SVM vs RVM, when to use what?
RVM is identical to SVM, but provides a probability distribution of scores.

RVM:

Pros:


RVM is better than SVM in terms of accuracy.(depends on size of training data), more training data implies more accuracy in RVM. (argued and experimentally shown in the paper [1])
Since it provides the probability distribution of score, not a point estimate. It opens up a variety of options to get point estimate out of Bayesian inference, like percentile, mean, median, average.


Cons: 


Since it uses EM algorithm for learning, it might get stuck at local minima.
Large data is required for learning


SVM:

Pros:


Standard SVM is a convex optimization problem(both hard and soft), therefore they always produce unique solution i.e. converge to global optima.


There is one very good paper about this. You can get more details here:

[1] (PDF) A comparative study of relevant vector machine and support vector machine in uncertainty analysis., accessed Jan 08 2019.