At a high-level, we've taken a unique approach to the time-series data problem. While every other time-series db has taken a non-relational approach (which also means no or poor SQL support), we've built ours on Postgres. As a result, not only do we support full SQL (JOINs, complex WHEREs/GROUPBYs, window functions, etc), but we also inherit the reliability and vast ecosystem of Postgres.

At a more detailed level, comparisons between TimescaleDB and others varies depending on the database. Typically though, if you are most concerned with data compression or simple column scans, some of the other options might be more suitable than ours. But otherwise you should see similar (if not better) insert and query performance, while maintaining all the other benefits of a relational DB / Postgres.

And with your relational data living alongside time-series data, many people have been able to simplify their stack (from multiple dbs into one db), which makes things easier to manage operationally and at the application level.

Re: OpenTSDB, we hear that managing an HDFS cluster / Zookeeper / etc can be a headache. You obviously don't have that problem with TimescaleDB.

Re: Prometheus, I should add that we've open-sourced `pg_prometheus` [1], which allows you to write Prometheus data into Postgres / TimescaleDB. So with regards to Prometheus, we are more of a complement than a replacement.

[1] https://github.com/timescale/pg_prometheus