In this paper we investigate the parallelization of sequence alignment algorithms through an approach that employs cloud computing for the dynamic provisioning of large amounts of computational resources and Apache Spark as the coordination framework for the parallelization of the application. SparkBLAST, a scalable parallelization of sequence alignment algorithms is presented and assessed. Apache Spark’s pipe operator and its main abstraction RDD (resilient distribution dataset) are used to perform scalable protein alignment searches by invoking BLASTP as an external application library. Experiments on the Google Cloud and Microsoft Azure have demonstrated that the Spark-based system outperforms a state-of-the-art system implemented on Hadoop in terms of speedup and execution times. It is worth noting that SparkBLAST can outperform CloudBlast even when just one of the vCPUs available per node is used by SparkBLAST, as demostrated by results obtained on the Google Cloud. In the experiments presented in this paper, the Hadoop-based system always used all vCPUs available per node.