The central aim of this meta-analysis was to determine whether the rumen microbiome can serve as an accurate predictor of performance in beef and dairy cattle compared with predictions based on diet composition. To support this comparison, a set of models was derived and compared. Models predicted milk yield (MY), average daily gain (ADG), dry matter intake (DMI), dairy feed efficiency (FE), and beef FE using different sets of independent variables: diet (D), microbial (M), and experimental (E). Diet variables included dry matter, organic matter, neutral detergent fiber, acid detergent fiber, crude protein, ether extract, nonfiber carbohydrate, starch, and forage percentages. Microbiome variables included relative abundance of 3 major rumen bacterial phyla, species richness, and species diversity. Experimental variables included publication year, breed type (dairy, beef, or Bos indicus), and rumen sampling fraction (fluid or solid). A second set of models used D and E variables as predictors for the microbiome. For both the production and microbiome model sets, predictor variable sets were used individually and in combination. Linear mixed-effects regression, weighted by 1/standard error of the mean, was used to derive models using data from 51 peer-reviewed publications. Models for the same response variable were compared on the basis of concordance correlation coefficient with study effects removed (uCCC), root-estimated variance associated with study and error, and corrected Akaike information criterion values, wherever appropriate. The MY model using D + M + E predictors outperformed all other MY models (uCCC = 0.71). ADG was most accurately predicted by D alone (uCCC = 0.92). Interestingly, M + E was more successful at predicting DMI than any model using D variables. Similarly, dairy FE was more accurately predicted by M + E than D, albeit only slightly (uCCC = 0.69 vs. 0.65). Beef FE could only be modeled using D variables. Overall, breed type proved a better predictor of relative abundances of most rumen bacterial phyla than D. Conversely, species richness and diversity indicators were unaffected by breed type, but could be predicted by D with moderate precision and accuracy (uCCC = 0.63 to 0.69). This analysis suggests that diet and the microbiome may exert independent effects on various aspects of performance. Further research is necessary to determine the reasons for these independent influences.