We present a new method for instance-specific algorithm configuration (ISAC). It is based on the integration of the algorithm configuration system GGA and the recently proposed stochastic offline programming paradigm. ISAC is provided a solver with categorical, ordinal, and/or continuous parameters, a training benchmark set of input instances for that solver, and an algorithm that computes a feature vector that characterizes any given instance. ISAC then provides high quality parameter settings for any new input instance. Experiments on a variety of different constrained optimization and constraint satisfaction solvers show that automatic algorithm configuration vastly outperforms manual tuning. Moreover, we show that instance-specific tuning frequently leads to significant speed-ups over instance-oblivious configurations.