az-cost-optimize

name	az-cost-optimize
description	Analyze Azure resources used in the app (IaC files and/or resources in a target rg) and optimize costs - creating GitHub issues for identified optimizations.

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.

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

Execute azmcp-bestpractices-get to get the latest Azure optimization guidelines. Reference these in optimization recommendations.

Step 2: Discover Azure Infrastructure

Resource Discovery:

• Execute azmcp-subscription-list to find available subscriptions
• Execute azmcp-group-list --subscription <subscription-id> to find resource groups
• Use az resource list --subscription <id> --resource-group <name> to list all resources
• Use MCP tools first when available, 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

IaC Detection:

• Scan for IaC files: **/*.bicep, **/*.tf, **/main.json, **/*template*.json
• If no IaC files found, STOP and report to user
• Do NOT use non-IaC files from the repository

Step 3: Collect Usage Metrics

Execute KQL queries via azmcp-monitor-log-query:

// 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

Validate Current Costs:

• Look up current Azure pricing at https://azure.microsoft.com/pricing/
• Document: Resource → Current SKU → Estimated monthly cost

Step 4: Generate Cost Optimization Recommendations

Compute Optimizations:

• App Service Plans: Right-size based on CPU/memory usage
• Function Apps: Premium → Consumption plan for low usage

Database Optimizations:

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

Storage Optimizations:

• Implement lifecycle policies (Hot → Cool → Archive)
• Consolidate redundant storage accounts

Calculate Priority Score:

Priority Score = (Value Score × Monthly Savings) / (Risk Score × Implementation Days)

High Priority: Score > 20
Medium Priority: Score 5-20
Low Priority: Score < 5

Step 5: User Confirmation

Present summary before creating GitHub issues:

• Total Resources Analyzed
• Current Monthly Cost
• Potential Monthly Savings
• Wait for confirmation before creating issues

Step 6: Create Individual Optimization Issues

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

Label issues with "cost-optimization" (green) and "azure" (blue).

Issue body includes:

• Description of optimization
• Implementation steps (IaC modifications + Azure CLI commands)
• Evidence (current config, usage pattern, cost impact)
• Validation steps
• Risks and considerations

Step 7: Create EPIC Coordinating Issue

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

Label with "cost-optimization", "azure", and "epic".

Epic includes:

• Executive Summary
• Architecture overview (Mermaid diagram)
• Implementation tracking with issue links
• Progress tracking
• Success criteria

Error Handling

• No IaC files found: Report and stop
• Azure Authentication Failure: Provide Azure CLI setup steps
• No Resources Found: Create informational issue

Success Criteria

• All cost estimates verified against actual resource configurations
• Individual issues created for each optimization
• EPIC issue provides comprehensive coordination
• All recommendations include executable Azure CLI commands