Azure VM Recommender
Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations by analyzing workload type, performance requirements, scaling needs, and budget. No Azure subscription required — 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 wants to compare VM families, sizes, or pricing tiers
- User asks about trade-offs (cost vs performance, single VM vs scale set, orchestration modes)
- User needs a cost estimate without an Azure subscription
- User asks "Needs autoscaling?" or wants to decide between a single VM and a scale set
Workflow
Use reference files for initial filtering. Then verify with live documentation via
web_fetchbefore final recommendations. Ifweb_fetchfails, fall back to the reference files and surface the staleness warning from web-fetch-policy.md.
Step 1: Gather Requirements
Ask the user (infer when possible):
| Requirement | Examples |
|---|---|
| Workload type | Web server, relational DB, ML training, batch, dev/test |
| vCPU / RAM needs | "4 cores, 16 GB" 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, fixed count, or variable |
| Scaling needs | None, manual, autoscale (metrics / schedule) |
| Availability needs | Best-effort, fault-domain, cross-zone HA |
| Load balancing | None, Azure Load Balancer (L4), Application Gateway (L7) |
Step 2: Determine VM vs VMSS
Review VMSS Guide. Decision shortcut — start by asking Needs autoscaling? then walk the table:
| Signal | Pick |
|---|---|
| Autoscale on CPU, memory, or schedule | VMSS |
| Stateless web/API tier behind a load balancer | VMSS |
| Batch / parallel processing across many nodes | VMSS |
| Mixed VM sizes in one group | VMSS (Flexible) |
| Single long-lived server (jumpbox, AD DC) | VM |
| Unique per-instance config | VM |
| Stateful, tightly-coupled cluster | VM (or VMSS case-by-case) |
If recommending VMSS, verify with web_fetch per web-fetch-policy.md. When in doubt, default to a single VM.
Step 3: Select VM Family
Review VM Family Guide and pick 2–3 candidate families. Verify each candidate's specs with web_fetch against:
https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/<family-category>/<series-name>For Spot eligibility, also fetch https://learn.microsoft.com/en-us/azure/virtual-machine-scale-sets/use-spot. If any fetch fails, follow web-fetch-policy.md. Same SKUs apply to single VMs and VMSS.
Step 4: Look Up Pricing
Query the Azure Retail Prices API per Retail Prices API Guide.
VMSS: no extra charge — pricing is per-VM. Multiply per-instance price × expected count. For autoscale, estimate at both
minandmax.
Step 5: Validate Quota Availability
GATE — do not present recommendations until quota is validated.
If the user has a subscription + region, review and run the checks from VM Quota Validation Guide. Without a subscription, note quota must be checked before deployment.
| Outcome | Action |
|---|---|
| ✅ Sufficient | Proceed to Step 6 |
| ⚠️ Near limit (>80%) | Proceed but warn; suggest quota increase |
| ❌ Insufficient | Request increase, swap family, or try another region |
Include a "Quota Status" column (✅/⚠️/❌) in the table.
Step 6: 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 for VMSS with autoscale |
| Estimated $/hr | Per-instance pay-as-you-go |
| Why | Workload fit |
| Trade-off | What the user gives up |
Always explain why a family fits and the Trade-off (cost vs cores, burstable vs dedicated, VM simplicity vs VMSS scale).
For VMSS, also mention orchestration mode (default Flexible), autoscale strategy (metric / schedule / both), and load balancer type.
Step 7: Offer Next Steps
- Compare reservation / savings plan pricing (
priceType eq 'Reservation'in the API) - Suggest Azure Pricing Calculator for full estimates
- For VMSS: autoscale best practices, VMSS networking
Step 8: Hand Off to VM Creator (Optional)
If the user wants to actually provision what was recommended, hand off to vm-creator. See handoff-to-creator.md for the required Plan Card render and routing rules.
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 + instance count; default to single VM if still unsure |
| User asks VMSS pricing directly | Same VM pricing API; VMSS has no extra charge — multiply by instance count |
References
- VM Family Guide — family-to-workload mapping
- Retail Prices API Guide — query patterns, filters
- VMSS Guide — when to use VMSS, orchestration, autoscale
- VM Quota Validation Guide — vCPU checks, CLI commands
- web-fetch-policy.md — fail-safe behavior for live docs lookups
- handoff-to-creator.md — Step 8 hand-off rules
- vm-creator — provision the recommended SKU