Predictive Cost Budget Control for Generative AI: Forecasting Future Computational Expenditure and Proactively Allocating Financial Resources

The increasing deployment of generative artificial intelligence (AI) systems has introduced substantial and increasingly variable computational expenditure associated with large language model (LLM) inference, model serving, response generation, computational preparation, and distributed execution. As generative AI services expand, controlling computational expenditure becomes an important infrastructure management problem. Existing cost optimization approaches primarily focus on reducing the expenditure of individual computational operations, selecting lower-cost resources, or monitoring accumulated spending after computational activities have occurred. However, these approaches do not necessarily provide an explicit mechanism for predicting future computational expenditure and allocating financial resources before the corresponding workloads materialize. This paper introduces Predictive Cost Budget Control for Generative AI (PCBC-GAI), a conceptual framework that investigates how future computational expenditure can be forecast and proactively incorporated into financial budget allocation decisions. Rather than treating computational expenditure as a consequence that is evaluated after inference execution, PCBC-GAI considers future computational costs as predictive variables that can influence resource authorization, task selection, computational preparation, and financial resource distribution before execution begins. The proposed framework integrates future workload forecasting, GPU expenditure prediction, inference cost estimation, cloud resource expenditure modeling, budget reservation, predictive budget allocation, expenditure risk assessment, and adaptive financial control. A central component of the framework is a predictive budget allocation mechanism that estimates the expected financial requirements of future computational workloads and determines how much budget should be reserved for anticipated demand, discretionary precomputation, uncertain workloads, and unexpected inference requirements. The framework further distinguishes between forecast expenditure, committed expenditure, reserved budget, realized expenditure, and available financial capacity, recognizing that these quantities have different implications for operational decision-making. A rolling-horizon optimization formulation is developed to minimize expected total computational expenditure while maintaining budget feasibility, service-level requirements, and sufficient financial reserves under uncertain future demand. The framework also introduces uncertainty-aware budget allocation, predictive expenditure thresholds, adaptive budget reallocation, and task-level financial authorization. An experimental methodology is established to evaluate forecasting accuracy, budget allocation efficiency, expenditure deviations, budget utilization, unexpected budget overruns, computational completion rates, and service reliability. Comparisons with fixed budgeting, reactive expenditure monitoring, historical-average budgeting, and conventional cost-aware scheduling are proposed. The research does not assume that predictive budgeting automatically reduces computational expenditure. Instead, it investigates whether anticipating future computational costs can improve the timing, distribution, and financial control of computational resources. By introducing financial budget anticipation as an explicit layer of predictive computing, PCBC-GAI extends predictive generative AI infrastructure management from anticipating computational demand and resource requirements toward anticipating and proactively managing the financial commitments required to satisfy future demand.