# Introducing Vector Buckets

DevFeed: [Introducing Vector Buckets](<https://devfeed.tech/articles/introducing-vector-buckets-706.md>)

Original publisher: [Read original article](<https://supabase.com/blog/vector-buckets>)

Author: Fabrizio Fenoglio

Published: 2025-12-01T07:00:00Z

Content type: article

Language: en

Sources: [Supabase Blog](<https://devfeed.tech/sources/supabase-blog.md>)

Topics: [Supabase](<https://devfeed.tech/topics/supabase.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>)

Tags: [amazon-s3](<https://devfeed.tech/tags/amazon-s3.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [object-storage](<https://devfeed.tech/tags/object-storage.md>), [rag](<https://devfeed.tech/tags/rag.md>), [search](<https://devfeed.tech/tags/search.md>), [storage](<https://devfeed.tech/tags/storage.md>), [vector](<https://devfeed.tech/tags/vector.md>)

## AI overview

Supabase introduces Vector Buckets, a durable S3-backed storage option for large collections of vectors with built-in similarity search. It complements pgvector for workloads involving up to tens of millions of vectors, while allowing queries through Supabase clients or Postgres.

## Source excerpt

Introducing vector storage in Supabase: a durable storage layer with similarity search built-in.