We analyze how the cost of AI is controlled from the design stage by choosing between token-based or license-based consumption, setting limits per user, reusing repeated responses with caching, and validating that the business case closes with the maximum monthly expenditure.
In this article, we share a practical guide for CIOs, CDOs, CISOs, data architects, compliance departments, and purchasing teams who need to evaluate AI vendors and define the minimum acceptable requirements to bring a solution into production without falling into failed purchases.
