Majorization-minimization algorithms consist of iteratively minimizing a majorizing surrogate of an objective function. Because of its simplicity and its wide applicability, this principle has been very popular in statistics and in signal processing. In this paper, we intend to make this principle scalable. We introduce and study a stochastic majorization-minimization scheme, which is able to deal with large-scale or possibly infinite data sets. When applied to convex optimization problems under suitable assumptions, we show that it achieves an expected convergence rate of O(1/\sqrt{n}) after n iterations, and O(1/n) for strongly convex functions. Equally important, our scheme almost surely converges to stationary points for a large class of non-convex problems. We derive from our framework several efficient algorithms. First, we propose a new stochastic proximal gradient method, which experimentally matches state-of-the-art solvers for large-scale l1-logistic regression. Second, we develop an online DC programming algorithm for non-convex sparse estimation. Finally, we demonstrate the effectiveness of our technique for solving large-scale structured matrix factorization problems.