Introducing Vector Buckets

Vector Buckets offer a scalable solution for storing and querying large sets of high-dimensional vectors, enabling features such as semantic search, recommendations, and media similarity detection, by integrating with Supabase and Postgres. Designed to handle workloads up to tens of millions of vectors, Vector Buckets store embeddings in S3-backed object storage, allowing for efficient similarity search without overloading traditional database schemas. They support various applications, including AI documentation search, product recommendations, and media de-duplication, by using vector indexes that can be queried through familiar Supabase SDKs or directly via Postgres. The service, currently in Public Alpha, is free to use under a fair use policy, with plans to evolve based on user feedback and real-world usage.

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