Next-generation sequencing (NGS) has been extensively employed to perform microbiome characterization worldwide. As a culture-independent methodology, it has allowed high-level profiling of sample microbial composition. However, most studies are limited to information regarding relative bacterial abundances, ignoring scenarios in which sample microbe biomass can vary widely. Here, we develop an equivolumetric protocol for amplicon library preparation capable of generating NGS data responsive to input DNA, recovering proportionality between observed read counts and absolute bacterial abundances. Under specified conditions, we argue that the estimation of colony-forming units (CFU), the most common unit of bacterial abundance in classical microbiology, is challenged mostly by resolution and taxon-to-taxon variation. We propose Bayesian cumulative probability models to address such issues. Our results indicate that predictive errors vary consistently below one order of magnitude for observed bacteria. We also demonstrate our approach has the potential to generalize to previously unseen bacteria, but predictive performance is hampered by specific taxa of uncommon profile. Finally, it remains clear that NGS data are not inherently restricted to relative information only, and microbiome science can indeed meet the working scales of traditional microbiology.