Lantern vs pgvector
A side-by-side comparison of Lantern and pgvector, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Lantern | pgvector |
|---|---|---|
| Category | Vector DB | Vector DB |
| Pricing (differs) | FREEMIUM | FREE |
| License (differs) | Open core | Open source |
| Deployment (differs) | Hybrid | Self-host |
| Platforms (differs) | API, Linux, macOS | API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Lantern | pgvector community |
| Capabilities (differs) |
|
|
The honest brief
Lantern
Adds production vector search inside Postgres itself — HNSW indexing and hybrid BM25 search with no separate vector store.
- Lives inside the Postgres you already run
- Open-source, self-host or managed cloud
- HNSW plus hybrid BM25 search
- Built-in embedding generation
- Tied to the Postgres ecosystem
- Smaller community than pgvector
- Managed cloud tier still maturing
pgvector
Keeps vectors in your existing Postgres, so you JOIN against relational data and back it all up together.
- No new database to operate
- JOIN embeddings with relational data
- Free and open source
- Works on Supabase, Neon, any managed Postgres
- Scales worse than dedicated vector DBs
- Tuning HNSW/IVFFlat is on you
- No built-in hybrid search out of the box
When to pick which
Across the signals we compare, Lantern and pgvector differ on pricing, license, deployment, and platforms:
- Pricing
- FREEMIUM · FREE
- License
- Open core · Open source
- Deployment
- Hybrid · Self-host
- Platforms
- API, Linux, macOS · API
Their capability lists differ in recorded depth — compare the full lists above before deciding.