Pilosa actually uses Roaring internally for bitmap compression (in fact, one of the Pilosa devs did the first port of Roaring from Java to Go). One difference is that really wide bitmaps are sharded and distributed among nodes in the cluster. This allows Pilosa to parallelize the work of doing bitwise operations on high cardinality data.

I'll add to travisturner's response - Pilosa has a few bells and whistles beyond being a straight up bitmap index including:

- associating each bit with a timestamp (at various granularities) and queries over time ranges.

- adding arbitrary key/value metadata to each row or column

- automatic sorting/caching of bitmaps to support "TopN" queries