Hybrid cloud environments provide a scalable and cost-effective solution to train large-scale AI models by dynamically allocating computational resources and optimizing data placement strategies. However, training efficiency in these environments is significantly influenced by multiple interdependent metrics such as data locality, network bandwidth, latency, computing power, storage cost, scalability, public cloud API cost, memory utilization, GPU utilization, and energy efficiency. Tuning one of these parameters often leads to trade-offs in others. For instance, increasing bandwidth usage might result in higher energy consumption, whereas minimizing latency by co-locating data and compute may escalate transfer or storage costs. For the scope of this paper, we consider “Model Training Efficiency” as a multi-faceted metric that can be interpreted differently depending on the priority—whether optimizing for cost-effectiveness, minimizing energy consumption, or achieving the fastest model convergence. In this study, we propose a novel mathematical model to quantify training efficiency while capturing these trade-offs, thereby enabling informed data placement decisions. Furthermore, we also present real-world case studies illustrating how different strategies such as data caching, replication and cloud bursting can mitigate these trade-offs. Our goal is to provide a generic mathematical framework that enables organizations to strategically balance computational performance, cost, and resource efficiency for training AI models in a hybrid cloud configuration.