support for Terragrunt configurations
• Multi-region deployments
• Cost estimation integration
• Terraform plan preview
• Export to GitHub repository
• Template library for common patterns
• Kubernetes manifest generation
• Ansible playbook generation

📊 Version History

v1.0 (Current)

• Initial release
• Multi-cloud support (AWS, Azure, GCP)
• Complete Terraform file generation
• Modern UI with copy/download features
• Model fallback strategy
• Production-ready code generation

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Built with ❤️ for the DevOps community

Making Infrastructure-as-Code accessible to everyone!