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Board OntologyFinance

Third-Party / API / Data Costs

Direct cost of external APIs, data providers, enrichment, and model/LLM inference consumed to deliver the product. Broken out from cloud/hosting because for AI products these costs can move gross margin materially and scale with usage rather than headcount. — Finance KPI, I'mBoard-authored (editorial tier).

I'mBoard-authored (editorial tier)

No public third-party standard anchors this KPI yet, so I'mBoard authors and maintains the definition — transparently labeled as editorial tier. See the ontology methodology for the published vs editorial tier system and the back-attribution workstream.

Rogue ID: finance.third_party_data Type: Currency Domain: Finance

Definition

Direct cost of external APIs, data providers, enrichment, and model/LLM inference consumed to deliver the product. Broken out from cloud/hosting because for AI products these costs can move gross margin materially and scale with usage rather than headcount.

Formula

Direct external API / data / model-inference cost of delivery for the period.

Why it matters

For AI-native products this can be the swing factor in gross margin and deserves its own board line.

How to interpret

Watch the ratio to usage revenue and total revenue; per-unit inference cost trends matter more than the absolute.

Calculation policy

How an AI agent should compute this KPI from messy company data. Free-text rules consumed at reasoning time — not a deterministic DSL. The most common ways to get this wrong are listed under Common miscomputations.

Inclusion rules

  • Direct cost of external APIs, data providers, enrichment, and model/LLM inference consumed to DELIVER the product (per-unit serving cost).
  • Broken out from cloud/hosting because for AI products it can swing gross margin materially and scales with usage rather than headcount.

Exclusion rules

  • Cloud compute and hosting (finance.cloud_hosting).
  • Internal tooling or data used for R&D rather than serving customers (finance.rd_tools_software).
  • Model spend for internal experimentation / evaluation that is not production serving.

Required inputs

  • Vendor bills split between production-serving and internal use.
  • Per-unit inference / API cost.

Data-source priority

  • Vendor / model-provider billing tagged by production vs internal use.

Edge cases

  • A model API used both for production inference (COGS) and internal evaluation (R&D) must be allocated.
  • Cost scales with usage, not headcount — track per-unit inference trend.

Validation checks

  • Watch the ratio to finance.usage_revenue and total revenue; per-unit inference cost trend matters more than the absolute.
  • Feeds finance.total_cogs and finance.gross_margin_pct.

Common miscomputations

  • Burying inference cost inside finance.cloud_hosting — hides the AI gross-margin driver.
  • Counting internal-experiment model spend as COGS.
  • finance.total_cogs
  • finance.usage_revenue
  • finance.gross_margin_pct

Source

I'mBoard editorial — authored and maintained by I'mBoard, first published 2026-04-01. No third-party standard is cited for this KPI; when one emerges, the definition is back-attributed and promoted to the published tier (a minor version bump). Read the ontology methodology for the published vs editorial tier system, attribution rules, and dispute process.

Stage relevance

Company stagePriority
Series ARecommended
Series BRecommended
Series C+Recommended
PublicRecommended

Suggested for stages: Series A, Series B, Series C+, Public.

Default owning functions

  • Finance

Machine-readable

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