Despite the ubiquitous use of Markov Chain Monte Carlo methods for simulation from multivariate distributions, availability of principled stopping criteria that do not make unrealistic assumptions on the initialization of a Markov Chain relative to the target multivariate distribution is limited. Furthermore, stopping criteria that are applicable for simulation from multivariate distributions mixed type are also limited. By leveraging the benefits of modern computation that allow for simultaneous simulation of many Markov chains, we introduce stopping criteria that are easy to compute, require minimal storage, and inherit desirable properties in a variety of applications. We demonstrate the effectiveness of these stopping criteria in classification, clustering, and streaming settings.