Yes, but it's important to note that it's quite a specialized index. - It's an index that doesn't depend on the data, unlike traditional r-tree indices. This means non-uniform data won't be queried as efficiently as with rtree, but it may be faster to build.

- This data independence property is VERY important for distributed or streaming queries. For example, if you want to join datasets using Spark or other big data tools, each team can add a column for h3 cells independently and join somewhat efficiently. For large volumes of data, constructing the rtree is just not feasible, or more precisely, very disconnected from the rest of the "data ecosystem".

- It doesn't work with any coordinate reference system other than EPSG 4326 (which you may want if you only work on specific geographies to get more precision in your floats)

- It's clearly built with points in mind. Polygons, curves, or lines are an afterthought. For example, the polygonToCells function returns a set of cells that are entirely within the polygon. If you want to join, you'd need to also have the set of all cells that entirely contain the polygon. I've never found a reliable way to get that.

That being said, it's not bad at all, but if you don't have so much data that you can't compute rtree indices, just stick with PostGIS.