We present a framework that allows for the non-asymptotic study of the $2$-Wasserstein distance between the invariant distribution of an ergodic stochastic differential equation and the distribution of its numerical approximation in the strongly log-concave case. This allows us to study in a unified way a number of different integrators proposed in the literature for the overdamped and underdamped Langevin dynamics. In addition, we analyse a novel splitting method for the underdamped Langevin dynamics which only requires one gradient evaluation per time step. Under an additional smoothness assumption on a $d$--dimensional strongly log-concave distribution with condition number $κ$, the algorithm is shown to produce with an $\mathcal{O}\big(κ^{5/4} d^{1/4}ε^{-1/2} \big)$ complexity samples from a distribution that, in Wasserstein distance, is at most $ε>0$ away from the target distribution.