Neural Differential Equations have become an important modeling framework due to their ability to adapt to new problems automatically. Training a neural differential equation is effectively a search over a space of plausible dynamical systems. Controlling the computational cost for these models is difficult since it relies on the number of steps the adaptive solver takes. Most prior works have used higher-order methods to reduce prediction timings while greatly increasing training time or reducing both training and prediction timings by relying on specific training algorithms, which are harder to use as a drop-in replacement. In this manuscript, we use internal cost heuristics of adaptive differential equation solvers at stochastic time-points to guide the training towards learning a dynamical system that is easier to integrate. We ``close the blackbox'' and allow the use of our method with any sensitivity method. We perform experimental studies to compare our method to global regularization to show that we attain similar performance numbers without compromising on the flexibility of implementation. We develop two sampling strategies to trade-off between performance and training time. Our method reduces the number of function evaluations to 0.556x - 0.733x and accelerates predictions by 1.3x - 2x.