Sarcouncil Journal of Multidisciplinary

Abstract: This article introduces a novel AI Model Bidding System (AMBS) that functions as an intelligent broker for selecting optimal Large Language Models based on real-time needs. Drawing inspiration from AdTech header bidding and multi-armed bandit algorithms, the proposed framework dynamically auctions AI tasks to different models based on performance metrics, historical data, and user preferences. The system incorporates both auction-based AI selection and adaptive learning-based routing to optimize the allocation of queries across different LLMs. Technical contributions include a cost-performance AI auction framework, multi-cloud AI orchestration, and personalization models for preference-based AI selection. Experimental evaluation demonstrates substantial improvements in cost efficiency, response quality, and system adaptivity compared to static selection methods, while maintaining minimal overhead. The approach creates a marketplace where models compete based on their ability to deliver value within specific contexts and constraints, democratizing access to high-quality AI while optimizing resource allocation.