Amazon S3 Vectors is the first cloud object storage with native support for storing and querying vectors at scale. It delivers purpose-built, cost-optimized vector storage for AI agents, RAG, inference, and semantic search, supporting up to 2 billion vectors per index with sub-100ms query latency and up to 90% cost reduction compared to dedicated vector databases.

The S3 Vectors Wrapper brings your vector indexes into Postgres as queryable foreign tables. Insert, query, and delete vectors with plain SQL, perform approximate nearest neighbor (ANN) similarity search using the <=> operator, and filter results by metadata. Works with Supabase Vault for secure credential management and uses the same two-secret pattern as the S3 and Iceberg wrappers.

Supported Operations

Object	Select	Insert	Update	Delete
Vector Indexes	✅	✅		✅

Supported Data Types

Postgres Type	S3 Vectors Type
text	String
boolean	Boolean
smallint	Int16
integer	Int32
bigint	Int64
real	Float32
double precision	Float64
jsonb	JSON object
s3vec	Float32 array (vector)

Resources

• supabase/wrappers on GitHub