Company
Publisher of the explainer and vendor of TimescaleDB; the post appears on timescale.com / tigerdata.com.
Tiger Data says pg_vectorsearch built a 100M-vector index in 49 minutes where pgvector's HNSW took 42 hours on the same 512 GiB machine. A DEV Community review calls the query win real and the build comparison tilted. The extension is still alpha.
Reality
Tiger Data says its pg_vectorsearch alpha answers at a 6 ms median and 95% recall on 100M vectors in its own tests. Its release notes say filtered queries can come back short of LIMIT, so we would keep pgvector HNSW in place for now.
Reality
The extension puts BM25 ranking behind an ordinary Postgres index, so a newly committed row is searchable without a trip to an Elasticsearch cluster. PlanetScale's own benchmark reports 199 queries per second against ParadeDB's 7.9.
Reality
Gleb Tcivie's open source monitoring tool outgrew Prometheus once node identity in metric labels blew past the 10-second scrape budget. It now runs on one Postgres database with TimescaleDB.
Reality
A Tiger Data explainer traces the gap to late-arriving rows, and to the fact that four refresh strategies filed under one label disagree precisely there.
Reality