Get an in-depth look at how two leading time-series databases stack up. We compare data models, query language, reliability, performance, and more.

PostgreSQL insights for every developer journey. Get tips and tutorials on database performance, query optimization, time-series data, and more—from real-time analytics and IoT data management to events and vector embeddings. 


TigerData (Creators of TimescaleDB)

TimescaleDB vs. InfluxDB: Purpose Built Differently for Time-Series Data is an in-depth look into how two leading time-series databases stack up in terms of the data model, query language, reliability, performance, ecosystem, operational management, and company/community support.

TimescaleDB vs. InfluxDB: Purpose-built for time-series data

I’ve worked with TimescaleDB and InfluxDB in the past and from what I can tell, if possible, TimescaleDB is the much better engine/ extension.
You can do everything you normally do but within your DB, you can connect your data.
One thing I don’t understand is why the author choose Postgres 12 – the last few major releases bring some major performance improvements.