(TimescaleDB engineer here) There are major feature and capabilities available in TimescaleDB that are not available in pg_partman. On the query side we implem

(TimescaleDB engineer here) There are major feature and capabilities available in TimescaleDB that are not available in pg_partman. On the query side we implement a whole bunch of planner and execution time optimizations that don't come with plain PostgreSQL (and pg_partman does not implement any query optimizations AFAIK). These include optimizations that have to do with ordering based on time_bucket/date_trunc, execution-time chunk exclusion, etc. These result in query speedups of more than 1000x on many common time-series queries. TimescaleDB is much more automated than pg_partman and thus easier to maintain and administer. There are a lot less knobs to tune and a lot less things to go wrong in TimescaleDB. We implement analytical features necessary for time-series analyis: gap-filling, common time-series functions liked time_bucket, first, last, etc. We also implement a lot of data management functionality geared towards time-series data: scheduled data reordering, schedule data dropping/expiration, etc. This past Monday we released major feature called continuous aggregates. That automatically maintain a materialized view of aggregates over your time-series data, updating it as new data comes in and correctly handling backfilled data as well. The two projects are really not comparable in breadth or scope IMHO.