This chapter illustrates how bioinactivation4 can support the development and application of microbial inactivation models following the principles of predictive microbiology. This web-based tool provides a user-friendly interface to the functions implemented in the bioinactivation package for R, so it can be used to fit models to experimental data or to predict the microbial response. These operations can be done under isothermal or dynamic conditions, and using a variety of common predictive microbiology models. The features of the software are illustrated using two case studies that represent typical workflows. The first one predicts the thermal inactivation of Listeria monocytogenes based on literature data both as discrete curves and as prediction intervals (including variability). The second one develops and validates a new model for the inactivation of Bacillus cereus from experimental data gathered under isothermal and dynamic conditions. Besides providing a step-by-step guide on how to use of the software, this chapter also discusses some open scientific topics and limitations of the methodology.