Model Cost Allocation

How is the cost of enterprise AI inference attributed?

Model Cost Allocation is the concept of attributing and allocating AI-related operating cost — who or what drove the cost.

Once usage is measured, the operating cost behind it has to be attributed to the models, teams, products, or customers responsible. Model Cost Allocation owns that question — who or what drove the cost — so cost can be traced consistently before it feeds pricing and margin.

Attribution and allocation of AI-related operating cost.

• Cost attribution — connecting AI-related operating cost to what drove it.
• Dimensions of allocation — models, teams, products, and customers, treated as concepts.
• From usage to cost ownership — how normalized usage evidence becomes attributable cost.
• Cost evidence — a consistent basis the rest of the package can price and evaluate margin against.

These are concepts, not a cost-allocation engine, showback or chargeback system, or formula. Implementation and financial policy remain the buyer’s.

The second capability — one peer among four.

1. AI usage metering — what was consumed
2. Model cost allocation — who or what drove the cost (this namespace)
3. Usage-based AI pricing — how consumption is priced
4. Inference revenue — how it is monetized
5. Formal revenue recognition — outside scope

Model Cost Allocation sits second in the economic sequence, but it is a structurally equal peer of the other capability namespaces — not the package anchor and not hierarchically superior. The canonical package namespace is the AI Inference Economics Foundation.

One package, four peer capability namespaces.

The canonical package namespace is the AI Inference Economics Foundation — the authoritative reference for the package and its capability architecture.

Capability namespace links identify LJP-controlled namespace positions within the AI Inference Economics Foundation. Reference pages may be published or updated independently as the capability set is completed. See the canonical capability architecture.

A focused capability namespace, not a finished platform.

A buyer receives a focused capability namespace for Model Cost Allocation: a controlled domain and semantic reference point that can organize human-readable explanation, machine-readable metadata, ontology relationships, and future documentation or API-oriented architecture around one commercial AI economics capability.

• The modelcostallocation.com namespace position within the AI Inference Economics Foundation.
• A clean, public reference page for the capability.
• Machine-readable resources — ontology, namespace manifest, and a plain-text summary.
• An ontology relationship to the canonical AI Inference Economics Foundation.
• A reusable vocabulary for cost attribution, allocation, and cost ownership.
• A starting point for internal FinOps, finance, engineering, product, and AI governance alignment.
• Optionality for future documentation, metadata, knowledge-graph, API, or agent-oriented architecture.

This is a semantic and commercial reference asset, not a finished platform. It does not currently function as an API, agent endpoint, cost-allocation engine, billing engine, or standard; the future-oriented items above are optionality, not current capabilities.

LJP is not affiliated with, endorsed by, or representing any standards body. Standards references describe conceptual alignment only.

Structured reference for this namespace.

• ontology.jsonld — the concept, its relationship to the canonical package, and its governed semantic record.
• namespace.json — the public namespace manifest for this capability.
• llms.txt — a plain-text summary for language models.