One of the many technical challenges that arises when scheduling bioinformatics analyses at scale is determining the appropriate amount of memory and processing resources. Both over- and under-allocation leads to an inefficient use of computational infrastructure. Over allocation locks resources that could otherwise be used for other analyses. Under-allocation causes job failure and requires analyses to be repeated with a larger memory or runtime allowance. We address this challenge by using a historical dataset of bioinformatics analyses run on the Galaxy platform to demonstrate the feasibility of an online service for resource requirement estimation.