Serverless vector search: No cold starts. No size limits.
Firebase, Cloudflare Vectorize — convenient but constrained. LanceDB gives you serverless simplicity with petabyte scale and native hybrid search.
Tomorrow's AI is being built on LanceDB today
Stop paying document read costs for vector scans
Firestore charges per document read. Vector search scans lots of documents. That math gets expensive fast. LanceDB runs on Cloud Storage at object storage prices with native hybrid search.
| Firebase / Firestore | LanceDB | |
|---|---|---|
| Cost | Per-document read pricing | Object storage rates. Up to 100x savings. |
| Scale | Firestore document limits | 20 PB largest table. Billions of vectors. |
| Search | Vector via extension. No hybrid. | Native vector + full-text + SQL hybrid in one query. |
| Data model | Document-first | Columnar, vector-native with schema evolution. |
| Best for | Small vector sets in Firebase apps | Vector search at any scale. |
Edge vectors are for tiny datasets. LanceDB is for the rest.
Cloudflare Vectorize is great for edge-cached vector lookups on small datasets. When you need real scale and hybrid search, you need a real vector database.
| Cloudflare Vectorize | LanceDB | |
|---|---|---|
| Scale | Edge-optimized, size-limited | 20 PB largest table. 20K+ QPS. Billions of vectors. |
| Architecture | Edge-distributed | Object storage with compute-separate separation. |
| Search | Basic vector search | Native vector + full-text + SQL hybrid in one query. |
| Features | Basic vector search | Search, analytics, schema evolution. |
| Best for | Tiny vector sets at the edge | Production-scale vector workloads |
Talk to Engineering
Or try LanceDB OSS — same code, scales to Cloud.