There are reasons to fit both a Kaplan-Meier estimator and a parametric or semi-parameteric survival function to the same data, even if they are doing the same thing.
The first of these is as a sanity check - do different methods give you similar answers? If not, why not? This is often why the Kaplan-Meier is fitted - as a means to evaluate whether or not a parametric estimator is performing well and to provide a comparison.
Now, why you may want to do this? Sometimes a parametric version of a survival distribution is important. For example, when estimating a parameter that is going to be used in a mathematical/computational model, life is much easier if you can describe survival time parametrically. So you could fit say, an exponential distribution, and then use the KM to check and make sure that you haven't deviated too far from the actual survival distribution for the sake of convenience.
I've written papers that do exactly this.