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In brief.
AI services are billed in three ways: by token (the unit of text consumed by the model), by credit (in-house units repackaged by the provider) or by flat fee (a fixed price per scope). This choice is not a contractual detail, it determines adoption: a meter installs rationing behaviour — you hesitate, you do not iterate; a flat fee liberates usage. Yet the value of document AI comes precisely from volume and iteration.
The three models, without caricature
By token: you pay the model’s raw consumption; apparently transparent, unpredictable in practice, because serious document processing multiplies invisible calls (analysis, extraction, cross-checks). A team handling a large file can consume the month’s budget in a week. By credit: the provider repackages tokens into in-house units, often with barely legible conversion rules; the meter remains, with an added layer of opacity. By flat fee: a price per scope, independent of the volume processed; finance records a line, not a curve.
The behavioural effect, the blind spot of comparisons
Faced with a meter, every employee becomes a micro-controller: rerunning an analysis costs, reprocessing a file costs. Usage settles at the minimum, the tool is under-used, and the project — judged on adoption — fails despite having worked technically. Three questions to put to any provider, with written answers required: does my annual cost depend on my volume? What happens in the month when we process three times as many files? Who bears the capping mechanisms, finance or the users?
The Optivalue.ai approach
Optivalue.ai is subscribed to on a flat-fee basis, with no token or credit billing: the cost is predictable and usage is not rationed, which aligns the platform’s interest with its real adoption.
Isn’t pay-as-you-go fairer?
In theory; in practice it produces either under-use or unmanaged budget drift.
How do you compare two offers with different models?
Simulate a realistic year, peaks included, and require the total cost in writing under both scenarios.
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