💰 Azure Cost Optimizer

An intelligent Python tool that analyzes your Azure subscription, identifies cost-saving opportunities, and provides actionable recommendations. Save up to 40% on your Azure bill with automated insights.

✨ Features

Feature	Description
📊 Comprehensive Cost Analysis	Analyze spending by resource group, service, and location
💡 Smart Recommendations	AI-driven suggestions for cost optimization
📈 Trend Analysis	Track spending trends and predict future costs
🎨 Visual Reports	Generate PDF, Excel, and JSON reports
📧 Automated Alerts	Email notifications for budget overruns
🔄 Scheduled Scans	Continuous monitoring with configurable schedules
🐳 Docker Support	Run anywhere with containerized deployment

Optimization Categories

• 🖥️ Idle Resources: Find unused VMs, disks, and public IPs
• 📐 Right-sizing: Identify oversized VMs based on actual metrics
• 💵 Reserved Instances: Calculate savings with RI purchases
• 💾 Storage Optimization: Detect expensive storage tiers
• 🗑️ Snapshot Cleanup: Identify outdated snapshots and backups
• 🗄️ Database Optimization: Find idle or oversized SQL databases

---

🚀 Quick Start

Prerequisites

• Python 3.11+ (Download)
• Azure subscription with Cost Management access
• Azure CLI or Service Principal credentials

Installation

# Clone repository
git clone https://github.com/rchamoli/azure-cost-optimizer.git
cd azure-cost-optimizer

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install package
pip install -e .

# Or install with development dependencies
pip install -e ".[dev]"

Configuration

# Copy environment template
cp .env.example .env

# Edit with your Azure credentials
nano .env  # or use your preferred editor

Required environment variables:

AZURE_SUBSCRIPTION_ID=your-subscription-id
AZURE_TENANT_ID=your-tenant-id
AZURE_CLIENT_ID=your-client-id
AZURE_CLIENT_SECRET=your-client-secret

💡 Tip: You can also use Azure CLI authentication. If logged in via az login, credentials are picked up automatically.

Usage

# Basic analysis with console output
python cost_optimizer.py analyze

# Or use the installed CLI
azure-cost-optimizer analyze

# Analyze specific resource group
azure-cost-optimizer analyze --resource-group myResourceGroup

# Generate PDF report
azure-cost-optimizer analyze --format pdf --output report.pdf

# Analyze last 90 days
azure-cost-optimizer analyze --days 90

# Send report via email
azure-cost-optimizer analyze --email

# Validate Azure connection
azure-cost-optimizer validate

# Show current configuration
azure-cost-optimizer config

---

📊 Sample Output

Console Output

╔════════════════════════════════════════════════════════════╗
║           Azure Cost Optimization Report                   ║
║                  January 2024                              ║
╚════════════════════════════════════════════════════════════╝

📊 Cost Summary:
   Current Month:     $15,234.56
   Previous Month:    $14,890.23
   Change:            +2.3% ($344.33)
   Projected (EOY):   $182,814.72

💡 Potential Savings: $4,521.00 per month (29.7%)

🎯 Top Recommendations:

1. Stop Idle Virtual Machines (8 found)
   💰 Estimated Savings: $1,840/month
   📍 Resources: vm-dev-01, vm-test-03, vm-staging-02...

2. Delete Unattached Disks (15 found)
   💰 Estimated Savings: $325/month

3. Right-size Oversized VMs (5 found)
   💰 Estimated Savings: $1,156/month

4. Purchase Reserved Instances
   💰 Estimated Savings: $1,200/month

---

🏗️ Project Structure

azure-cost-optimizer/
├── src/azure_cost_optimizer/    # Main package
│   ├── analyzers/               # Cost analysis modules
│   │   ├── compute.py           # VM and disk analysis
│   │   ├── storage.py           # Storage analysis
│   │   ├── network.py           # Network analysis
│   │   └── database.py          # Database analysis
│   ├── recommenders/            # Recommendation engines
│   │   └── reserved_instances.py
│   ├── reporters/               # Report generators
│   │   ├── console.py           # Terminal output
│   │   ├── pdf.py               # PDF generation
│   │   ├── excel.py             # Excel export
│   │   └── email.py             # Email reports
│   ├── utils/                   # Utilities
│   │   ├── azure_client.py      # Azure SDK wrapper
│   │   ├── cost_calculator.py   # Cost calculations
│   │   └── metrics.py           # Metrics collection
│   ├── cli.py                   # CLI commands
│   ├── config.py                # Configuration
│   ├── models.py                # Data models
│   └── optimizer.py             # Main orchestrator
├── tests/                       # Test suite
├── .github/workflows/           # CI/CD pipelines
├── pyproject.toml               # Project configuration
├── Dockerfile                   # Container image
└── docker-compose.yml           # Docker Compose setup

---

⚙️ Configuration

All configuration is done via environment variables. See .env.example for the full list.

Core Settings

Variable	Description	Default
AZURE_SUBSCRIPTION_ID	Azure subscription to analyze	Required
AZURE_TENANT_ID	Azure AD tenant ID	Optional*
AZURE_CLIENT_ID	Service principal client ID	Optional*
AZURE_CLIENT_SECRET	Service principal secret	Optional*
ANALYSIS_DAYS	Days to analyze	30
DEFAULT_OUTPUT_FORMAT	Output format	console

*Optional if using Azure CLI authentication

Threshold Settings

Variable	Description	Default
IDLE_VM_CPU_PERCENT	CPU % to consider VM idle	5
IDLE_VM_DAYS	Days of idle before flagging	7
SNAPSHOT_AGE_DAYS	Age to flag old snapshots	90
MIN_SAVINGS_THRESHOLD	Minimum savings to report	10

Email Settings

Variable	Description	Default
EMAIL_ENABLED	Enable email reports	false
SMTP_SERVER	SMTP server hostname	smtp.gmail.com
SMTP_PORT	SMTP port	587
EMAIL_FROM	Sender email address	-
EMAIL_TO	Recipients (comma-separated)	-

---

🐳 Docker Deployment

Build and Run

# Build image
docker build -t azure-cost-optimizer .

# Run analysis
docker run --env-file .env azure-cost-optimizer analyze

# Generate PDF report (mount volume for output)
docker run --env-file .env -v $(pwd)/reports:/app/reports \
  azure-cost-optimizer analyze --format pdf --output /app/reports/report.pdf

Docker Compose

# Run one-time analysis
docker compose run --rm optimizer analyze

# Start scheduled analyzer (weekly)
docker compose --profile scheduled up -d scheduler

---

🧪 Development

Setup

# Install with dev dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=src/azure_cost_optimizer --cov-report=html

# Run specific test file
pytest tests/test_models.py -v

Linting & Formatting

# Run linter
ruff check src/ tests/

# Auto-fix issues
ruff check --fix src/ tests/

# Format code
ruff format src/ tests/

# Type checking
mypy src/azure_cost_optimizer

---

🔐 Security

Required Azure Permissions

The service principal needs these roles:

Role	Purpose
Cost Management Reader	Read cost data
Reader	Read resource metadata
Monitoring Reader	Read performance metrics

Grant Permissions

# Create service principal with required roles
az ad sp create-for-rbac --name "cost-optimizer-sp" \
  --role "Cost Management Reader" \
  --scopes /subscriptions/{subscription-id}

# Assign additional roles
az role assignment create \
  --assignee {service-principal-id} \
  --role "Reader" \
  --scope /subscriptions/{subscription-id}

az role assignment create \
  --assignee {service-principal-id} \
  --role "Monitoring Reader" \
  --scope /subscriptions/{subscription-id}

Security Best Practices

• ✅ Never commit .env or credentials to git
• ✅ Use Azure Managed Identity in production
• ✅ Rotate service principal secrets regularly
• ✅ Use least-privilege access (read-only roles)
• ✅ Enable audit logging for the service principal
• ✅ Store secrets in Azure Key Vault for production

---

🚢 Production Deployment

Production Checklist

• Use Azure Managed Identity instead of service principal secrets
• Deploy to Azure Container Instances or Azure Functions
• Enable Azure Monitor for container logs
• Set up budget alerts in Azure Cost Management
• Configure email notifications for stakeholders
• Schedule weekly/monthly analysis runs
• Store reports in Azure Blob Storage
• Enable RBAC for report access

Azure Container Instance Deployment

# Deploy to ACI
az container create \
  --resource-group myResourceGroup \
  --name cost-optimizer \
  --image ghcr.io/rchamoli/azure-cost-optimizer:latest \
  --assign-identity \
  --restart-policy OnFailure \
  --environment-variables \
    AZURE_SUBSCRIPTION_ID=your-sub-id \
    LOG_LEVEL=INFO

Azure Functions Deployment

# Deploy as Azure Function (timer-triggered)
func azure functionapp publish YourFunctionApp

---

🔧 Troubleshooting

Common Issues

Authentication Failed

Error: DefaultAzureCredential failed to retrieve a token

Solutions:

1. Verify Azure CLI is logged in: az account show
2. Check service principal credentials in .env
3. Ensure tenant ID is correct
4. Run azure-cost-optimizer validate to test connection

Cost Data Unavailable

Warning: Cost Management API access limited

Solutions:

1. Ensure Cost Management Reader role is assigned
2. Cost data may take 24-48 hours to appear for new subscriptions
3. Check if Cost Management is enabled for the subscription

No Recommendations Found

Possible causes:

1. All resources are already optimized
2. Metrics data not available (wait 7+ days)
3. Thresholds too strict - adjust in .env
4. Resource group filter too narrow

---

📈 Programmatic Usage

from azure_cost_optimizer import CostOptimizer

# Create optimizer instance
optimizer = CostOptimizer(subscription_id="your-sub-id")

# Run analysis
result = optimizer.analyze(days=30)

# Get recommendations
recommendations = optimizer.get_recommendations(limit=10)

# Calculate total savings
total_savings = optimizer.calculate_potential_savings()
print(f"Potential savings: ${total_savings}/month")

# Generate reports
optimizer.generate_report(format="pdf", output="report.pdf")
optimizer.generate_report(format="json", output="data.json")

# Export to JSON
optimizer.export_to_json("analysis.json")

# Send email report
optimizer.send_email(attachment_format="pdf")

# Clean up
optimizer.close()

---

🛣️ Roadmap

• Multi-subscription support
• AWS and GCP cost analysis
• Machine learning for anomaly detection
• Slack/Teams integration
• Custom policies and rules engine
• Historical trend analysis (12+ months)
• What-if scenario modeling
• Integration with Azure DevOps
• Terraform cost estimation
• Real-time cost alerts

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🤝 Contributing

Contributions are welcome! Please see our Contributing Guide for details.

Areas Needing Help

• Additional analyzers for Azure services (AKS, App Service, etc.)
• Enhanced visualization options
• Performance optimizations
• Documentation improvements
• Integration tests

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📄 License

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

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👤 Author

Rahul Chamoli - Cloud Engineer & Cost Optimization Specialist

• 💼 LinkedIn
• 🐙 GitHub

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🙏 Acknowledgments

• Azure Cost Management Team
• Python Azure SDK contributors
• Open-source reporting libraries (ReportLab, Pandas, Rich)

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📚 Resources

• Azure Cost Management Documentation
• Azure SDK for Python
• Azure Well-Architected Framework - Cost Optimization
• FinOps Foundation

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💰 Save money, optimize resources, sleep better!

⭐ Star this repo if it helps you reduce your Azure costs!