This paper presents a generative modeling approach called Gmda designed for tabular data, adapted to its arbitrary feature correlation structure. The generative model is trained so that sampled regions in the feature space contain the same fraction of true and synthetic samples, allowing true and synthetic data distributions to be aligned using a frugal and sound learning criterion. The merits of Gmda in terms of the usual performance indicators (pairwise correlation errors, precision, recall, predictive performance) are on par with or better than the state-of-the-art approaches for tabular data based on VAEs, GANs, or diffusion models. The key point is that it provides generative models with one or more orders of magnitude that are more frugal than baseline approaches.