Cloud Cost Optimizer

Project Overview

Cloud Cost Optimizer is an automated Azure resource analysis and optimization tool that helps organizations reduce cloud spending through intelligent resource management. By analyzing usage patterns, identifying idle resources, and providing actionable recommendations, the tool has achieved significant cost savings across multiple Azure subscriptions.

Tech Stack

Results & Impact

The Problem

Enterprise Azure environments face several cost management challenges:

• Visibility Gap: No unified view of resource costs across multiple subscriptions and resource groups
• Idle Resources: Developers creating resources for testing and forgetting to delete them, leading to unnecessary spending
• Over-Provisioning: Resources allocated with more capacity than needed based on actual usage patterns
• Manual Analysis: Cost optimization requiring hours of manual Azure Portal exploration and spreadsheet work
• Delayed Action: Cost issues discovered weeks later when bills arrive, missing optimization opportunities

The Solution

An automated system that continuously monitors Azure resources and provides intelligent optimization recommendations:

Automated Resource Discovery

• Scans all subscriptions and resource groups daily
• Catalogs VMs, App Services, databases, storage accounts, and more
• Tracks resource metadata, tags, and configuration
• Builds historical usage database for trend analysis

Intelligent Analysis Engine

• Idle Resource Detection: Identifies VMs with <5% CPU utilization over 7 days
• Right-Sizing Recommendations: Suggests smaller SKUs based on actual usage patterns
• Unused Resource Cleanup: Finds orphaned disks, NICs, and public IPs
• Reserved Instance Opportunities: Recommends RI purchases for consistent workloads
• Storage Tier Optimization: Identifies data suitable for cooler storage tiers

Interactive Dashboard

• Real-time cost breakdown by subscription, resource group, and tag
• Trend charts showing spending over time
• Prioritized recommendation list sorted by potential savings
• One-click resource actions (stop, resize, delete)
• Custom alerts for cost anomalies

Key Features

• Multi-Subscription Support: Analyze costs across unlimited Azure subscriptions from a single dashboard
• Usage Pattern Analysis: Machine learning algorithms identify usage trends and predict future needs
• Automated Recommendations: System generates prioritized list of cost-saving actions
• Forecasting: Predict next month's spending based on current trends
• Budget Alerts: Get notified when spending approaches defined thresholds
• Tag-Based Analysis: Break down costs by project, environment, or department using tags
• Export Reports: Generate PDF/Excel reports for stakeholders
• API Integration: REST API for programmatic access to recommendations

Technical Highlights

Azure SDK Integration

Leverages multiple Azure SDK clients for comprehensive resource analysis:

• Resource Management: Lists and inspects all resource types
• Monitor Client: Retrieves metrics (CPU, memory, network) for usage analysis
• Cost Management: Accesses billing data and cost breakdowns
• Advisor Client: Integrates native Azure Advisor recommendations

Efficient Data Pipeline

Handles large-scale resource scanning efficiently:

1. Parallel subscription scanning using Python asyncio
2. Incremental updates - only fetch changed resources
3. PostgreSQL for historical data with optimized indexes
4. Redis caching for frequently accessed data
5. Background workers for metric collection

Recommendation Engine

Rule-based system with customizable thresholds:

• Idle VM detection: CPU <5%, Network <1MB/day for 7 days
• Right-sizing: 30-day usage analysis with 20% headroom buffer
• Orphaned resources: Unattached disks/NICs older than 30 days
• RI opportunities: Consistent usage >70% for 90+ days

Challenges & Solutions

Challenge 1: Rate Limiting

Problem: Azure APIs have strict rate limits. Scanning 12 subscriptions with 850+ resources was hitting throttling limits.

Solution: Implemented exponential backoff retry logic and distributed requests over time. Added intelligent caching to reduce redundant API calls by 80%.

Challenge 2: Metric Granularity

Problem: Azure Monitor metrics have retention limits and sampling rates that made detailed analysis difficult.

Solution: Built local metric aggregation system that stores daily summaries in PostgreSQL. This provides long-term trend analysis without hitting API limits.

Challenge 3: Complex Permissions

Problem: Different Azure subscriptions had different RBAC configurations, causing access denied errors.

Solution: Graceful degradation - continue analyzing accessible resources while logging permission issues. Dashboard shows permission gaps for administrators to fix.

Future Enhancements

• Automated Actions: Allow approved recommendations to execute automatically (with safeguards)
• Multi-Cloud Support: Extend to AWS and GCP for unified cloud cost management
• Machine Learning: Use ML to predict anomalies and detect unusual spending patterns
• Slack/Teams Integration: Send daily digest of recommendations to team channels
• Cost Allocation: Chargeback reports for departmental billing
• What-If Analysis: Model cost impact of architecture changes before implementation