Is Clustering used in real world systems/products involving large amounts of data? How are the nuances taken care of?
Clustering (mostly) does not apply to huge data.

Since you cannot automate result evaluation and validation (forget the 'internal' measures, they are useless) nor use the automatic results in any meaningful way (partially because clustering is just way too unreliable for automatic use).

Clustering requires you to study the result, learn, and then decide what to do next. You can't automate this.

It is explorative. A tool for you to study your data; not a tool for machines to automatically predict labels. Some clusters will be good, some will be bad. This is fine, you got some good clusters! Just don't rely on everything being clustered. There will always be data that does not belong to any cluster. And often, there will be clusters that just don't make any sense to you, or that are too trivial to be of use or interest.
Results just do not improve with data size.
Methods don't scale well.

Most clustering methods (including k-means) have worst case runtimes of O(n^2) or O(n^3). That is really problematic. But since they are analyzing an NP-hard problem, that is quite decent.

So I guess, you want to work on a sample.
RAM is big these days, and vectors are compact.

You can usually squeeze a billion vectors into RAM. So anything that - like k-means - works with numerical vectors (and not, say, videos and images) just fits into main memory. No need to go larger than that.