Hire Expert
Supabase Developers

The single biggest reason teams choose Supabase — and the area where the most damage is done by inexperienced developers — is Row Level Security. Because Supabase exposes your tables directly through an auto-generated API protected by the anon and authenticated keys, the database itself must be the security boundary. If RLS is disabled, or a policy is missing on even one table, that table is effectively public to anyone with the anon key. Our engineers enable RLS on every table by default, write explicit policies per operation (select, insert, update, delete), key them on auth.uid() and custom JWT claims for organization and role, and verify isolation by running queries as the anon and authenticated roles — not by assuming the client will behave. The service-role key, which bypasses RLS entirely, is treated as a server-only secret and never shipped to the browser or an edge function reachable by clients.

For multi-tenant B2B SaaS this is the whole game. We model tenants explicitly (organizations, memberships, roles), put the tenant identifier in the JWT, and write policies that scope every read and write to the caller's tenant. Combined with Postgres foreign keys, check constraints, and triggers for audit trails, the result is a security and integrity model you can actually test and prove — rather than a pile of application-layer if-statements that one forgotten code path can defeat.

On the AI side, Supabase's pgvector extension lets you store embeddings in the same Postgres database as your application data, which removes the need for a separate vector store for most products. We enable the vector type, choose sensible embedding dimensions, build IVFFlat or HNSW indexes for the dataset size, and write similarity-search SQL functions that power semantic search, document Q&A, and retrieval-augmented generation. Keeping embeddings next to relational data means a single RLS-protected query can join a user's documents, their metadata, and the nearest vectors — clean, secure, and fast — which is exactly the kind of AI feature we build on top of an existing Supabase backend without forcing a migration to another database.