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# You Don't Need a Vector Database, Postgres Already Has pgvector

**[Prisma Blog](https://daily.dev/sources/prisma-blog)** · 11 min read · 0 upvotes · 0 comments

## Summary

pgvector is a Postgres extension that adds a vector column type and cosine similarity operators, enabling semantic search directly in Postgres without a dedicated vector database like Pinecone or Weaviate. The post walks through building a semantic search demo using Prisma Next and its @prisma-next/extension-pgvector pack, which provides type-safe vector operations, a Vector<N> type tied to declared dimensions, and automatic extension migration. The tutorial covers spawning a temporary Prisma Postgres database, declaring a schema with a vector column, inserting embeddings, and querying by cosine similarity — all with compile-time type checking. A comparison table outlines when pgvector is sufficient (up to a few million vectors, apps already on Postgres) versus when a dedicated vector engine is warranted (billion-scale ANN, hybrid search features).

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.prisma.io/blog/you-dont-need-a-vector-database-postgres-already-has-pgvector>

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---

Tags: [#postgresql](https://daily.dev/tags/postgresql), [#vector-search](https://daily.dev/tags/vector-search), [#prisma](https://daily.dev/tags/prisma), [#pgvector](https://daily.dev/tags/pgvector)

[View this post on daily.dev](https://daily.dev/posts/you-don-t-need-a-vector-database-postgres-already-has-pgvector-d62seekr1)

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