Introducing: Postgres Best Practices
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Introducing Vector Buckets
supabase.comSaved December 25, 2025
Summary
Supabase Vector Buckets provide S3-backed storage for up to tens of millions of high-dimensional vectors per index with built-in similarity search using cosine, euclidean, or L2 metrics.
Users create buckets and indexes via dashboard or SDK, insert vectors with optional metadata, and query via Supabase clients or Postgres for k-NN searches and filtering. They complement pgvector for large-scale datasets in semantic search, recommendations, and RAG, targeting sub-second performance for most workflows.
