Infiltration-based sustainable urban drainage systems (i-SUDS) often turn out to be simple and effective solutions for on-site runoff and pollution control. Their ability to limit the discharge to sewer networks or receiving waters can be broadly assessed in terms of (pluri)annual stormwater volume reduction. Although accepted as a relevant efficiency metric, this long-term volume reduction does not integrate well in design practices that have traditionally relied on event-based approaches. This article introduces a modeling framework, involving a hydrological model and machine-learning emulation, from which a web app was developed to allow practitioners to investigate the relation between i-SUDS design and pluriannual volume reduction efficiencies. The theoretical basis for modeling and a description of the web app are first provided. A diagnosis of the hydrological model is then conducted. The uncertainty caused by model parameters that do not directly relate to i-SUDS design is evaluated through a sensitivity analysis performed over multiple design scenarios. The latter is found to be highly variable and potentially significant, thereby justifying its explicit consideration in the web app. As part of this diagnosis, the impact of a shallow groundwater or a low-permeability layer on simulated volume reduction efficiencies is later evaluated to clarify the validity domain of the model. Practical recommendations on the minimum distance to shallow groundwater or low permeability layer, for the rainfall conditions considered in the web app, are given as a function of project size and the permeability of the soil media. The applicability of the web app is later illustrated from a selection of outputs. Its outcomes are finally compared to those of a simple design rule based on the combination permanent storage (as rainfall depths) and drawdown duration targets. Results confirm the inability of such simple design rules to fully capture pluriannual volume reduction efficiency and point out the risk of oversizing i-SUDS.