---
title: How Vector Search Works
description: Understand how ParadeDB natively supports vector types
canonical: https://www.paradedb.com/docs/documentation/vector/overview
---

<Note>This is a beta feature available in versions `0.25.0` and above.</Note>

Today, most Postgres users rely on the [pgvector](https://github.com/pgvector/pgvector) extension for similarity (i.e. vector) search. pgvector works well for many use cases, but has a few limitations:

- **Filtering Performance:** Its indexes are separate from the ParadeDB index, so performance suffers when composing vector search with text search and other filters.
- **Memory Constraints:** Index build times balloon when the entire index does not fit in memory, preventing pgvector from scaling to large datasets.
- **Index Quality Under Updates:** Indexes can degrade over time under update-heavy workloads.

The ParadeDB index supports pgvector's `vector` type and is designed to solve these limitations. Vectors are indexed with a SPANN-style index, the state-of-the-art approach for billion-scale vector search.

Because vectors live in the same index as text and filters, vector search can also be combined with [text search](/documentation/full-text/overview) into a single ranking. See [Hybrid Search](/documentation/hybrid/overview).
