R-vs-Python is almost never the problem in production. Interpreted-vs-compiled is almost always the issue. (I'm aware of Numba and similar efforts. Last time I tried it, it sucked on nuts. And Theano is a rather specialized bit that most people don't actually need.)

JMHO. But I've never seen anyone dealing with truly huge data and inference problems that had the low-level bits in anything other than C++ or FORTRAN. I could imagine that Scala can do a pretty good job now, especially if you use Spark a lot. But R vs Python seems like a really stupid question. Use the one that has the libraries you need.