We present a deep prototype-based learning architecture which achieves a performance that is competitive to a conventional, shallow prototype-based model but at a fraction of the computational cost, especially w.r.t. memory requirements. As prototype-based classification and regression methods are typically plagued by the exploding number of prototypes necessary to solve complex problems, this is an important step towards efficient prototype-based classification and regression. We demonstrate these claims by benchmarking our deep prototype-based model on the well-known MNIST dataset.