Master Supabase Vector with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Define your first Supabase Vector use case and success metric. Connect a foundation model and configure credentials. Attach retrieval/tools and set guardrails for execution. Run evaluation datasets to benchmark quality and latency. Deploy with monitoring, alerts, and iterative improvement loops.
💡 Quick Start: Follow these 1 steps in order to get up and running with Supabase Vector quickly.
Explore the key features that make Supabase Vector powerful for ai memory & search workflows.
Native PostgreSQL extension for storing and indexing high-dimensional vectors with HNSW and IVFFlat index types for efficient approximate nearest neighbor search
Storing 500,000 document embeddings and querying the top 10 most similar results in under 50ms using HNSW indexing
Combine vector similarity search with PostgreSQL full-text search and standard SQL WHERE clauses in a single query, filtering by metadata, date ranges, or categories alongside semantic matching
Finding the most semantically relevant support articles that were also published in the last 30 days and tagged with a specific product category
PostgreSQL's row-level security policies apply to vector tables, ensuring each user or tenant can only search and retrieve their own embeddings
Building a multi-tenant RAG application where each customer's knowledge base is isolated so users only retrieve results from their own documents
Serverless Edge Functions with pre-built templates for generating embeddings via OpenAI, Hugging Face, and other providers, then storing them directly in the database
Creating an API endpoint that accepts a document, generates its embedding via OpenAI, stores it in pgvector, and returns a confirmation in one Edge Function
Supabase's real-time subscriptions work with vector tables, enabling live notifications when new embeddings are added or existing data changes
Building a knowledge base that notifies connected clients when new documents are indexed, keeping search results fresh without polling
Vector search lives alongside Supabase Auth, Storage, Edge Functions, and real-time subscriptions. One platform, one set of credentials, one billing relationship
Building a complete RAG chatbot backend with user authentication, document storage, embedding search, and real-time streaming all from Supabase
Supabase Vector inherits PostgreSQL's mature reliability features: WAL-based crash recovery, point-in-time restore, and read replicas. The managed platform provides automatic daily backups, monitoring dashboards, and connection pooling via PgBouncer. High availability with automatic failover is available on Pro and Enterprise plans.
Yes. Since Supabase Vector is built on pgvector and PostgreSQL, you can self-host by running PostgreSQL with the pgvector extension on any infrastructure. Supabase itself is open-source and can be self-hosted via Docker. The self-hosted route requires manually configuring the Supabase stack (PostgREST, GoTrue, etc.) and managing PostgreSQL operations.
Supabase pricing is based on database size, compute, and bandwidth. Vector operations don't incur separate charges. Optimize by choosing smaller embedding dimensions (e.g., 384 instead of 1536), using HNSW indexes instead of exact search for large tables, and implementing caching for frequent queries. The free tier includes 500MB of database storage, sufficient for tens of thousands of embeddings.
Supabase Vector trades raw vector search performance at scale for platform simplicity. If your application already uses Supabase for auth, storage, and APIs, adding vector search is nearly frictionless. Pinecone and Qdrant will outperform pgvector for datasets with tens of millions of vectors and offer features like automatic scaling, quantization, and horizontal sharding that pgvector lacks.
Very low. Your vector data, indexes, and SQL queries work on any PostgreSQL instance with pgvector installed. The Supabase platform features (Auth, Edge Functions, real-time) create some coupling, but the core vector functionality is portable. Export using standard pg_dump or COPY commands.
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Tutorial updated March 2026