This paper studies prediction with multiple candidate models in heterogeneous settings, where different models may be better suited to different inputs. We propose input-adaptive Bayesian model averaging, which assigns model weights conditional on the input through an adaptive prior and estimates the resulting posterior with amortized variational inference.