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Objective: Although symptom relief is a critical aspect for successful drug development in Sjögren's disease, patient experiences with Sjögren's‐related symptoms are understudied. Our objective was to determine how pain, dryness, and fatigue, the cardinal symptoms of Sjögren's disease, drive cluster phenotypes. Methods: We used data from the Sjögren's International Collaborative Clinical Alliance (SICCA) Registry and a Sjögren's Foundation survey. We performed hierarchical clustering of symptoms by levels of dryness, fatigue, and pain. Using international and US cohorts, we performed multiple logistic regression analysis to compare the clusters, which included comparisons of differences in symptoms, quality of life (QoL), medication use, and systemic manifestations. Results: Four similar clusters were identified among 1,454 SICCA registrants and 2,920 Sjögren's Foundation survey participants: 1) low symptom burden in all categories (LSB); 2) dry with low pain and low fatigue (DLP); 3) dry with high pain and low to moderate fatigue (DHP); and 4) high symptom burden in all categories (HSB). Distribution of SICCA registrants matching the symptom profile for each cluster was 10% in the LSB cluster, 30% in the DLP cluster, 23% in the DHP cluster, and 37% in the HSB cluster. Distribution of survey participants matching the symptom profile for each cluster was 23% in the LSB cluster, 14% in the DLP cluster, 21% in the DHP cluster, and 42% in the HSB cluster. Individuals in the HSB cluster had more total symptoms and lower QoL but lower disease severity than those in the other clusters. Despite having milder disease as measured by laboratory tests and organ involvement, individuals in the HSB cluster received immunomodulatory treatment most often. Conclusion: We identified 4 symptom‐based Sjögren's clusters and showed that symptom burden and immunomodulatory medication use do not correlate with Sjögren's end‐organ or laboratory abnormalities. Findings highlight a discordance between objective measures and treatments and offer updates to proposed symptom‐based clustering approaches. [ABSTRACT FROM AUTHOR]