# Embedded

> Browse 14 items tagged with Embedded.

## Items

- [SurrealDB](https://vectordb.ever.works/items/surrealdb) — Multi-model database with vector search, graph queries, full-text keyword search, and real-time subscriptions for hybrid vector+keyword+graph retrieval. Ideal for multimodal RAG in full-stack apps. More versatile than pure vector DBs like Qdrant with embedded/multi-model support.
- [VelesDB](https://vectordb.ever.works/items/velesdb) — Embedded vector + graph + columnar database with HNSW indexing.
- [Chroma Local Embedding Database](https://vectordb.ever.works/items/chroma-local-embedding-db) — Lightweight embedded vector store for low-latency on-device vector operations in prototyping AI apps, using HNSW for fast ANN search with built-in embeddings and metadata filtering. Enables quick local RAG on edge devices; simpler and lower-latency than cloud Qdrant for developer workflows.
- [Couchbase Lite Vector](https://vectordb.ever.works/items/couchbase-lite-vector) — Embedded NoSQL database enabling low-latency on-device vector search for offline GenAI on mobile/IoT/browsers via ANN indexing. ACID-compliant with sync replication for edge RAG; more mobile-focused and offline-capable than cloud Qdrant.
- [embedded-vector-db](https://vectordb.ever.works/items/embedded-vector-db) — Lightweight Node.js library for low-latency on-device vector similarity search using HNSW and BM25 hybrid, with CRUD, metadata filtering, and persistence for edge RAG pipelines. Enables real-time semantic search without servers; more lightweight than cloud Qdrant.
- [nano-vectordb-rs](https://vectordb.ever.works/items/nano-vectordb-rs) — Minimal Rust library for fast on-device cosine similarity search with Rayon parallelism and embedded persistence, ideal for low-latency prototyping on edge hardware. Supports quick inserts/queries for real-time AI; lighter than full DBs like Qdrant edge.
- [ObjectBox Vector](https://vectordb.ever.works/items/objectbox-vector) — Resource-efficient on-device vector database with sync for mobile/IoT/embedded, enabling low-latency offline AI vector ops without cloud. Supports edge-first apps; more efficient than server-based Qdrant.
- [rvLite](https://vectordb.ever.works/items/rvlite) — Compact 2MB standalone database for low-latency vector search on IoT/mobile/embedded, no server needed for on-device real-time AI ops.
- [tinyvector](https://vectordb.ever.works/items/tinyvector-m1guelpf) — Pure Rust embedding database as lightweight Axum server for low-latency on-device vector search scaling to 100M+ vectors in memory. High accuracy/speed for edge RAG; simpler than Qdrant edge.
- [Victor](https://vectordb.ever.works/items/victor) — Web-optimized Rust vector DB for low-latency on-device storage/search via WASM, with efficient formats and PCA compression for browsers/edge. Supports JS/Rust APIs; compact vs cloud Qdrant.
- [VortexDB](https://vectordb.ever.works/items/vortexdb) — Rust-built vector DB with pluggable HNSW/KD-Tree/Flat indexers for low-latency on-device similarity search, HTTP/gRPC/TUI clients, RocksDB persistence. Suited for edge AI; modular vs monolithic Qdrant.
- [Vector Search at the Edge](https://vectordb.ever.works/items/vector-search-at-the-edge) — Techniques and tools for deploying vector search in edge environments including embedded databases, WASM implementations, and edge-optimized models for privacy and low-latency applications.
- [Zvec](https://vectordb.ever.works/items/zvec) — Lightweight embedded vector database for RAG systems useful in edge environments, running directly on devices with local vector search and no network latency or cloud dependencies.
- [ObjectBox](https://vectordb.ever.works/items/objectbox) — A high-performance embedded database for edge devices and mobile, offering vector search capabilities for AI applications.

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_Canonical page: https://vectordb.ever.works/tags/embedded_