SQLite-Vector
SQLite Vector is a cross-platform, ultra-efficient SQLite extension that brings vector search capabilities to your embedded database. It works seamlessly on iOS, Android, Windows, Linux, and macOS, using just 30MB of memory by default. With support for Float32, Float16, BFloat16, Int8, UInt8 and 1Bit, and highly optimized distance functions, it’s the ideal solution for Edge AI applications.
Installed by default in SQLite CloudGitHub: https://github.com/sqliteai/sqlite-vector
Highlights
- No virtual tables required – store vectors directly as
BLOBs in ordinary tables - Blazing fast – optimized C implementation with SIMD acceleration
- Low memory footprint – defaults to just 30MB of RAM usage
- Zero preindexing needed – no long preprocessing or index-building phases
- Works offline – perfect for on-device, privacy-preserving AI workloads
- Plug-and-play – drop into existing SQLite workflows with minimal effort
- Cross-platform – works out of the box on all major OSes
Why Use SQLite-Vector?
| Feature | SQLite-Vector | Traditional Solutions |
|---|---|---|
| Works with ordinary tables | ✅ | ❌ (usually require special virtual tables) |
| Doesn’t need preindexing | ✅ | ❌ (can take hours for large datasets) |
| Doesn’t need external server | ✅ | ❌ (often needs Redis/FAISS/Weaviate/etc.) |
| Memory-efficient | ✅ | ❌ |
| Easy to use SQL | ✅ | ❌ (often complex JOINs, subqueries) |
| Offline/Edge ready | ✅ | ❌ |
| Cross-platform | ✅ | ❌ |
Unlike other vector databases or extensions that require complex setup, SQLite-Vector just works with your existing database schema and tools.