Second, SVMs---especially with a non-linear kernel---are much trickier to tune than logistic. SVMs are very sensitive to choice of hyper-parameters (user specified tuning weights), which means repeatedly retraining. And if you use too powerful of a kernel, you will overfit your data very easily.
For these reasons, my advice is to start with logistic, and then if you're not satisfied, switch to SVMs.
(As an aside, it's totally possible to kernelize logistic regression. Without suitable regularization, it'll be even worse with regards to the number of datapoints you need though.)