---
title: "How does an embedding index differ from a vector database?"
description: "A vector database stores vectors and answers similarity queries. An embedding index is that search capability declared on the same table that holds the source pieces."
url: "https://pixeltable.com/learn/embedding-index-vs-vector-database"
updated: "2026-09-29"
vertical: "Retrieval"
doc: "https://docs.pixeltable.com/datastore/embedding-index"
---

# How does an embedding index differ from a vector database?

A vector database stores vectors and answers similarity queries. An embedding index is that search capability declared on the same table that holds the source pieces.

Updated: 2026-09-29
Part of [What is an embedding index?](https://pixeltable.com/learn/what-is-an-embedding-index).

## On this page

- [How it works](https://pixeltable.com/learn/embedding-index-vs-vector-database#how-it-works)
- [What it is not](https://pixeltable.com/learn/embedding-index-vs-vector-database#what-it-is-not)
- [Comparison](https://pixeltable.com/learn/embedding-index-vs-vector-database#comparison)
- [Where Pixeltable fits](https://pixeltable.com/learn/embedding-index-vs-vector-database#where-pixeltable-fits)
- [Questions](https://pixeltable.com/learn/embedding-index-vs-vector-database#questions)

## How it works {#how-it-works}


- The vector database is a product whose rows are vectors plus metadata.
- The embedding index is a structure on a table that also stores the passage, frame, or file.
- You pick a separate vector database when something else owns the source of truth. You declare an index when the table already does.

## What it is not {#what-it-is-not}

Replacing a vector database does not, by itself, give you RAG. Chunking, staying in sync, and calling the model are still separate jobs.

## embedding index versus a vector database: this, and the thing it is confused with {#comparison}

|  | This | Not this |
| --- | --- | --- |
| Source of truth | The table, if the index is on it | The vector database, if you copied vectors in |
| Sync | The row update | An ETL job |
| RAG | Still needs chunks and a model call | Not finished when the index exists |

## Where Pixeltable fits {#where-pixeltable-fits}

Pixeltable keeps the index on the table. Pinecone and LanceDB remain options when a separate vector store is the product you want. They are not required for the index itself.

## Questions {#questions}

### How does embedding index versus a vector database work? {#faq-1}

The vector database is a product whose rows are vectors plus metadata. The embedding index is a structure on a table that also stores the passage, frame, or file. You pick a separate vector database when something else owns the source of truth. You declare an index when the table already does.

### What is embedding index versus a vector database often confused with? {#faq-2}

Replacing a vector database does not, by itself, give you RAG. Chunking, staying in sync, and calling the model are still separate jobs.

## In the blog

- [Pixeltable + LanceDB Integration: AI Infrastructure with Seamless Vector Database Export](https://pixeltable.com/blog/pixeltable-lancedb-integration)
- [Pixeltable vs Pinecone: When You Need a Vector Database vs Unified AI Infrastructure](https://pixeltable.com/blog/pixeltable-vs-pinecone-vector-database-comparison)
- [Five Things Pixeltable Does That No Combination of LangChain, Pinecone, and Airflow Can](https://pixeltable.com/blog/five-things-pixeltable-does-competitors-cant)
- [S3 Files, Hugging Face Buckets, and the Storage Problem Pixeltable Already Solved](https://pixeltable.com/blog/s3-files-huggingface-buckets-pixeltable-storage)
- [The AI Database Landscape: Why Traditional Databases Fall Short for Multimodal AI](https://pixeltable.com/blog/ai-database-landscape-infrastructure-guide)
- [Why Your RAG Is Wrong: The Ultimate Guide to Production-Ready Embedding Management](https://pixeltable.com/blog/embedding-management-guide)

## Related

- [Documentation](https://docs.pixeltable.com/datastore/embedding-index)
- [Why AI Teams Are Switching from Vector Databases to Pixeltable for Multimodal Applications](https://pixeltable.com/blog/teams-switching-pixeltable-vector-databases)
- [Pixeltable vs Pinecone: When You Need a Vector Database vs Unified AI Infrastructure](https://pixeltable.com/blog/pixeltable-vs-pinecone-vector-database-comparison)
- [Pixeltable + LanceDB Integration: AI Infrastructure with Seamless Vector Database Export](https://pixeltable.com/blog/pixeltable-lancedb-integration)
- [Pixeltable vs Pinecone](https://pixeltable.com/compare/pixeltable-vs-pinecone)
- [Pixeltable vs LanceDB](https://pixeltable.com/compare/pixeltable-vs-lancedb)