AI Pricing: Strategies to Prevent Escalating Costs

First, make sure you understand how each vendor prices their AI products. Some price using tokens, others by character counts, and some charge per user. Large vendors with application suites may incorporate AI into their existing suites, sell it as a separate product, or do some combination of the two. Conduct a full financial analysis to enable accurate comparisons.

Use a consistent method to evaluate different AI vendors' proposals. Create a model to estimate annual costs, including expenses like fine-tuning and retraining. This will help you see which option is the most cost-effective.

To avoid unexpected costs, make sure the vendor is transparent about any hidden fees, like overages on prepaid credits, during the bidding process. Given today’s pricing approaches will likely evolve, insist on scalability to minimize unbudgeted costs.

Ensure that the price for overages of credits or tokens is fixed. Negotiate for cost ceilings to cap any extra charges to protect against unexpected expenses.

GenAI solutions can be particularly complicated to evaluate. Vendors may use a combination of metrics noted in No. 1. Understanding and clarifying how the metric in question is defined and measured is critical when calculating budget requirements and comparing solutions. 

Be transparent with internal stakeholders about the fact that if your approach starts with a pilot for a small or moderate proof of concept, the associated costs may be more predictable but could still grow beyond expectation when production occurs at scale. 

AI and GenAI products can lead to significant extra costs when integrated with existing software. Work with your data architecture team to understand these interactions. Review the terms and conditions of your current software to avoid additional licensing fees.