Stay free if you only need 500mb database including vector storage and pgvector extension enabled. Upgrade if you need 8gb database with vector storage and no project pausing. Most solo builders can start free.
Why it matters: pgvector performance degrades beyond a few million vectors. Dedicated vector databases like Pinecone or Qdrant significantly outperform at scale
Available from: Pro ($25/month)
Why it matters: Embedding generation must happen externally or through Edge Functions. No built-in model hosting for creating embeddings from raw text
Available from: Pro ($25/month)
Why it matters: Limited vector-specific features compared to dedicated solutions: no built-in quantization, named vectors, or horizontal sharding for vectors
Available from: Pro ($25/month)
Why it matters: PostgreSQL expertise required for complex performance tuning. Choosing between HNSW vs IVFFlat indexes and configuring parameters (ef_construction, m, lists) demands database knowledge
Available from: Pro ($25/month)
Why it matters: Scaling beyond single-node PostgreSQL limits requires Supabase's higher-tier plans or manual read replica configuration
Available from: Pro ($25/month)
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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Last verified March 2026