The growing energy consumption of the ICT sector, driven by increasing data volumes, presents significant environmental and economic challenges. With datacenters accounting for a large portion of global electricity use, finding efficient data management solutions is essential. In bioinformatics, a major contributor to big data, optimizing data compression algorithms offers a powerful strategy to reduce both energy consumption and operational costs. Aligned with the United Nations’ Sustainable Development Goals and European Union objectives, this study introduces a model for estimating the energy consumption of various data compression algorithms within the ICT sector, focusing on genomic data. The model facilitates the simulation of optimal configurations and compression techniques to minimize energy consumption. This model has been implemented into ECOmpress, an online portal that allows users to compare the energy impact of different algorithms. When applied to genomic datasets, specialized algorithms reduced energy consumption by 26% to 38%, highlighting the benefits of compression optimization in bioinformatics. ECOmpress enables users to make informed decisions and achieve energy efficiency across a range of scenarios. The web tool is freely available at https://cobilab.github.io/ecompress