Analyzing Economic Frameworks for Serverless Computing: Taxonomy, Metrics, and Future Directions

Serverless Computing is one of the most innovative and transformative patterns in Cloud-Native Computing, introducing a model in which application logic is completely decoupled from infrastructure management and resource provisioning. Despite significant technical advantages, its inherent characteristics —such as dynamic workload behavior and a multi-tenant environment — have made cost assessment and control a complex, multidimensional challenge. Cost, pricing, and economic modeling paradigms play a key role in understanding financial dynamics and optimizing resource consumption. These approaches help redefine the relationship between system performance and economic efficiency by using data-driven analysis, adaptive strategies, and intelligent predictive models. However, their implementation still faces challenges, such as cost volatility, opaque billing, and a lack of standard metrics for measuring economic efficiency, which hinder the realization of the Pay-for-Use concept. This paper analyzes and categorizes the existing literature on economic modeling in Serverless Computing using a systematic review, and presents an analytical framework to explain the linkages among the technical, economic, and managerial dimensions within an advanced taxonomy. The main goal of this research is to describe the current state of knowledge, identify research gaps, and provide future directions for developing sustainable economic models. The findings show that intelligent, data-driven economic models can improve pricing accuracy, cost predictability, and economic adaptability in serverless ecosystems. Finally, this research can serve as a theoretical foundation for guiding economic decision-making and designing optimal policies for serverless architectures.