mcpbeat Sign in

Azure Cost Estimator Agent Skill

Estimate monthly costs for Azure resources by querying the Azure Retail Prices API. Parses ARM templates to identify resources, SKUs, and regions, then looks up real retail pricing. Produces a per-resource cost breakdown with monthly totals. Use during template generation or when user asks about costs.

3k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
265
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/Azure/git-ape --skill azure-cost-estimator

The instruction itself

26 sections, as written by the author

Azure Cost Estimator

Estimate monthly costs for Azure resources using the Azure Retail Prices API — a free, unauthenticated REST API that returns real Microsoft retail pricing.

When to Use

  • During template generation (Stage 2) to show cost estimates before deployment
  • When user asks "how much will this cost?" or "estimate costs"
  • To compare cost of different SKU options
  • To validate budget constraints before deployment

API Reference

Endpoint: https://prices.azure.com/api/retail/prices

Key facts:

  • No authentication required
  • OData $filter for targeted queries
  • Filter values are case-sensitive (e.g., 'Virtual Machines' not 'virtual machines')
  • Returns max 1,000 records per page (use NextPageLink for pagination)
  • Currency defaults to USD; override with currencyCode='EUR' etc.
  • Always filter for priceType eq 'Consumption' and isPrimaryMeterRegion eq true unless looking for reservations

Filterable fields: armRegionName, serviceName, armSkuName, meterName, productName, skuName, serviceFamily, priceType

Procedure

1. Parse ARM Template Resources

Extract from the ARM template:

  • Resource type (Microsoft.Compute/virtualMachines, Microsoft.Storage/storageAccounts, etc.)
  • SKU/size (e.g., Standard_B1ls, Standard_LRS)
  • Region (armRegionName value, e.g., southeastasia, eastus)
  • Any quantity-affecting properties (disk size, number of instances, reserved capacity)

2. Map Resource Types to Pricing API Queries

Use the mapping table below to construct the correct API filter for each resource type. Run each query using curl in the terminal.

Query pattern:

curl -s "https://prices.azure.com/api/retail/prices?\$filter=<FILTER>" | python3 -c "
import sys, json
data = json.load(sys.stdin)
for item in data.get('Items', []):
    print(f\"{item['meterName']:30s} {item['retailPrice']:>10.6f} {item['unitOfMeasure']:15s} {item['productName']}\")
"

Resource Type Mapping

Virtual Machines (Microsoft.Compute/virtualMachines)
serviceName eq 'Virtual Machines'
and armRegionName eq '{region}'
and armSkuName eq '{vmSize}'
and priceType eq 'Consumption'
and contains(productName, 'Linux')      # or 'Windows' based on osProfile

Pick the result where meterName matches the SKU base name (e.g., B1ls for Standard_B1ls).

Unit: 1 Hour → multiply by 730 for monthly estimate.

OS detection from ARM template:

  • osProfile.linuxConfiguration present → Linux
  • osProfile.windowsConfiguration present → Windows
Managed Disks (Microsoft.Compute/disks or implicit VM OS disk)
serviceName eq 'Storage'
and armRegionName eq '{region}'
and meterName eq '{diskTier} LRS Disk'    # e.g., 'P4 LRS Disk', 'S4 LRS Disk'
and priceType eq 'Consumption'

Disk tier mapping (from diskSizeGB or sku.name):

| ARM sku.name | Prefix | Sizes |

|----------------|--------|-------|

| Premium_LRS | P | P4 (32GB), P6 (64GB), P10 (128GB), P15 (256GB), P20 (512GB), P30 (1TB) |

| StandardSSD_LRS | E | E4, E6, E10, E15, E20, E30 |

| Standard_LRS | S | S4, S6, S10, S15, S20, S30 |

If VM uses osDisk.managedDisk.storageAccountType → use that to determine the tier.

Unit: 1/Month → use directly.

Storage Accounts (Microsoft.Storage/storageAccounts)
serviceName eq 'Storage'
and armRegionName eq '{region}'
and skuName eq '{redundancy}'            # e.g., 'Standard LRS', 'Standard GRS'
and meterName eq 'LRS Data Stored'       # or 'GRS Data Stored'
and productName eq 'Blob Storage'
and priceType eq 'Consumption'

Unit: 1 GB/Month → estimate based on expected storage. Use 10 GB as default if unknown.

Also add transaction costs: search for meterName eq 'Write Operations' (per 10,000 ops).

Function Apps (Microsoft.Web/sites with kind: functionapp)

Consumption plan:

serviceName eq 'Functions'
and armRegionName eq '{region}'
and priceType eq 'Consumption'

Key meters:

  • Execution Time — per GB-s ($0.000016/GB-s, first 400,000 GB-s/month free)
  • Total Executions — per execution ($0.20/million, first 1M/month free)

Dedicated plan: Price the App Service Plan instead (see below).

App Service Plans (Microsoft.Web/serverfarms)
serviceName eq 'Azure App Service'
and armRegionName eq '{region}'
and armSkuName eq '{skuName}'            # e.g., 'B1', 'S1', 'P1v3'
and priceType eq 'Consumption'

Unit: 1 Hour → multiply by 730 for monthly.

SQL Database (Microsoft.Sql/servers/databases)

DTU model:

serviceName eq 'SQL Database'
and armRegionName eq '{region}'
and meterName eq '{tier} DTUs'           # e.g., 'Basic DTUs', 'S1 DTUs'
and priceType eq 'Consumption'

vCore model:

serviceName eq 'SQL Database'
and armRegionName eq '{region}'
and skuName eq '{tier}'
and priceType eq 'Consumption'
Cosmos DB (Microsoft.DocumentDB/databaseAccounts)
serviceName eq 'Azure Cosmos DB'
and armRegionName eq '{region}'
and meterName eq '100 RU/s'             # or 'Autoscale - 100 RU/s'
and priceType eq 'Consumption'

Unit: 1 Hour per 100 RU/s → multiply by 730 × (provisioned RU/s ÷ 100).

Storage: search meterName eq '1 GB Data Stored'.

Public IP (Microsoft.Network/publicIPAddresses)
serviceName eq 'Virtual Network'
and armRegionName eq '{region}'
and meterName eq 'Static Public IP'      # or 'Dynamic Public IP' or 'Basic IPv4 Static Public IP Address'
and priceType eq 'Consumption'

Unit: 1 Hour → multiply by 730.

Application Insights (Microsoft.Insights/components)
serviceName eq 'Azure Monitor'
and armRegionName eq '{region}'
and meterName eq 'Data Ingestion'
and priceType eq 'Consumption'

Unit: 1 GB — first 5 GB/month free. Estimate 1 GB/month for dev, 5-10 GB for prod.

Key Vault (Microsoft.KeyVault/vaults)
serviceName eq 'Key Vault'
and armRegionName eq '{region}'
and priceType eq 'Consumption'

Key meters: Operations (per 10,000), Certificate Renewals, HSM Key Operations.

Typically < $1/month for dev workloads.

Log Analytics Workspace (Microsoft.OperationalInsights/workspaces)
serviceName eq 'Azure Monitor'
and armRegionName eq '{region}'
and meterName eq 'Pay-as-you-go Data Ingestion'
and priceType eq 'Consumption'

Unit: 1 GB — first 5 GB/day free on pay-as-you-go tier.

Network Security Group / Virtual Network / NIC

These resources are free — no pricing API query needed. Note this in the output:

Network Security Group     $0.00/month   (no charge)
Virtual Network            $0.00/month   (no charge)
Network Interface          $0.00/month   (no charge)

3. Query the API

For each resource, run the constructed query. Use curl with proper URL encoding:

curl -s "https://prices.azure.com/api/retail/prices?\$filter=serviceName%20eq%20%27Virtual%20Machines%27%20and%20armRegionName%20eq%20%27southeastasia%27%20and%20armSkuName%20eq%20%27Standard_B1ls%27%20and%20priceType%20eq%20%27Consumption%27" \
  | python3 -c "
import sys, json
data = json.load(sys.stdin)
for item in data.get('Items', []):
    if item.get('isPrimaryMeterRegion'):
        print(json.dumps({
            'meter': item['meterName'],
            'price': item['retailPrice'],
            'unit': item['unitOfMeasure'],
            'product': item['productName'],
            'sku': item.get('armSkuName', ''),
            'type': item['type']
        }, indent=2))
"

4. Calculate Monthly Costs

Apply the correct multiplier based on unitOfMeasure:

| Unit | Monthly Multiplier | Notes |

|------|-------------------|-------|

| 1 Hour | × 730 | 365.25 days × 24 hours ÷ 12 months |

| 1 GB/Month | × estimated GB | Use actual or default estimate |

| 1/Month | × 1 | Already monthly |

| 100/Month | × quantity ÷ 100 | Per 100 units/month |

| 1 GB | × estimated GB | Ingestion-based |

| 10K | × estimated ops ÷ 10000 | Transaction-based |

5. Handle Free Tiers and Included Quantities

Note any free tier allowances in the output:

| Service | Free Allowance |

|---------|---------------|

| Functions (Consumption) | 1M executions + 400K GB-s/month |

| Application Insights | 5 GB ingestion/month |

| Log Analytics | 5 GB/day ingestion |

| Cosmos DB (Serverless) | No minimum, pay per RU |

| Bandwidth | First 5 GB outbound/month |

If the estimated usage falls within the free tier, show $0.00 with a note.

6. Present Cost Estimate

Format the output as a clear cost breakdown:

### 💰 Estimated Monthly Cost

| # | Resource | SKU/Tier | Meter | Unit Price | Monthly Est. |
|---|----------|----------|-------|-----------|-------------|
| 1 | vm-linuxvm-dev-sea | Standard_B1ls | B1ls (Linux) | $0.0052/hr | $3.80 |
| 2 | OS Disk (30GB) | Standard_LRS | S4 LRS Disk | $1.54/mo | $1.54 |
| 3 | pip-linuxvm-dev-sea | Basic Static | Static IP | $0.0036/hr | $2.63 |
| 4 | NSG, VNet, NIC | — | — | — | $0.00 |
| | | | | **Total** | **$7.97/mo** |

**Notes:**
- Prices are Microsoft retail (pay-as-you-go) in USD
- Actual costs may vary with reserved instances, savings plans, or enterprise agreements
- Bandwidth egress is not included (first 5 GB/month free)
- Prices retrieved from [Azure Retail Prices API](https://learn.microsoft.com/en-us/rest/api/cost-management/retail-prices/azure-retail-prices) on {date}

**Cost optimization options:**
- 💡 1-Year Reserved Instance: ~{X}% savings
- 💡 3-Year Reserved Instance: ~{Y}% savings
- 💡 Spot Instance: ~{Z}% savings (interruptible)

7. Save Cost Estimate

Save the estimate to the deployment artifacts:

File: .azure/deployments/{deployment-id}/cost-estimate.json

{
  "estimatedAt": "2026-02-19T10:00:00Z",
  "currency": "USD",
  "region": "southeastasia",
  "monthlyTotal": 7.97,
  "resources": [
    {
      "name": "vm-linuxvm-dev-southeastasia",
      "type": "Microsoft.Compute/virtualMachines",
      "sku": "Standard_B1ls",
      "meter": "B1ls",
      "unitPrice": 0.0052,
      "unitOfMeasure": "1 Hour",
      "monthlyEstimate": 3.80
    }
  ],
  "notes": [
    "Retail pay-as-you-go pricing",
    "Bandwidth egress not included"
  ],
  "source": "Azure Retail Prices API",
  "sourceUrl": "https://prices.azure.com/api/retail/prices"
}

Error Handling

If a price is not found for a resource:

  • Try broader filters (remove armSkuName, search by productName with contains())
  • Try alternate serviceName values (e.g., 'Azure App Service' vs 'App Service')
  • If still not found: show ❓ Price not found with the query used, so the user can verify manually
  • Never fabricate a price — show Unknown and link to the Azure Pricing Calculator

If the API is unreachable:

  • Fall back to a note: "Cost estimation unavailable — API unreachable. Check manually at https://azure.microsoft.com/pricing/calculator/"

Constraints

  • DO NOT fabricate or guess prices — all prices must come from the API response
  • DO NOT use hardcoded prices — always query the API for current rates
  • DO NOT forget to filter for isPrimaryMeterRegion eq true to avoid duplicate results
  • DO NOT mix up serviceName values — they are case-sensitive (e.g., 'Virtual Machines' not 'virtual machines')
  • ALWAYS show the pricing date so users know how current the estimate is
  • ALWAYS note that actual costs may differ from retail pricing (EA, CSP, savings plans)

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

13k tokens
Capacity
by microsoft
vendor ×3

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

6k tokens scripts
Customize
by microsoft
vendor ×3

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

8k tokens
Deploy Model
by microsoft
vendor ×3

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

26k tokens scripts
Preset
by microsoft
vendor ×3

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take azure/azure-cost-estimator from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.