Inference in continuous-time stochastic dynamical models is a challenging problem. To complement existing sampling-based methods, variational methods have recently been developed for this problem. Our approach solves the variational continuous-time inference problem by discretisation that essentially reduces it to a discrete-time problem. Our framework makes learning the model in addition to inference easy. Other extensions such as heteroscedastic models are also relatively easy to consider within this framework.