# Vector Databases Clearly Explained

DevFeed: [Vector Databases Clearly Explained](<https://devfeed.tech/articles/vector-databases-clearly-explained-18041.md>)

Original publisher: [Read original article](<https://blog.levelupcoding.com/p/vector-databases-clearly-explained>)

Author: Nikki Siapno

Published: 2026-07-25T13:32:58Z

Content type: tutorial

Language: en

Sources: [Level Up Coding System Design Newsletter](<https://devfeed.tech/sources/level-up-coding-system-design-newsletter.md>)

Topics: [Databases](<https://devfeed.tech/topics/databases.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>)

Tags: [databases](<https://devfeed.tech/tags/databases.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [search](<https://devfeed.tech/tags/search.md>), [technical](<https://devfeed.tech/tags/technical.md>), [vector](<https://devfeed.tech/tags/vector.md>), [vector-database](<https://devfeed.tech/tags/vector-database.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>)

## AI overview

This tutorial explains how vector databases differ from traditional databases by searching for semantic similarity rather than exact keyword matches. It describes embeddings, vectors, metadata, filtering, permissions, ranking, and distance metrics as parts of vector search.

## Source excerpt

The mental model that makes vector databases click.