The latter. Because of the table partitioning there is not much dependency between old and new data so we've seen consistent performance as you add more and more data.

TimescaleDB clustering is currently limited to a single primary with multiple read-only replicas (which provide both HA failover and scaling concurrent reads).

So the limitation here is the storage capacity available for the nodes, and so the above reflects the amount of data we've seen in use in various RAIDed or network-attached storage environments (like the cloud).

One interesting thing here is that you can "elastically" add a new disk to a existing hypertable, and new writes will be automatically load balanced across the new disk. (In Postgres speak, we support multiple tablespaces in a single hypertable, and we allow you to dynamically add a new tablespace to an existing one: https://docs.timescale.com/api#attach_tablespace )

While it's true column stores perform better on single-column aggregates, they perform worse on multi-column operations, thresholding queries, and other types of complex analytics that one often sees on time-series workloads. We have published benchmarks on some common column stores that show these tradeoffs.