Total cost of ownership
The full cost of running an AI system across its life: model and inference spend plus integration, human review, exceptions, recovery, governance and maintenance — not just the price per call.
Total cost of ownership is the complete cost of having and operating something over time. For an AI system it goes far beyond the inference bill. It includes integration and engineering, retries and tool calls, human review, exception handling, failure and recovery work, governance and assurance, training and change management, and ongoing maintenance as models and routes change.
A cheaper model can lower one line while raising another. That is why a unit-price comparison is a weak basis for a decision.
Make it attributable first
AI TCO should be attributable before it is optimisable. If nobody can see which budget owner absorbs which cost, an optimisation in one team can simply push cost to another. A cost-migration ledger records the before, after, delta and owner for each dimension, and the cost-migration test checks that a saving survives the handoff between budgets.
The most useful denominator is not a call but a result: the cost per accepted outcome.
Read more in A cheaper AI model can move the cost instead of removing it.
Related terms
Cost-migration test
A check that traces every apparent AI saving across budget owners to see whether the cost was removed or merely moved into review, exceptions, recovery, governance or adoption.
Cost-migration ledger
A small record attached to a meaningful AI change that lists, for each cost dimension, the before, after, delta and owner, so shifted costs stay visible across budgets.
Cost per accepted outcome
The total cost of getting one AI-assisted result that is actually accepted and used, counting model spend, retries, human review, exceptions and recovery — a truer measure than cost per call.