az-cost-optimize

name	az-cost-optimize
description	Analizar recursos de Azure usados en la aplicacion (archivos IaC y/o recursos en un rg objetivo) y optimizar costes, creando GitHub issues para las optimizaciones identificadas.

Azure Cost Optimize

Este flujo analiza archivos Infrastructure-as-Code (IaC) y recursos de Azure para generar recomendaciones de optimizacion de costes. Crea issues individuales de GitHub para cada oportunidad de optimizacion y un issue EPIC para coordinar la implementacion, facilitando el seguimiento y la ejecucion de iniciativas de ahorro.

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