For one, as a package developer, publishing a source distribution of a package on PyPI is almost trivial. Publishing on Anaconda Cloud requires you to build the binary packages on all the OS's that you want to support (and for all Python versions you want to support) which most people delegate to some CI. So there is a whole new level of complexity involved.
You can use pip-install from within a conda environment. Conda isolates better than virtualenv does, IMHO.
When I have used it, and I have to for a certain project, it is incredibly slow to resolve depenendcies. Enough so that I go for a walk or do something else for 15 minutes while it thinks about whatever it's doing.
I "feel" like it is overkill to use anaconda for things unrelated to 'data science' and the likes, but I'm not sure why I feel that way.
It kind of makes sense to use for other projects as well since you don't need to import all the things conda offers.