What's the difference between using libSVM in sci-kit learn, or e1070 in R, for training and using support vector machines?
I do not have experiece with e1070, however from googling it it seems that it either uses or is based on LIBSVM (I don't know enough R to determine which from the cran entry). Scilearnkit also uses LIBSVM. 
In both cases the model is going to be trained by LIBSVM. Speed, scalability, variety of options available is going to be exactly the same, and in using SVMs with these libraries the main limitations you will face are the limitations of LIBSVM.
I think that giving further advice is going to be difficult unless you clarify a couple of things in your question: what is your objective? Do you already know LIBSVM? Is this a learning project? Who is paying for your time? Do you feel more comfortable in Python or in R?
Recently I was contemplating the choice of using either R or Python to train support vector machines.
Aside from the particular strengths and weaknesses intrinsic to both programming languages, I'm wondering if there is any heuristic guidelines for making a decision on which way to go, based on the packages themselves.
I'm thinking in terms of speed of training a model, scalability, availability of different kernels, and other such performance-related aspects.
Given some data sets of different sizes, how could one decide which path to take?  
I apologize in advance for such a possibly vague question.