With the emergence of cloud computing, many organizations have moved their data to the cloud in order to provide scalable, reliable and highly available services. To meet the ever growing users' needs, these services mainly rely on geographically-distributed data replications thereby guaranteeing good performance and high availability. However, with replication, consistency comes into question. Services providers in the cloud have the freedom to select the level of consistency according to the access patterns or the loads of their applications. Moreover, most consistency optimizations effort concentrate on how to provide adequate trade-offs between consistency and performance and/or consistency and latency. However, as the monetary cost completely depends on the service providers, in this paper we argue that monetary cost should be taken into consideration when evaluating or selecting consistency level in the cloud. Accordingly, we define a new metric called consistency-cost efficiency. Based on the consistency-cost efficiency, we present a simple yet efficient economical consistency model, called Bismar, that adaptively tunes the consistency level at run-time in order to reduce the monetary cost while simultaneously maintaining a low fraction of stale reads. Experimental evaluations with Cassandra cloud storage on a Grid'5000 testbed show the validity of the metric, and demonstrate the effectiveness of the new consistency model.