How big should a machine learning deployment be? What is a reasonable size for a microservice container that performs logistic regression? Although Python is the generally used ecosystem for data science work, it doesn't provide satisfactory answers to the size question. Size matters: if deploying to the edge or to low-resource devices, there's a strict upper bound on how large the deployment can be; if deploying to the cloud, size (and compute overhead) correspond directly to cost. In this talk, I will show how typical data science pipelines can be rewritten directly in C++ in a straightforward way, and that this can provide both performance improvements as well as massive size improvements, with total size of deployments sometimes in the single-digit MBs.