For common neural network models, the output probability has no meaning for out of distribution inputs, so you need to do an ensemble or some other method to get at the actual model confidence. I don't know enough about Gaussian processes to know if they have any limitations like that.

But it's an interesting point, e.g. if a CNN works better on in-distribution data, but a Gaussian process is better at providing a confidence estimate for OOD points (if it does), a hybrid model is possible.