Cost Reports That Cannot Double-Count

Six controls for a weekly AI spend report that treats cloud billing as the system of record and gateway attribution as a non-additive proxy.

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Summary

Two systems report AI fleet spend: cloud Cost Management returns the actual bill, and a gateway proxy allocates that spend across models and teams. Adding the two produces a number that is wrong every time. A weekly report shipped with six controls that keep the boundary explicit: a single system of record, attribution labeled as non-additive, end-exclusive date windows, DST-safe delivery, and a deterministic visual fallback.

What changed

  • Azure Cost Management is declared the authoritative bill. LiteLLM attribution is labeled as allocation and never summed with the cloud total.
  • Reconciliation subtotals by resource group, month, and model family are checked against the canonical total using end-exclusive [start, end) windows.
  • A dual-UTC-slot schedule with an internal Europe/London gate delivers exactly one 12:30 local report through GMT and BST transitions.
  • AI-generated infographics pass OCR verification of every financial field. On any mismatch, a deterministic 1536x1024 template replaces the image.

The accounting error

Gateway attribution maps tokens to resources already on the bill. Treating it as additive spend inflates totals by the full proxy amount. The fix is structural: separate sections, explicit labels, a reconciliation table between sources, and no layout position that invites summing.

Six-control frame (reusable)

  1. Declare one system of record for the bill.
  2. Label gateway data as attribution unless it reconciles to the bill.
  3. Never add proxy estimates to authoritative actual cost.
  4. Use end-exclusive windows and reconcile every subtotal to the canonical total.
  5. Make local-time schedules DST-safe with an internal timezone gate.
  6. Make generated financial graphics fail closed: OCR exact fields, then deterministic fallback.

Verification

7/7 tests covering collection, reconciliation, DST gate, visual fallback, and secret scanning. Live read-only collection from both sources. Browser QA at 1440px desktop and true 390px mobile. Scheduler readback confirmed job enabled, last status OK, next run scheduled, script-only, no model pin. Each run produces a JSON, CSV, report, prompt, image, and SHA-256 manifest bundle.

Claim boundary

The receipts prove the collector, reconciliation, DST gate, visual fallback, secret controls, browser rendering, and scheduler registration. They do not prove the first future scheduled delivery will exercise the paid image generation path successfully. The report has an explicit deterministic fallback for that case.

No private subscription IDs, internal paths, raw costs tied to private infrastructure, tester identities, keys, or endpoints are included.

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