As cloud architectural platforms evolve, understanding how to maximize cost efficiency of application deployments is difficult without the ability to assess cost vs. performance tradeoffs of new technology platforms. With the advent of container-based computing, new opportunities for improving resource utilization efficiency have emerged. Compared to traditional cloud application deployments hosted on dedicated virtual machines (VMs), deployments to container clusters can save significant resources by aggregating application deployments to a shared pool of VMs. However, the degree of savings is often uncertain, and hobbled by excessive container resource allocation reflective of engineers' instincts to treat them as individual VMs. As practitioners are accustomed to performing application deployments to VMs, we are especially interested in understanding if VM resource allocations (e.g. CPU, RAM, disk) are appropriate for container deployments. In this research, we set out to analyze gaps between memory allocation and memory utilization for application deployments to container clusters.