Modern scale-out services are comprised of thousands of individual machines, which must be continuously monitored for unexpected failures. One recent approach to monitoring is latent fault detection, an adaptive statistical framework for scale-out, load-balanced systems. By periodically measuring hundreds of performance metrics and looking for outlier machines, it attempts to detect subtle problems such as misconfigurations, bugs, and malfunctioning hardware, before they manifest as machine failures. Previous work on a large, real-world Web service has shown that many failures are indeed preceded by such latent faults. Latent fault detection is an offline framework with large bandwidth and processing requirements. Each machine must send all its measurements to a centralized location, which is prohibitive in some settings and requires data-parallel processing infrastructure. In this work we adapt the latent fault detector to provide an online, communication- and computation-reduced version. We utilize stream processing techniques to trade accuracy for communication and computation. We first describe a novel communication-efficient online distributed variance monitoring algorithm that provides a continuous estimate of the global variance within guaranteed approximation bounds. Using the variance monitor, we provide an online distributed outlier detection framework for non-stationary multivariate time series common in scale-out systems. The adapted framework reduces data size and central processing cost by processing the data in situ, making it usable in wider settings. Like the original framework, our adaptation admits different comparison functions, supports non-stationary data, and provides statistical guarantees on the rate of false positives. Simulations on logs from a production system show that we are able to reduce bandwidth by an order of magnitude, with below 1% error compared to the original algorithm.