It is incorrect that R-trees can't store points. They are designed to support rectangles, and will need to do so at inner nodes. But a good implementation should store points at the leaf level, and roughly have the double data capacity there.
You can trivially store point, and expose them as a "rectangles" with min=max to the tree management code.
Your data isn't small. Small would be like 100 objects. For 100 objects, an R-tree won't make much sense, as it would likely consists of a single leaf only. For good performance, an R-tree needs a good fan-out. k-d-tree always have a fan-out of 2; they are binary trees. At 100k objects, a k-d-tree will be pretty deep. Assuming that you have a fanout of 100 (for dynamic r-trees, you then should allow up to 200 objects per page), you can store 1 million points in a 3-level tree.
I've used the ELKI R*-tree, and it is really fast. But it's not commercial friendly, unless you get a different license: it's AGPL-3 licensed, which is a copyleft license.
Furthermore, the API isn't designed for standalone use. If you want to use them, the best way is to work with the full ELKI framework, instead of trying to rip out the R*-tree.
If your data is low dimensional (say, 3-dimensional) and has a finite bound, don't underestimate the performance of simple grid-based approaches. In particular for in-memory operations. In many cases, I wouldn't even go to an Octree, but just define the optimal grid for my use case, and then implement it using object lists. Keep sorted by one coordinate within each grid cell to further accelerate performance.