An Adaptive Pricing Framework for Real-Time AI Model Service Exchange

Artificial intelligence (AI) model services, known for their remarkable efficiency and the convenience of automation, successfully engage customers across various downstream tasks. These tasks include, but are not limited to, weather forecasting, traffic management, content generation, and precision recommendations. Given that not all AI model users possess sufficient data to drive AI model training or the specialized knowledge to construct high-performance AI model structures, this has led to a trend in AI model service transactions, a novel facet of the digital economy. Unlike conventional digital products like music that maintain consistent quality, AI models undergo performance degradation over time. This phenomenon occurs as the data on which AI models are trained becomes outdated, leading to a “distribution shift” away from the target distribution of the most recent downstream tasks. The performance degradation, resulting in diminished consumer demand and making the AI model less competitive, leads to lower revenue for the AI model service provider. In this work, we analyze the dynamic impact of performance degradation on the demand for AI model services from consumers and propose an adaptive pricing framework for service providers to maximize revenue in real-time AI model service exchange. Specifically, We propose an optimal transport (OT) distance-based approach to estimate model performance degradation effectively. Building on this methodology, we implement several practical solutions for predicting changes in future demand rates resulting from current pricing configurations. We then propose a demand-driven AI model update mechanism for service providers to maintain high demand rates for their products while reducing retraining AI models' costs. We finally propose a reinforcement learning-based pricing mechanism that facilitates adaptive and rapid pricing responses to achieve revenue maximization. We conduct extensive experiments on two real-world datasets, exploring both 2-competitor and multi-competitor market scenarios. The results validate the effectiveness of our proposed pricing framework, demonstrating a significant revenue advantage compared to utilizing other baseline pricing strategies in AI model service transactions.