Azure Compute Skill

Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations by analyzing workload type, performance requirements, scaling needs, and budget. No Azure subscription required — all data comes from public Microsoft documentation and the unauthenticated Retail Prices API.

When to Use This Skill

• User asks which Azure VM or VMSS to choose for a workload
• User needs VM size recommendations for web, database, ML, batch, HPC, or other workloads
• User wants to compare VM families, sizes, or pricing tiers
• User asks about trade-offs between VM options (cost vs performance)
• User needs a cost estimate for Azure VMs without an Azure account
• User asks whether to use a single VM or a scale set
• User needs autoscaling, high availability, or load-balanced VM recommendations
• User asks about VMSS orchestration modes (Flexible vs Uniform)

Workflow

Use reference files for initial filtering CRITICAL: then always verify with live documentation from learn.microsoft.com before making final recommendations. If web_fetch fails, use reference files as fallback but warn the user the information may be stale.

Step 1: Gather Requirements

Ask the user for (infer when possible): Requirement Examples Workload type Web server, relational DB, ML training, batch processing, dev/test vCPU / RAM needs "4 cores, 16 GB RAM" or "lightweight" / "heavy" GPU needed? Yes → GPU families; No → general/compute/memory Storage needs High IOPS, large temp disk, premium SSD Budget priority Cost-sensitive, performance-first, balanced OS Linux or Windows (affects pricing) Region Affects availability and price Instance count Single instance, fixed count, or variable/dynamic Scaling needs None, manual scaling, autoscale based on metrics or schedule Availability needs Best-effort, fault-domain isolation, cross-zone HA Load balancing Not needed, Azure Load Balancer (L4), Application Gateway (L7)

Step 2: Determine VM vs VMSS

Workflow:

1. Review VMSS Guide to understand when VMSS vs single VM is appropriate
2. Use the gathered requirements to decide which approach fits best
3. REQUIRED: If recommending VMSS, fetch current documentation to verify capabilities:

   web_fetch https://learn.microsoft.com/en-us/azure/virtual-machine-scale-sets/overview
   web_fetch https://learn.microsoft.com/en-us/azure/virtual-machine-scale-sets/virtual-machine-scale-sets-autoscale-overview
   

4. If web_fetch fails, proceed with reference file guidance but include this warning:

   Unable to verify against latest Azure documentation. Recommendation based on reference material that may not reflect recent updates.

Needs autoscaling?
├─ Yes → VMSS
├─ No
│  ├─ Multiple identical instances needed?
│  │  ├─ Yes → VMSS
│  │  └─ No
│  │     ├─ High availability across fault domains / zones?
│  │     │  ├─ Yes, many instances → VMSS
│  │     │  └─ Yes, 1-2 instances → VM + Availability Zone
│  │     └─ Single instance sufficient? → VM

Signal Recommendation Why Autoscale on CPU, memory, or schedule VMSS Built-in autoscale; no custom automation needed Stateless web/API tier behind a load balancer VMSS Homogeneous fleet with automatic distribution Batch / parallel processing across many nodes VMSS Scale out on demand, scale to zero when idle Mixed VM sizes in one group VMSS (Flexible) Flexible orchestration supports mixed SKUs Single long-lived server (jumpbox, AD DC) VM No scaling benefit; simpler management Unique per-instance config required VM Scale sets assume homogeneous configuration Stateful workload, tightly-coupled cluster VM (or VMSS case-by-case) Evaluate carefully; VMSS Flexible can work for some stateful patterns

Warning: If the user is unsure, default to single VM for simplicity. Recommend VMSS only when scaling, HA, or fleet management is clearly needed.

Step 3: Select VM Family

Workflow:

1. Review VM Family Guide to identify 2-3 candidate VM families that match the workload requirements
2. REQUIRED: verify specifications for your chosen candidates by fetching current documentation:

   web_fetch https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/<family-category>/<series-name>
   

   Examples:
   • B-series: https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/general-purpose/b-family
   • D-series: https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/general-purpose/ddsv5-series
   • GPU: https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/gpu-accelerated/nc-family
3. If considering Spot VMs, also fetch:

   web_fetch https://learn.microsoft.com/en-us/azure/virtual-machine-scale-sets/use-spot
   

4. If web_fetch fails, proceed with reference file guidance but include this warning:

   Unable to verify against latest Azure documentation. Recommendation based on reference material that may not reflect recent updates or limitations (e.g., Spot VM compatibility). This step applies to both single VMs and VMSS since scale sets use the same VM SKUs.

Step 4: Look Up Pricing

Query the Azure Retail Prices API — Retail Prices API Guide

Tip: VMSS has no extra charge — pricing is per-VM instance. Use the same VM pricing from the API and multiply by the expected instance count to estimate VMSS cost. For autoscaling workloads, estimate cost at both the minimum and maximum instance count.

Step 5: Present Recommendations

Provide 2–3 options with trade-offs: Column Purpose Hosting Model VM or VMSS (with orchestration mode if VMSS) VM Size ARM SKU name (e.g., Standard_D4s_v5) vCPUs / RAM Core specs Instance Count 1 for VM; min–max range for VMSS with autoscale Estimated $/hr Per-instance pay-as-you-go from API Why Fit for the workload Trade-off What the user gives up

Tip: Always explain why a family fits and what the user trades off (cost vs cores, burstable vs dedicated, single VM simplicity vs VMSS scalability, etc.). For VMSS recommendations, also mention:

• Recommended orchestration mode (Flexible for most new workloads)
• Autoscale strategy (metric-based, schedule-based, or both)
• Load balancer type (Azure Load Balancer for L4, Application Gateway for L7/TLS)

Step 6: Offer Next Steps

• Compare reservation / savings plan pricing (query API with priceType eq 'Reservation')
• Suggest Azure Pricing Calculator for full estimates
• For VMSS: suggest reviewing autoscale best practices and VMSS networking

Error Handling

Scenario Action API returns empty results Broaden filters — check armRegionName, serviceName, armSkuName spelling User unsure of workload type Ask clarifying questions; default to General Purpose D-series Region not specified Use eastus as default; note prices vary by region Unclear if VM or VMSS needed Ask about scaling and instance count; default to single VM if unsure User asks VMSS pricing directly Use same VM pricing API — VMSS has no extra charge; multiply by instance count

References

• VM Family Guide — Family-to-workload mapping and selection
• Retail Prices API Guide — Query patterns, filters, and examples
• VMSS Guide — When to use VMSS, orchestration modes, and autoscale patterns

GitHub Owner

Owner: microsoft

GitHub Links

• Website: https://opensource.microsoft.com
• Email: opensource@microsoft.com
• Verified domains: microsoft, opensource.microsoft.com, microsoft.com

Files

VMSS Guide

• View: https://github.com/microsoft/github-copilot-for-azure/blob/HEAD/plugin/skills/azure-compute/references/vmss-guide.md

VM Family Guide

• View: https://github.com/microsoft/github-copilot-for-azure/blob/HEAD/plugin/skills/azure-compute/references/vm-families.md

Retail Prices API Guide

• View: https://github.com/microsoft/github-copilot-for-azure/blob/HEAD/plugin/skills/azure-compute/references/retail-prices-api.md

VM Family Guide

• View: https://github.com/microsoft/github-copilot-for-azure/blob/HEAD/plugin/skills/azure-compute/references/vm-families.md

Retail Prices API Guide

• View: https://github.com/microsoft/github-copilot-for-azure/blob/HEAD/plugin/skills/azure-compute/references/retail-prices-api.md

VMSS Guide

• View: https://github.com/microsoft/github-copilot-for-azure/blob/HEAD/plugin/skills/azure-compute/references/vmss-guide.md