B-Trees are most often used for database indexes on hard disk, but they have advantages even as an in-memory data structure, given the modern memory heirarchy with multiple layers of cache and with virtual memory. Even if virtual memory is on an SSD, that won't change.
I use an in-memory B+-style multiway tree library that I wrote quite a lot in C++. It can have performance advantages - the reason it was originally written was to try to use cache better - but I have to admit it often doesn't work that way. The problem is the trade-off which means items have to move around within nodes on inserts and deletes, which doesn't happen for binary trees. Also, some of the low-level coding hacks I used to optimise it - well, they probably confuse and defeat the optimiser, truth told.
Anyway, even if your databases are stored on an SSD, that's still a block-oriented storage device, and there's still an advantage to using B-Trees and other multiway trees.
BUT about ten years ago, cache-oblivious algorithms and data structures were invented. These are oblivious to the size and structure of caches etc - they make (asymptotically) the best possible use of any memory heirarchy. B-Trees need to be "tuned" to a particular memory heirarchy to make the best use (though they work fairly well for quite a wide range of variation).
Cache oblivious data structures aren't often seen in the wild yet, if at all, but it time they may well make the usual in-memory binary trees obsolete. And they may also prove worthwhile for hard disks and SSDs as well, since they don't care what the cluster-size or hard-disk cache page size is.
Van Emde Boas layout is very important in cache-oblivious data structures.
The MIT OpenCourseware algorithms course includes some coverage of cache oblivious data structures.