Quantitative mass spectrometry is increasingly being used in high-throughput proteomics studies, with ever increasing cohorts and sample sizes. These studies are in part enabled by data independent acquisition (DIA/SWATH) to systematically measure peptides by label-free quantification (LFQ), which gives comprehensive peptide detection and quantification at theoretically unlimited throughput. However, there is limited literature detailing the quantitative challenges of performing proteomics at 100s of samples per week, with most studies focused on the number of detections which are reported. We set out to deeply characterize a high-throughput DIA-MS workflow and assess, empirically, whether accounting for technical variance with quality control and system suitability can be used to improve the accuracy and precision of quantitative proteomics experiments.