In this work, we explore the Transcriptomics Atlas pipeline adapted for cost-efficient and high-throughput computing in the cloud. We propose a scalable, cloud-native architecture designed for running a resource-intensive aligner – STAR – and processing hundreds of terabytes of RNA-sequencing data. We implement optimization techniques that significantly reduce cost and execution time. The impact of particular optimizations is measured in medium-scale experiments followed by a large-scale experiment that leverages all of them and validates the design. Early stopping optimization allows us to reduce the total alignment time by 23%. For the cloud environment, we identify suitable EC2 instance types and verify the applicability of spot instances usage.