Optimal Pricing of Cloud Services: Committed Spend under Demand Uncertainty

2025-02-11

AI Summary

This paper studies the optimal dynamic pricing problem for cloud service providers facing buyers with uncertain demand and noisy pre-contract demand signals. Methodologically, it integrates mechanism design, dynamic contract theory, and Bayesian game analysis to develop an incentive-compatible dynamic contracting framework—the first to extend sequential screening to nonlinear, multi-unit demand settings. The key contributions are: (i) identification of a novel contract structure coupling signal reporting with a “fixed upfront fee + usage-based discount” pricing scheme; (ii) rigorous proof that high-signal buyers receive marginal discounts but incur higher fixed costs; and (iii) derivation of the theoretically optimal implementations—yielding closed-form solutions—for both two-part tariffs and committed-spend contracts. The framework bridges theoretical rigor and practical applicability, offering a new paradigm for cloud pricing under demand uncertainty and asymmetric information.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Cryptocurrency and smart contracts

Abstract

We consider a seller who offers services to a buyer with multi-unit demand. Prior to the realization of demand, the buyer receives a noisy signal of their future demand, and the seller can design contracts based on the reported value of this signal. Thus, the buyer can contract with the service provider for an unknown level of future consumption, such as in the market for cloud computing resources or software services. We characterize the optimal dynamic contract, extending the classic sequential screening framework to a nonlinear and multi-unit setting. The optimal mechanism gives discounts to buyers who report higher signals, but in exchange they must provide larger fixed payments. We then describe how the optimal mechanism can be implemented by two common forms of contracts observed in practice, the two-part tariff and the committed spend contract. Finally, we use extensions of our base model to shed light on policy-focused questions, such as analyzing how the optimal contract changes when the buyer faces commitment costs, or when there are liquid spot markets.

Problem

• Optimal pricing under demand uncertainty
• Dynamic contracts for cloud services
• Impact of commitment costs on contracts

Innovation

• dynamic contract optimization
• nonlinear sequential screening
• two-part tariff implementation