Sepsis is a highly heterogeneous and life-threatening syndrome associated with high morbidity and mortality worldwide. The repeated failure of large randomized trials underscores the urgent need for precise patient stratification. Recent advances in machine learning and multi-omics technologies have facilitated the identification of distinct clinical subphenotypes and molecular endotypes. Clinical subphenotypes, typically derived from routinely available clinical variables and circulating biomarkers, reflect aggregated downstream manifestations of underlying biological processes; however, the absence of clearly identifiable pathobiological mechanisms specific to each subgroup may limit their utility as actionable treatable traits. In contrast, molecular endotyping leverages multi-omics data to elucidate the pathophysiological drivers of sepsis, offering a foundation for mechanism-based interventions. However, most endotypes remain insufficiently actionable for individualized treatment decisions at the bedside. Furthermore, existing classification frameworks rely predominantly on static assessments, which do not adequately reflect the dynamic evolution of sepsis pathophysiology. Increasing evidence underscores that sepsis is inherently dynamic, with immune responses, metabolic states, and organ dysfunction fluctuating over time. Integrating longitudinal clinical and molecular data to capture the temporal evolution of host responses and organ dysfunction through dynamic subtyping offers a promising approach to optimize patient stratification. In this narrative review, we summarize recent advances in static and dynamic subphenotyping, discuss omics-derived endotypes, and outline strategies to integrate these dimensions into clinically actionable frameworks for precision medicine in sepsis.