Strong evidence suggests that the gut microbiota is altered in inflammatory bowel disease (IBD), indicating its potential role in noninvasive diagnostics. However, no clinical applications are currently used for routine patient care. The main obstacle to implementing a gut microbiota test for IBD is the lack of standardization, which leads to high interlaboratory variation. We studied the between-hospital and between-platform batch effects and their effects on predictive accuracy for IBD. Fecal samples from 91 pediatric IBD patients and 58 healthy children were collected. IS-pro, a standardized technique designed for routine microbiota profiling in clinical settings, was used for microbiota composition characterization. Additionally, a large synthetic data set was used to simulate various perturbations and study their effects on the accuracy of different classifiers. Perturbations were validated in two replicate data sets, one processed in another laboratory and the other with a different analysis platform. The type of perturbation determined its effect on predictive accuracy. Real-life perturbations induced by between-platform variation were significantly greater than those caused by between-laboratory variation. Random forest was found to be robust to both simulated and observed perturbations, even when these perturbations had a dramatic effect on other classifiers. It achieved high accuracy both when cross-validated within the same data set and when using data sets analyzed in different laboratories. Robust clinical predictions based on the gut microbiota can be performed even when samples are processed in different hospitals. This study contributes to the effort to develop a universal IBD test that would enable simple diagnostics and disease activity monitoring.