Sepsis mortality risk prediction has been explored in the machine learning (ML) community as a use case that could benefit from data-driven decision support systems in intensive care units (ICU). However, most of the work focuses on training models to reach a high classification performance, and there are limited efforts on technical artifacts that explore the practical utility of such predictive models. Therefore, we present the tool SepsisVision, an example of a web-based explainable user interface (XUI) to support sepsis mortality risk through static explanatory and interactive exploratory interfaces to navigate the behavior of an ML model through different kinds of explanations: SHAP-based local and global feature attributions, distribution comparison, and counterfactual analysis. Tool available at: https://sepsisvision.streamlit.app/ .