1) We analyze the different tradeoffs and goals of Grid, Cloud and parallel (cluster/supercomputer) computing. 2) They tradeoff performance, fault tolerance, ease of use (elasticity), cost, interoperability. 3) Different application classes (characteristics) fit different architectures and we describe a hybrid model with Grids for data, traditional supercomputers for large scale simulations and clouds for broad based "capacity computing" including many data intensive problems. 4) We discuss the impressive features of cloud computing platforms and compare MapReduce and MPI. 5) We take most of our examples from the life science area. 6) We conclude with a description of FutureGrid -- a TeraGrid system for prototyping new middleware and applications.