LanceDB vs turbopuffer
Comparing two vector database platforms on pricing, features, free tier, and trade-offs.
Quick summary
LanceDB — Serverless vector database for multimodal AI. LanceDB is an open-source serverless vector database with embedded and cloud options, native multimodal support, and a columnar on-disk format.
turbopuffer — Serverless vector search on object storage. turbopuffer is a serverless vector database built on S3, offering very cheap storage pricing and pay-per-query model — designed for RAG at scale without fixed pod costs.
Feature comparison
| Feature | LanceDB | turbopuffer |
|---|---|---|
| Pricing model | Freemium | Paid |
| Starting price | $50/mo | Usage-based |
| Free tier | Yes | No |
| Open source | Yes | No |
| Type | Embedded + cloud | Serverless |
| Free Tier | Open source unlimited | None |
| Serverless | Yes | Yes |
| Self-hosted | Yes | No |
| Multi-tenant | Yes | Yes |
| Hybrid Search | Yes | Yes |
| Max Dimensions | 32768 | 10000 |
| Metadata Filtering | Yes | Yes |
LanceDB
Serverless vector database for multimodal AI
Pros
- Embedded mode — no server needed
- Columnar format (great for analytics)
- Strong multimodal support
- Open source
Cons
- Younger project, smaller community
- Less battle-tested in production
- Cloud tier newer than competitors
turbopuffer
Serverless vector search on object storage
Pros
- Storage on S3 — extremely cheap
- Pay per query, no pod hours
- Good for cold / infrequently-queried data
- Simple API
Cons
- Higher query latency than Pinecone/Qdrant
- No free tier
- Closed source
Which should you choose?
Choose LanceDB if you value open source and want the option to self-host, and a free tier is important for your stage. Choose turbopuffer if you need production-grade features and are ready to pay.