Data-as-a-Service (DaaS) is a branch of cloud computing that provides support to "query the Web". Due to its ultrahigh scale, it is important establish rules for pricing resources, and guidelines for infrastructure investments. Those decisions should prioritize the compliance with SLA requirements, minimizing the incidence of agreement breaches that compromise the performance of the cloud services, as well as optimizing the use of resources and the cost of the services. The objective of this work is to address the pricing problem of DaaS by developing a cost model that optimizes the prices of query virtual machines across data centers by performing a cost-based scheduling.