Metamodeling techniques have been widely used as substitutes of high-fidelity and time-consuming models in various engineering applications. Examples include polynomial chaos expansions, neural networks, Kriging or support vector regression. This papers attempts to compare the latter two in different case studies so as to assess their relative efficiency on simulation-based analyses. Similarities are drawn between these two metamodels types leading to the use of anisotropy for SVR. Such a feature is not commonly used in the SVR related literature. A special care is given to a proper automatic calibration of the model hyperparameters by using an efficient global search algorithm, namely the covariance matrix adaptation - evolution scheme (CMA-ES). Variants of these two metamodels, associated with various kernel or auto-correlation functions, are first compared on analytical functions and then on finite-element-based models. From the comprehensive comparison, it is concluded that anisotropy in the two metamodels clearly improves their accuracy. In general, anisotropic L2-SVR with the Matérn kernels is shown to be the most effective metamodel.