We revisit species sampling priors from a computational perspective and show that they can be treated through simple representations that retain their full predictive structure. This enables posterior inference with off-the-shelf algorithms, yielding straightforward MCMC implementations and accessible expressions for predictive laws and partition distributions, without resorting to ad hoc numerical approximations.