Clusters of loosely connected machines are becoming an important model for commercial computing. The cost/performance ratio makes these scale-out solutions an attractive platform for a class of computational needs. The work we describe in this paper focuses on understanding performance when using a scale-out environment to run commercial workloads. We describe the novel scale-out environment we configured and the workload we ran on it. We explain the unique performance challenges faced in such an environment and the tools we applied and improved for this environment to address the challenges. We present data from the tools that proved useful in optimizing performance on our system. We discuss the lessons we learned applying and modifying existing tools to a commercial scale-out environment, and offer insights into making future performance tools effective in this environment.