Kernel methods are widely used in statistics, machine learning, and physical simulations. These methods give rise to dense matrices that are naïvely expensive to multiply or invert. Herein, we present CovarianceFunctions.jl, a package that automatically detects and exploits low rankness, hierarchical structure, approximate sparsity. We highlight applications of this technology in Bayesian optimization and physical simulations.