Choose a unit that means something
Inference is the execution of a trained model to produce an output. It consumes resources, but an inference request is not necessarily the right unit for commercial analysis. A document review might require extraction, retrieval, several model calls and a human decision. Counting requests alone can make an elaborate workflow look comparable with a simple text transformation when their delivery burdens are quite different.
Define the accepted task before calculating its cost. Does completion mean an output was generated, a user approved it, or an action was successfully recorded in another system? Keep attempted and accepted work distinguishable. Otherwise, retries and rejected answers disappear from the analysis even though the company pays for them. The definition should match the customer promise and remain stable enough to compare workloads.
Separate variable work from shared capacity
Token usage, external tool calls and some forms of data processing vary with activity. Dedicated infrastructure, deployment support and account management may be committed before much usage occurs. Retrieval introduces its own work: ingesting documents, updating indexes, storing representations and enforcing access permissions. Human review adds another dimension because exception-heavy tasks may need more attention than routine ones.
These distinctions matter when comparing customers. An account with frequent, predictable tasks can behave differently from one with occasional large jobs and demanding support requirements. Average cost may hide both. A practical analysis groups workloads by the factors that drive resource use, such as input length, tool use, latency expectations and review intensity. It should also identify which costs genuinely disappear if an account leaves.
Compare deployment on operational terms
A hosted model service can reduce the burden of operating model infrastructure, while leaving the application dependent on a supplier's interfaces and commercial terms. Self-hosting offers more direct control over deployment, but brings responsibility for capacity, updates, security and incident response. Neither approach is automatically cheaper. Utilisation, engineering effort and contractual requirements can reverse an apparently straightforward comparison.
Model routing, caching and batch processing may reduce particular costs, but each has limits. A smaller model still needs task-specific evaluation. Cached results must respect permissions and changes in source information. Batch processing may conflict with an interactive workflow. Optimisation should therefore be assessed against accepted task performance, not merely a lower charge per request. A cheaper system that creates more review work may increase total delivery cost.
Align the charge with a comprehensible promise
Seat-based pricing is familiar where value follows regular individual use, but heavy automated workloads may disconnect seats from costs. Usage pricing can track activity more directly, yet makes budgeting harder if customers cannot predict consumption. Task-based pricing is easier to relate to business work when the task has a clear boundary. Outcome-based pricing requires agreement about attribution, acceptance and factors outside the supplier's control.
Commercial design also includes limits, overage handling, deployment charges and support scope. These are not merely contract details: they determine who bears uncertainty. An unrestricted offer can place unpredictable inference or review costs on the supplier. Very granular charging can make the product difficult to adopt. The better structure depends on how observable the workload is and how much variation each party can reasonably manage.
Make the uncertain assumptions visible
A strategy model should distinguish observed delivery records from expectations about future behaviour. Where evidence is weak, describe light, typical and demanding workloads through their actual characteristics rather than attaching unsupported precision. Ask how economics change when inputs become longer, users require more revisions, or enterprise customers request isolated environments. Keep implementation work separate from recurring service delivery so expansion does not disguise unrecovered setup costs.
The point is not to predict every request. It is to identify which assumptions could make the chosen segment or contract unattractive. Finance, product and engineering should be able to trace the same customer promise through to its operational consequences. Formal accounting treatment and individual financial decisions require qualified professional advice; the strategic discussion concerns how the product's delivery model fits its commercial design.
Keep a visible distinction between cost per attempt and cost per accepted task. Product improvements can move those measures in different directions.