Supabase Vector
SupabaseThe budget-friendly, developer-loved way to add vector search to a Postgres stack you already run.
Score breakdown
Our verdict
For clients who already run Postgres and don't want another vendor in the stack, Supabase's pgvector integration is often the pragmatic choice — one database, simpler billing, full SQL for combining structured and semantic queries.
Once query volume and index size get large, we've seen purpose-built vector databases like Pinecone pull ahead on latency — worth planning for that migration path if you expect to scale hard.
Pros & cons
Pros
- ✓Vectors live alongside relational data — one database, one bill, simpler ops
- ✓Generous free tier and predictable, low pricing at small-to-mid scale
- ✓Full SQL access — easy to combine semantic and structured filtering
- ✓Strong developer experience and fast-growing community
Cons
- –Query latency at very large scale trails purpose-built vector databases like Pinecone
- –Index tuning requires more Postgres knowledge than a managed vector API
- –Less specialized tooling for advanced RAG patterns out of the box
Ideal for
- Teams already running Postgres who want to avoid a new vendor
- Cost-sensitive projects at small-to-mid scale
- Use cases mixing structured filters with semantic search
- Startups wanting to move fast on a familiar stack
Pricing
Generous free tier · Pro from ~$25/mo · usage-based scaling beyond that
Questions, answered.
At small-to-mid scale, the difference is often negligible; at very large scale, dedicated vector databases tend to hold latency advantages.
Yes — that's one of its biggest advantages, since it's all standard Postgres underneath.
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