Az Cost Optimize

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 to find resource groups
• Get a list of all resources in the relevant group(s):
• Use az resource list --subscription --resource-group
• For each resource type, use MCP tools first if possible, then CLI fallback:
• azmcp-cosmos-account-list --subscription - Cosmos DB accounts
• azmcp-storage-account-list --subscription - Storage accounts
• azmcp-monitor-workspace-list --subscription - 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

1. 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.

1. 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 to find Log Analytics workspaces
• Use azmcp-monitor-table-list --subscription --workspace --table-type "CustomLog" to discover available data

1. 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:

```kql // 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) ```

1. Calculate Baseline Metrics:

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

1. 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

1. Calculate Evidence-Based Savings:

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

1. 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 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: github
• Source: github/awesome-copilot
• License: MIT
• Homepage: https://awesome-copilot.github.com/

Install and usage instructions live in the source repository linked above.