Infrastructure as a Service pricing models for resources are meant to reflect the operational costs and profit margins for providers to deliver virtualized resources to customers subject to an underlying Service Level Agreements (SLAs). While the operational costs incurred by providers are dynamic – they vary over time depending on factors such as energy cost, cooling strategies, and aggregate demand – the pricing models extended to customers are typically fixed – they are static over time and independent of aggregate demand. This disconnect between the dynamic cost incurred by a provider and the fixed price paid by a customer results in an economically inefficient marketplace. In particular, it does not provide incentives for customers to express workload scheduling flexibilities that may benefit them as well as providers. In this paper, we utilize a dynamic pricing model to address this inefficiency and give customers the opportunity and incentive to take advantage of any flexibilities they may have regarding the provisioning of their workloads. We present CloudPack: a framework for workload colocation, which provides customers with the ability to formally express workload flexibilities using Directed Acyclic Graphs, optimizes the use of cloud resources to minimize total costs while allocating clients’ workloads, and utilizes Shapley valuation to rationally – and thus fairly in a game-theoretic sense – attribute costs to the customers. Using extensive simulation, we show the practical utility of our CloudPack colocation framework and the efficacy of the resulting marketplace in terms of cost savings.