Unlike traditional software with a fixed licence, many AI services price on actual use: the amount of text processed, the number of requests, the complexity of the tasks. Understanding that cost structure helps you estimate the likely spend realistically before adopting a tool.
What drives the variable cost
The cost of a conversational AI depends mainly on the amount of text exchanged — both incoming, as the context supplied, and outgoing, as the answer generated. Longer conversations, with more company context injected, cost proportionally more than short, direct exchanges.
Optimisations that cut cost without cutting quality
Techniques such as temporarily holding the static part of the context (context caching, discussed elsewhere) significantly reduce the repeated cost of the same background instructions, making it possible to offer a richer AI service without the spend rising in proportion with every conversation.
Estimating the likely spend
To arrive at a realistic budget, it is better to consider the expected number of interactions per month and the average length of each, rather than relying on a generic figure. A pilot with a limited number of users, monitored for a month, gives a far more reliable estimate than any theoretical forecast.