Azure Cost Calculator

Azure Cost Calculator

Deterministic Azure cost estimation using the public Retail Prices API. Never guess prices; always query the live API via the scripts.

Runtime Detection

Choose the script runtime based on what is available:

Both produce identical JSON output. Bash flags use --kebab-case equivalents of PowerShell -PascalCase parameters (e.g., -ServiceName → --service-name).

Declarative Parameters

Service reference files specify query parameters as Key: Value pairs. Translate to Bash --kebab-case or PowerShell -PascalCase flags; quote string values with spaces. See workflow.md for the full parameter table, translation examples, and output formats.

Workflow

Phase 1: Analysis (no API queries)

1. Parse: extract resource types, quantities, and sizing from user's architecture

2. Clarify: if any of these are true, stop and ask before continuing:

   • A resource maps to a category but not a specific service (e.g., "a database") → list 2–4 options
   • A resource has no count, no sizing/tier, or no workload scale → ask for specifics
   • A resource has no expected monthly volume: data transferred/ingested (GB), transactions, requests, messages, tokens, or users/devices → ask for estimated volume
   • A multi-model or multi-feature service (e.g., Azure OpenAI, AI Services, Defender for Cloud) has no model or feature variant specified → ask which one (cost can vary 15–30×)
   • User describes a goal without a hosting model (e.g., "a web app") → present 2–3 options with trade-offs
   • Batch all gaps into one prompt. Offer concrete choices with sensible options (e.g., "100 GB/month?", "GPT-4o or GPT-4o-mini?"). One round max; if user declines a specific parameter, apply safe defaults only for Safe-default gaps and disclose them; if any Never-assume gap remains, do NOT proceed; state what cannot be estimated without the missing input.

3. Locate each service reference using the lookup workflow in shared.md (file search → routing map → category browse → broad search → discovery)

4. Read matched service files; check billingNeeds and follow dependency chains (e.g., AKS → VMs → Managed Disks)

5. Classify each parameter using the Disambiguation Protocol in shared.md:

   • Specified: user provided value (use verbatim)
   • Never-assume gap: required parameter missing (must ask)
   • Safe-default gap: optional parameter missing (use default, disclose)

6. Specification Review: present a summary:

   Service	Specified	Missing (will ask)	Defaults (will assume)

   • If any never-assume parameter is missing → ask user before proceeding
   • If only safe-default gaps remain → disclose defaults and proceed to Phase 2
   • Single-service shortcut: skip this table for single-service estimates where all parameters are specified

Phase 2: Estimation

7. Query: run the pricing script for each service using parameters from service files + user input + resolved defaults
8. Calculate: apply cost formulas from service files; multiply by quantities
9. Verify arithmetic: for each line item, restate the formula with actual numbers, compute, and confirm the result. If any intermediate calculation involves multiplication of two numbers > 10, compute it step-by-step (e.g., 14.5 × 640 → 14 × 640 → 10 × 640 = 6,400; 4 × 640 = 2,560; subtotal = 8,960; 0.5 × 640 = 320; total = 9,280). Do not rely on mental math for multi-digit operations.
10. Present: output the estimate with:

• Assumptions block (see Disambiguation Protocol in shared.md), listed before cost numbers
• Line items: service, unit price, quantity/hours, monthly cost
• Grand total: re-sum all line-item monthly costs independently; if discrepancy, use re-summed value

Post-Estimate Iteration

After presenting the estimate, the user may request changes (switch region, add RI, resize instances, add/remove services). Re-run only the affected queries; do not restart the full workflow.

Reference Index (load on demand)

Critical Rules

1. Never guess prices: always run the script against the live API
2. Infer currency and region from user context: if unspecified, ask the user or default to USD and eastus
3. Ask before assuming: if a required parameter is ambiguous or missing, stop, clarify and ask the user. Never silently default a never-assume parameter. At the request level, use the Clarify checks in Step 2 (for example monthly volume, data transfer, and model/feature variant). At the parameter level, use the authoritative Disambiguation Protocol table in shared.md.
4. Default output format is Json: never use Summary (invisible to agents)
5. Lazy-load service references: only read files from references/services/ directly required by the user's query. Use the file-search workflow (Step 2) to locate specific files.
6. PowerShell: use -File, not -Command: run scripts with pwsh -File or powershell.exe -File; on Linux/macOS, bash strips OData quotes from inline commands. PS 5.1 caveats: (a) Always add -ExecutionPolicy RemoteSigned before -File when using powershell.exe; default Windows policies silently block script execution (see Runtime Detection note above). (b) Use -Command instead of -File when passing array parameters (e.g., -Region 'eastus','australiaeast'), because -File mode does not parse PowerShell expression syntax and collapses the array into a single string.
7. Use exact category names: group line items using the exact Category Index names from shared.md verbatim (e.g., "Compute", "Databases", "AI + ML"). Do not paraphrase, abbreviate, or rename them.
8. Scope to user-specified resources: only include resources explicitly stated in the user's architecture. Companion resources from billingNeeds are included automatically.
9. MeterId: when the user requests meter IDs, add --include-meter-id / -IncludeMeterId. No extra API calls needed.

Service File Metadata

YAML front matter fields. Optional fields use default elision; omitted means the default applies.

Universal Traps

These apply to EVERY query:

1. serviceName and all filter values are case-sensitive: use exact values from service reference files
2. Unfiltered queries return mixed SKU variants: always filter with productName/skuName to the specific variant needed
3. Multi-meter resources need separate queries: run one query per meter with -MeterName

Batch Estimation Mode

When estimating 3 or more services, use these rules to reduce token consumption:

1. Partial reads: read only lines 1–45 of each service file (YAML front matter, trap, first query pattern).
2. Front matter routing: use YAML metadata to skip unnecessary work:
   • hasMeters: false / pricingRegion: api-unavailable → skip API; use Known Rates or primaryCost
   • pricingRegion: global → Region: Global; empty-region → omit region
   • apiServiceName → use instead of serviceName in queries
   • hasFreeGrant: true → apply grant deduction; privateEndpoint: true → add PE line item
3. Full read triggers: no query pattern in partial read, non-default config, 0/unexpected results, or billingConsiderations applies.
4. Parallel queries: run independent service queries in parallel, but limit to 3–5 concurrent requests to avoid API rate limiting. If querying more than 5 services, stagger starts in batches.
5. Skip redundant references: read shared.md and pitfalls.md once at the start, not between services.
6. Progressive distillation: after each service query returns, emit a summary row before proceeding: | Category | Service | Resource | Unit Price | Unit | Qty | Monthly Cost | Notes | Multi-meter services get one row per line item. After all queries complete, assemble the final estimate from the accumulated rows. Do not re-read service files already distilled unless a full read trigger is needed. During Post-Estimate Iteration, replace the distillation row(s) for any re-queried service.
7. Compact output: use OutputFormat: Compact (or --output-format Compact in Bash) for batch queries. Compact returns only the 9 fields needed for cost calculation (MeterName, ProductName, SkuName, UnitPrice, UnitOfMeasure, MonthlyCost, Currency, ReservationTerm, TierMinUnits); no query echo, no summary block. With --include-meter-id, Compact includes MeterId as a 10th field. Use full Json format when debugging unexpected results or when a service file requires fields not in the Compact set.

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