This paper provides enterprise leaders with a holistic framework to manage and forecast generative AI (Gen AI) costs across infrastructure, platform, API, and software layers. It examines the unique volatility of token-based pricing, GPU scarcity, and rapidly evolving service SKUs, emphasizing the need for real-time observability, FinOps integration, and cross-functional governance.
The paper warns against traditional cloud cost assumptions and introduces strategies for workload right-sizing, forecasting cadence, and architectural discipline. It concludes with an actionable AI maturity model, encouraging organizations to evolve from experimentation to scaled deployments with strong financial controls and governance structures to prevent budget overruns as Gen AI scales.