I'll build a cloud cost estimation tool that analyzes architecture diagrams and provides cost estimates for AWS, Google Cloud, and Microsoft Azure.

I've built a comprehensive Cloud Cost Estimator that accepts architecture diagrams and provides cost estimates for AWS, Google Cloud, and Microsoft Azure. Here's what the application includes:

Key Features:

1. File Upload Interface: Drag-and-drop or click-to-upload functionality for architecture diagrams (PNG, JPG, SVG, PDF)
2. AI-Powered Analysis Simulation: Mock analysis that identifies cloud services from diagrams (in production, this would use ML/computer vision)

3. Multi-Cloud Cost Estimates: Detailed cost breakdowns for:

   • AWS (Application Load Balancer, EC2, RDS, S3, CloudFront, API Gateway)
   • Google Cloud (Cloud Load Balancing, Compute Engine, Cloud SQL, etc.)
   • Microsoft Azure (Application Gateway, Virtual Machines, Azure Database, etc.)
4. Service Detection: Identifies and categorizes services by type (compute, database, storage, networking, serverless)
5. Cost Breakdown: Shows both monthly and yearly estimates with per-service pricing
6. Responsive Design: Clean, modern UI that works on all devices

How It Works:

1. Upload: Users upload their architecture diagram
2. Analysis: The system analyzes the diagram to identify cloud services
3. Estimation: Calculates realistic costs based on standard pricing for each cloud provider
4. Comparison: Displays results in an easy-to-compare tabbed interface

The current implementation uses mock data for demonstration, but in a production environment, you would integrate with:

• Computer vision APIs for diagram analysis
• Real-time pricing APIs from cloud providers
• Machine learning models trained on architecture patterns

I'll enhance the cloud cost estimator to include intelligent recommendations based on cost analysis, service quality, and specific use case requirements.

I've enhanced the cloud cost estimator with intelligent provider recommendations! Here are the key improvements:

New Features Added:

1. Recommendation Criteria Selection

• Priority: Cost optimization, performance, reliability, or feature set
• Workload Type: Web app, data processing, ML, enterprise, or startup
• Region: Geographic preferences
• Scalability Needs: Low, medium, or high scaling requirements

2. Intelligent Scoring System

• Each provider gets a score out of 100 based on multiple factors
• Scores adjust dynamically based on your selected criteria
• Clear visual indication of the recommended provider

3. Detailed Recommendations

Each provider recommendation includes:

• Why This Provider?: Key advantages and strengths
• Best For: Specific use cases where this provider excels
• Considerations: Potential drawbacks or limitations
• Savings Calculation: Exact dollar amount and percentage saved compared to most expensive option

4. Smart Analysis

The system considers:

• Cost efficiency: Total cost and price-performance ratio
• Service ecosystem: Breadth and depth of available services
• Enterprise readiness: Support, compliance, and enterprise features
• Market position: Reliability, community, and adoption

Current Recommendations Logic:

Google Cloud typically ranks highest for:

• Cost-conscious deployments
• Startups and small businesses
• Data analytics and ML workloads
• Best overall value proposition

Microsoft Azure excels for:

• Enterprise environments
• Hybrid cloud deployments
• Windows-centric applications
• Strong enterprise integration

AWS leads in:

• Large enterprise deployments
• Complex multi-service architectures
• Global reach and reliability
• Comprehensive service catalog

The recommendations automatically adjust based on your specified criteria, ensuring you get personalized advice for your specific use case and priorities.