AI Cost Controls: Budgets, Throttling & Model Tiering

Generative AI has become integral to various industries, leading to significant increases in enterprise AI budgets by 2026 due to the ongoing costs of inference and compute cycles triggered by user interactions. This shift necessitates robust cost controls to prevent unexpected expenses and potential misuse, such as "denial-of-wallet" attacks. The article presents a comprehensive framework for managing AI feature costs, emphasizing the importance of budgeting, usage throttling, model tiering, and FinOps governance. It highlights the need for real-time monitoring tools like Clarifai’s Costs & Budget dashboard to track spending and optimize resource allocation. Effective cost management involves understanding AI cost drivers, designing multi-level budgets, implementing dynamic rate limits, and employing model tiering to balance cost and performance. The text also underscores the importance of continuous monitoring and anomaly detection to avoid budget overruns. Real-world case studies illustrate success through early budgeting, collaborative governance, and continuous improvement, while failures often result from hidden costs and poor planning. Future trends predict the evolution of FinOps practices, regulatory impacts, and new pricing models, necessitating adaptive strategies for sustainable AI cost management.