I've made use of Google's S2 library across a couple different jobs for various use cases. The C++ bindings are well-documented and very fast, in fact I found writing my own Python bindings allowed a few faster operations than using the Python library directly, for example generating a spanning set of cell IDs.

I find it liberating to not rely on a backend implementation of geospatial indexing, or rather being agnostic to backend, simply indexing very efficiently the 64-bit integers.

The hierarchical nature of the Hilbert curves means that the child cells contain the ID of the parent, and also fit perfectly into the parent. This is in contrast with H3 where you necessarily cannot fit the 7 child hexagons into the parent without some overlap (see for example the logo on the H3 website https://eng.uber.com/h3/)