The basic difference is that fitrsvm fits an exact SVM model, in the sense that it uses the exact kernel function and solves the "dual" problem. fitrkernel solves the "primal" problem using an explicit finite-sized feature space, which results in an approximation of the kernel function. For large datasets, the kernel approximation can be much faster and give good enough results.
"The software divides all elements of the predictor matrix X by the value of KernelScale. Then, the software applies the appropriate kernel norm to compute the Gram matrix."
