Optimization of Generative AI Costs in Multi-Agent and Multi-Cloud Systems

The generative AI system is being adopted across the several fields to provide novel solutions for text generation, image synthesis, and decision-making. But when they are used in multi-agent and multi-cloud systems, they are expensive in terms of computation and finance. Regarding the aforementioned factors, this paper aims to examine methods of reducing such costs while achieving system efficiency. Such measures as dynamic workload distribution, resource scaling, as well as cost-conscious model selection is described. Through the examples of case studies and simulations, we show that incorporating these strategies can drastically decrease expenses and ensure immediate and accurate scalability across clouds of different ecosystems.