AI services are expanding rapidly, and pricing them accurately is now a top concern for IT Finance teams. This guide—developed with Magic Orange—explores key cost drivers, SaaS vs. custom AI pricing, and how FinOps practices are evolving to meet the demands of AI-driven strategies.

AI shifts cost structures by front-loading expenses into the training phase, requiring new financial models, smarter cost allocation, and sustainability-aware infrastructure planning to manage total cost of ownership effectively.

Building AI in-house requires careful planning across cloud and on-prem infrastructure, labor, data management, and security. Balancing cost, performance, and control is key to long-term success.

As AI adoption grows, organizations must manage unique cost drivers - from serverless service usage to expensive model training. Aligning AI costs with business value is key to maximizing ROI and making informed strategic decisions.