A scalable variational inference approach for stochastic block models is proposed. The approach is based on a case-control approximation of the likelihood function, which is an unbiased estimator of the full likelihood. Using the case-control likelihood under a variational inference perspective allows us to strongly reduce the computational complexity, making model estimation feasible for large networks. We evaluate the performance of the proposed algorithm using both simulated and real data coming from a Facebook derived social network.