Perhaps the most important recent advance in species delimitation has been the development of model-based approaches to objectively diagnose species diversity from genetic data. Additionally, the growing accessibility of next-generation sequence data sets provides powerful insights into genome-wide patterns of divergence during speciation. However, applying complex models to large data sets is time-consuming and computationally costly, requiring careful consideration of the influence of both individual and population sampling, as well as the number and informativeness of loci on species delimitation conclusions. Here, we investigated how locus number and information content affect species delimitation results for an endangered Mexican salamander species, Ambystoma ordinarium. We compared results for an eight-locus, 137-individual data set and an 89-locus, seven-individual data set. For both data sets, we used species discovery methods to define delimitation models and species validation methods to rigorously test these hypotheses. We also used integrated demographic model selection tools to choose among delimitation models, while accounting for gene flow. Our results indicate that while cryptic lineages may be delimited with relatively few loci, sampling larger numbers of loci may be required to ensure that enough informative loci are available to accurately identify and validate shallow-scale divergences. These analyses highlight the importance of striking a balance between dense sampling of loci and individuals, particularly in shallowly diverged lineages. They also suggest the presence of a currently unrecognized, endangered species in the western part of A. ordinarium's range.