---
title: "pgvector & Vector Search: Kensink Labs"
description: "Vector similarity search in PostgreSQL with pgvector. Simpler RAG architecture from a senior lab, eval-tested, full source ownership."
source: "https://www.kensink.com/technologies/pgvector/"
canonical: "https://www.kensink.com/technologies/pgvector/"
---
★ pgvector Technologies & Infrastructure 8-week engagement

PGVECTOR · VECTOR SEARCH IN POSTGRES

# Vector search inside Postgres. Simpler RAG, fewer moving parts.

pgvector adds similarity search to PostgreSQL, so embeddings sit next to your relational data. For most RAG and semantic search, you do not need a separate vector database.

pgvector PostgreSQL Vector store

[Start a conversation →](https://www.kensink.com/contact) [All technologies →](https://www.kensink.com/technologies)

Cycle

8 weeks · fixed price

Stack

Postgres + pgvector

Output

Production code + eval suite

Handoff

Full source ownership

\[THE SHORT VERSION\]

## One database for relational and semantic search.

A dedicated vector database adds a system to operate, sync, and secure. pgvector keeps embeddings in Postgres, joined to the rows they describe, which simplifies most retrieval workloads. We reach for a specialized store only at large scale.

When it fits

-   RAG and semantic search at small to mid scale
-   Teams that already run Postgres and want fewer systems
-   Use cases that join embeddings to relational data

\[HOW WE BUILD IT\]

## How we build with pgvector.

01

### Scope and fit

We decide where pgvector earns its place in your system, and where a simpler tool wins. No resume-driven architecture.

02

### Build on a tested foundation

We integrate pgvector against a foundation we trust: typed code, CI, and observability from the first commit. Boring infrastructure, modern surface.

03

### Eval before launch

An eval suite proves the build behaves before it reaches a user. We measure, then ship.

04

### Handoff with ownership

Your team gets the code, the tests, and a runbook. No lock-in to us or to a vendor framework.

\[ WHAT YOU GET \]

## What the engagement leaves behind.

Senior

Engineers who have shipped this before

100%

Source ownership at handoff

Eval-first

Tested before it ships

0

Framework lock-in

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[View .md](https://www.kensink.com/technologies/pgvector.md)

\[RELATED\]

## Worth a look next.

[

Technologies

PostgreSQL

Read more

](https://www.kensink.com/technologies/postgresql)[

Design Patterns

RAG

Read more

](https://www.kensink.com/llm/rag)[

LLM Models

Embeddings

Read more

](https://www.kensink.com/models/embeddings)

APPLIED K-FRAMEWORK

## Bring the problem.  
We’ll bring the build.

Senior engineers, eval suite at handoff, full source ownership. Sprint, program, or ongoing. We shape the engagement to the work.

[Start a conversation →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework)