Vector Search

Weaviate vs Qdrant: Which is Better for Vector Search? Quick Verdict For teams with a budget over $10,000 per year and requiring advanced hybrid search capabilities, Weaviate is the better choice due to its robust feature set and scalability. However, for smaller teams or those on a tighter budget, Qdrant’s more affordable pricing model and ease of use make it a more suitable option. Ultimately, the decision between Weaviate and Qdrant depends on the specific needs and constraints of your project. ...

Does Supabase Have Vector Search? (2026 Update) The Short Answer: Yes Supabase supports vector search through its integration with pgvector, a PostgreSQL extension that allows for efficient vector similarity searches. This means that while Supabase itself does not natively implement vector search, it can leverage the capabilities of pgvector to enable this functionality for users, particularly those working with AI embeddings. How to Use Vector Search in Supabase (If Yes) Navigate to the Supabase SQL editor or use the Supabase CLI to create a table with a column of type vector using the pgvector extension. This will allow you to store vector embeddings in your database. Click on the “Extensions” tab in the Supabase dashboard and ensure that the pgvector extension is enabled for your project. If it’s not enabled, you can enable it from this tab. Result: With pgvector enabled and your table set up, you can now use SQL queries to perform vector searches. For example, you can use the l2_distance or cosine_distance functions provided by pgvector to find the closest matches to a given vector, which is particularly useful in applications involving AI embeddings. Workarounds (If No) Since the question is about the existence of vector search and Supabase does support it via pgvector, this section is not applicable in the context of needing a workaround for a missing feature. However, for completeness and to address potential limitations or edge cases: ...