Azure Cost Optimize

This workflow analyzes Infrastructure-as-Code (IaC) files and Azure resources to generate cost optimization recommendations. It creates individual GitHub issues for each optimization opportunity plus one EPIC issue to coordinate implementation, enabling efficient tracking and execution of cost savings initiatives.

Prerequisites

• Azure MCP server configured and authenticated
• GitHub MCP server configured and authenticated
• Target GitHub repository identified
• Azure resources deployed (IaC files optional but helpful)
• Prefer Azure MCP tools (azmcp-*) over direct Azure CLI when available

Workflow Steps

Step 1: Get Azure Best Practices

Action: Retrieve cost optimization best practices before analysis Tools: Azure MCP best practices tool Process:

1. Load Best Practices:
   • Execute azmcp-bestpractices-get to get some of the latest Azure optimization guidelines. This may not cover all scenarios but provides a foundation.
   • Use these practices to inform subsequent analysis and recommendations as much as possible
   • Reference best practices in optimization recommendations, either from the MCP tool output or general Azure documentation

Step 2: Discover Azure Infrastructure

Action: Dynamically discover and analyze Azure resources and configurations Tools: Azure MCP tools + Azure CLI fallback + Local file system access Process:

1. Resource Discovery:

   • Execute azmcp-subscription-list to find available subscriptions
   • Execute azmcp-group-list --subscription <subscription-id> to find resource groups
   • Get a list of all resources in the relevant group(s):
     • Use az resource list --subscription <id> --resource-group <name>
   • For each resource type, use MCP tools first if possible, then CLI fallback:
     • azmcp-cosmos-account-list --subscription <id> - Cosmos DB accounts
     • azmcp-storage-account-list --subscription <id> - Storage accounts
     • azmcp-monitor-workspace-list --subscription <id> - Log Analytics workspaces
     • azmcp-keyvault-key-list - Key Vaults
     • az webapp list - Web Apps (fallback - no MCP tool available)
     • az appservice plan list - App Service Plans (fallback)
     • az functionapp list - Function Apps (fallback)
     • az sql server list - SQL Servers (fallback)
     • az redis list - Redis Cache (fallback)
     • ... and so on for other resource types

2. IaC Detection:

   • Use file_search to scan for IaC files: "/*.bicep", "/*.tf", "/main.json", "/template.json"
   • Parse resource definitions to understand intended configurations
   • Compare against discovered resources to identify discrepancies
   • Note presence of IaC files for implementation recommendations later on
   • Do NOT use any other file from the repository, only IaC files. Using other files is NOT allowed as it is not a source of truth.
   • If you do not find IaC files, then STOP and report no IaC files found to the user.

3. Configuration Analysis:

   • Extract current SKUs, tiers, and settings for each resource
   • Identify resource relationships and dependencies
   • Map resource utilization patterns where available

Step 3: Collect Usage Metrics & Validate Current Costs

Action: Gather utilization data AND verify actual resource costs Tools: Azure MCP monitoring tools + Azure CLI Process:

1. Find Monitoring Sources:

   • Use azmcp-monitor-workspace-list --subscription <id> to find Log Analytics workspaces
   • Use azmcp-monitor-table-list --subscription <id> --workspace <name> --table-type "CustomLog" to discover available data

2. Execute Usage Queries:

   • Use azmcp-monitor-log-query with these predefined queries:
     • Query: "recent" for recent activity patterns
     • Query: "errors" for error-level logs indicating issues
   • For custom analysis, use KQL queries:

   // CPU utilization for App Services
   AppServiceAppLogs
   | where TimeGenerated > ago(7d)
   | summarize avg(CpuTime) by Resource, bin(TimeGenerated, 1h)
   
   // Cosmos DB RU consumption  
   AzureDiagnostics
   | where ResourceProvider == "MICROSOFT.DOCUMENTDB"
   | where TimeGenerated > ago(7d)
   | summarize avg(RequestCharge) by Resource
   
   // Storage account access patterns
   StorageBlobLogs
   | where TimeGenerated > ago(7d)
   | summarize RequestCount=count() by AccountName, bin(TimeGenerated, 1d)
   

3. Calculate Baseline Metrics:

   • CPU/Memory utilization averages
   • Database throughput patterns
   • Storage access frequency
   • Function execution rates

4. VALIDATE CURRENT COSTS:

   • Using the SKU/tier configurations discovered in Step 2
   • Look up current Azure pricing at https://azure.microsoft.com/pricing/ or use az billing commands
   • Document: Resource → Current SKU → Estimated monthly cost
   • Calculate realistic current monthly total before proceeding to recommendations

Step 4: Generate Cost Optimization Recommendations

Action: Analyze resources to identify optimization opportunities Tools: Local analysis using collected data Process:

1. Apply Optimization Patterns based on resource types found:

   Compute Optimizations:

   • App Service Plans: Right-size based on CPU/memory usage
   • Function Apps: Premium → Consumption plan for low usage
   • Virtual Machines: Scale down oversized instances

   Database Optimizations:

   • Cosmos DB:
     • Provisioned → Serverless for variable workloads
     • Right-size RU/s based on actual usage
   • SQL Database: Right-size service tiers based on DTU usage

   Storage Optimizations:

   • Implement lifecycle policies (Hot → Cool → Archive)
   • Consolidate redundant storage accounts
   • Right-size storage tiers based on access patterns

   Infrastructure Optimizations:

   • Remove unused/redundant resources
   • Implement auto-scaling where beneficial
   • Schedule non-production environments

2. Calculate Evidence-Based Savings:

   • Current validated cost → Target cost = Savings
   • Document pricing source for both current and target configurations

3. Calculate Priority Score for each recommendation:

   Priority Score = (Value Score × Monthly Savings) / (Risk Score × Implementation Days)
   
   High Priority: Score > 20
   Medium Priority: Score 5-20
   Low Priority: Score < 5
   

4. Validate Recommendations:

   • Ensure Azure CLI commands are accurate
   • Verify estimated savings calculations
   • Assess implementation risks and prerequisites
   • Ensure all savings calculations have supporting evidence

Step 5: User Confirmation

Action: Present summary and get approval before creating GitHub issues Process:

1. Display Optimization Summary:

   🎯 Azure Cost Optimization Summary
   
   📊 Analysis Results:
   • Total Resources Analyzed: X
   • Current Monthly Cost: $X 
   • Potential Monthly Savings: $Y 
   • Optimization Opportunities: Z
   • High Priority Items: N
   
   🏆 Recommendations:
   1. [Resource]: [Current SKU] → [Target SKU] = $X/month savings - [Risk Level] | [Implementation Effort]
   2. [Resource]: [Current Config] → [Target Config] = $Y/month savings - [Risk Level] | [Implementation Effort]
   3. [Resource]: [Current Config] → [Target Config] = $Z/month savings - [Risk Level] | [Implementation Effort]
   ... and so on
   
   💡 This will create:
   • Y individual GitHub issues (one per optimization)
   • 1 EPIC issue to coordinate implementation
   
   ❓ Proceed with creating GitHub issues? (y/n)
   

2. Wait for User Confirmation: Only proceed if user confirms

Step 6: Create Individual Optimization Issues

Action: Create separate GitHub issues for each optimization opportunity. Label them with "cost-optimization" (green color), "azure" (blue color). MCP Tools Required: create_issue for each recommendation Process:

1. Create Individual Issues using this template:

   Title Format: [COST-OPT] [Resource Type] - [Brief Description] - $X/month savings

   Body Template:

   ## 💰 Cost Optimization: [Brief Title]
   
   **Monthly Savings**: $X | **Risk Level**: [Low/Medium/High] | **Implementation Effort**: X days
   
   ### 📋 Description
   [Clear explanation of the optimization and why it's needed]
   
   ### 🔧 Implementation
   
   **IaC Files Detected**: [Yes/No - based on file_search results]
   
   ```bash
   # If IaC files found: Show IaC modifications + deployment
   # File: infrastructure/bicep/modules/app-service.bicep
   # Change: sku.name: 'S3' → 'B2'
   az deployment group create --resource-group [rg] --template-file infrastructure/bicep/main.bicep
   
   # If no IaC files: Direct Azure CLI commands + warning
   # ⚠️ No IaC files found. If they exist elsewhere, modify those instead.
   az appservice plan update --name [plan] --sku B2
   

   📊 Evidence

   • Current Configuration: [details]
   • Usage Pattern: [evidence from monitoring data]
   • Cost Impact: $X/month → $Y/month
   • Best Practice Alignment: [reference to Azure best practices if applicable]

   ✅ Validation Steps

   • Test in non-production environment
   • Verify no performance degradation
   • Confirm cost reduction in Azure Cost Management
   • Update monitoring and alerts if needed

   ⚠️ Risks & Considerations

   • [Risk 1 and mitigation]
   • [Risk 2 and mitigation]

   Priority Score: X | Value: X/10 | Risk: X/10 ```

Step 7: Create EPIC Coordinating Issue

Action: Create master issue to track all optimization work. Label it with "cost-optimization" (green color), "azure" (blue color), and "epic" (purple color). MCP Tools Required: create_issue for EPIC Note about mermaid diagrams: Ensure you verify mermaid syntax is correct and create the diagrams taking accessibility guidelines into account (styling, colors, etc.). Process:

1. Create EPIC Issue:

   Title: [EPIC] Azure Cost Optimization Initiative - $X/month potential savings

   Body Template:

   # 🎯 Azure Cost Optimization EPIC
   
   **Total Potential Savings**: $X/month | **Implementation Timeline**: X weeks
   
   ## 📊 Executive Summary
   - **Resources Analyzed**: X
   - **Optimization Opportunities**: Y  
   - **Total Monthly Savings Potential**: $X
   - **High Priority Items**: N
   
   ## 🏗️ Current Architecture Overview
   
   ```mermaid
   graph TB
       subgraph "Resource Group: [name]"
           [Generated architecture diagram showing current resources and costs]
       end
   

   📋 Implementation Tracking

   🚀 High Priority (Implement First)

   • #[issue-number]: [Title] - $X/month savings
   • #[issue-number]: [Title] - $X/month savings

   ⚡ Medium Priority

   • #[issue-number]: [Title] - $X/month savings
   • #[issue-number]: [Title] - $X/month savings

   🔄 Low Priority (Nice to Have)

   • #[issue-number]: [Title] - $X/month savings

   📈 Progress Tracking

   • Completed: 0 of Y optimizations
   • Savings Realized: $0 of $X/month
   • Implementation Status: Not Started

   🎯 Success Criteria

   • All high-priority optimizations implemented

   • 80% of estimated savings realized

   • No performance degradation observed
   • Cost monitoring dashboard updated

   📝 Notes

   • Review and update this EPIC as issues are completed
   • Monitor actual vs. estimated savings
   • Consider scheduling regular cost optimization reviews

Error Handling

• Cost Validation: If savings estimates lack supporting evidence or seem inconsistent with Azure pricing, re-verify configurations and pricing sources before proceeding
• Azure Authentication Failure: Provide manual Azure CLI setup steps
• No Resources Found: Create informational issue about Azure resource deployment
• GitHub Creation Failure: Output formatted recommendations to console
• Insufficient Usage Data: Note limitations and provide configuration-based recommendations only

Success Criteria

• ✅ All cost estimates verified against actual resource configurations and Azure pricing
• ✅ Individual issues created for each optimization (trackable and assignable)
• ✅ EPIC issue provides comprehensive coordination and tracking
• ✅ All recommendations include specific, executable Azure CLI commands
• ✅ Priority scoring enables ROI-focused implementation
• ✅ Architecture diagram accurately represents current state
• ✅ User confirmation prevents unwanted issue creation