LJP · ASSET GROUP
AI Inference Economics Foundation · Technical Reference

Model Cost Allocation

How should direct and shared AI-related costs be related to accountable economic objects?

Model Cost Allocation relates direct and shared AI-related costs to accountable economic objects such as models, applications, workflows, teams, products, tenants, or customers. It makes attribution scope and cost responsibility explicit; it does not prescribe an allocation formula, claim that one basis is universally fair, or determine customer price or accounting treatment.

Why it matters: AI costs can span provider charges, shared infrastructure, data, platform services, and organizational overhead. Decisions are difficult to compare when attribution scope and shared-cost assumptions remain implicit.

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§1 — Definition

Model Cost Allocation

The attribution and allocation of direct and shared AI-related costs to models, applications, workflows, teams, business units, products, tenants, customers, or other accountable economic objects.

§2 — Relationships

Closest comparison and adjacent concepts.

Usage evidence can support attribution; allocation converts relevant cost evidence into accountable economic views. Pricing remains a separate commercial decision.

Difference

What separates them

Usage metering records consumption evidence; cost allocation relates direct and shared cost to accountable economic objects.

Relationship

How they work together

Relevant usage measures can provide an attribution input, but allocation requires explicit cost scope and policy choices.

See also

§3 — Standards and Authority

Where the terminology comes from.

FinOps for AI supplies practitioner context for allocation, forecasting, optimization, and governance of AI spend. FOCUS 1.4 supplies open cost-and-usage data requirements that can support cost attribution. Neither source chooses an internal allocation basis.

Supporting source ↗

FinOps for AI

FinOps Foundation · FinOps Framework 2026; Technology Category: AI

Supports practitioner context for allocating, forecasting, optimizing, and governing granular AI-related cost and usage.

Current practitioner guidance reviewed July 31, 2026

A practitioner framework, not a universal allocation method or endorsement of LJP.

Supporting source ↗

FinOps Open Cost and Usage Specification 1.4

FinOps Open Cost and Usage Specification Project · FOCUS Specification 1.4; ratified June 4, 2026

Supports uniform billing-data dimensions and metrics used in cost and usage attribution across providers.

Published open cost-and-usage specification

Does not prescribe internal shared-cost formulas, responsibility policy, or accounting treatment.

§4 — Evaluation

Apply the distinction to the decision at hand.

Helps teams evaluate allocation scope, evidence, and assumptions without publishing or selecting a proprietary formula.

Continue to a controlled evaluation.

§5 — LJP Foundation

How this capability fits the package.

Model Cost Allocation separates cost attribution and internal accountability from technical usage evidence, customer-facing price, and revenue recognition.

Cost-attribution layer between consumption evidence and commercial pricing analysis.

§6 — Machine-Readable Resources

Public identity and discovery resources.

§7 — Credibility Boundary

What this reference does not claim.

This namespace does not publish an allocation formula, cost model, reconciliation method, fairness rule, chargeback policy, pricing model, accounting treatment, or claim that technical usage always provides an appropriate allocation basis.

This namespace is an LJP editorial construct. It claims no standards ownership or external endorsement and selects no vendor or implementation; protected methods and transaction materials are not disclosed.

Evaluate Model Cost Allocation in context.

Move from public technical orientation to a controlled package evaluation.

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