Elasticsearch vs pgvector (PostgreSQL Extension)
Side-by-side from the Top 11 ranking of The 11 Best Vector Databases (2026). Last verified May 31, 2026.
The short answer
Elasticsearch ranks higher on Top 11 (#7 vs #11) for AI engineers building RAG and semantic search at scale. A mature, integrated solution for teams already using the Elastic stack.
At a glance
| Elasticsearch | pgvector (PostgreSQL Extension) | |
|---|---|---|
| Top 11 rank | #7 / The 11 Best Vector Databases (2026) | #11 / The 11 Best Vector Databases (2026) |
| Score (out of 9.4) | 8.3 | 7.2 |
| Best for | Vector search for existing Elastic users | Vector search inside PostgreSQL |
| Pricing | $$$ (Free to custom/enterprise) | $ (Open source) |
| HQ | Mountain View, USA | Open Source |
| Founded | 2012 | — |
pgvector (PostgreSQL Extension)
A pragmatic choice for adding vector search to an existing Postgres stack.
github.com/pgvector/pgvectorMethodology and scoring weights live at /methodology. No vendor pays for placement — see about.