As cloud services need a fair pricing for both service providers and customers. If the price is too high, the customer may not use it, if the price is too low, service providers have less incentive to develop services. This paper proposes a novel pricing framework for cloud services using game theory (Cournot Duopoly, Cartel, and Stackelberg models) and data mining techniques (clustering and classification, e.g., SVM (Support Vector Machine)) to determine optimal prices for cloud services. The framework is dynamic because the price is determined based on recent usage data and available resources, it is also intelligent as it takes into various economic models into consideration, it is benign because it considers two conflicting parties, service providers and consumers, into consideration at the same time, and it is customizable based on various pricing strategies proposed by service providers and usage patterns as exhibited by consumers. Linear regression is used in various game theory models to determine the optimal price. A global pricing union (GPU) framework is proposed to achieve the best practice of game theory models. Based on the proposed technique, this paper applies this pricing framework to a case study in cloud services, and demonstrates that the prices obtained meet the requirement of traditional supply-demand analysis. In other words, the price obtained is good enough.