Fair question. 100M isn't a ceiling, it's what we have seen in that deployment. We have not run a billion series test yet.

The reason we think it scales differently - labels are just columns in Parquet, so there is no per series index that grows with cardinality. In that deployment one label alone has ~2.5M distinct values among 500+ labels, which would be painful for an index based TSDB but here is just a high cardinality column. What drives cost for us is ingestion rate (data points/s) and how much data a query has to scan for a particular time range not series count. Ingest scales horizontally by adding ingestors, and queries prune by time partition and column stats.

A billion series benchmark is on our list, and we'll publish the numbers when we run it.