ObjectBox

Vector search extension for SQLite that runs anywhere

sqlite-vec is a lightweight vector search extension for SQLite written in pure C with zero dependencies. It brings nearest-neighbor search capabilities directly into SQLite databases, enabling AI applications to store and query embeddings without running a separate vector database. The extension works everywhere SQLite runs including Linux, macOS, Windows, WebAssembly in browsers, and even Raspberry Pi devices. Sponsored by Mozilla Builders, Fly.io, and Turso.

One vector-search extension across PostgreSQL, DuckDB and SQLite

MIT-licensed vector-search extension for PostgreSQL, DuckDB and SQLite that shares one graph-index core with PQ/RaBitQ quantization, persistent indexes and metadata filtering; SQLite packages cover Linux, macOS, iOS, Android and WASM, so it runs inside existing databases instead of as a separate vector service.

Embedded vector database for multimodal AI with petabyte scale

LanceDB is an open-source embedded vector database built on the Lance columnar format for multimodal AI. It delivers near in-memory performance from disk with zero-copy architecture, supporting vector search, full-text search, and SQL. Native SDKs for Python, TypeScript, and Rust integrate with LangChain, LlamaIndex, and DuckDB. Backed by a $30M Series A, used by Harvey AI and Runway, with 18,000+ GitHub stars.

Fast embeddable vector search engine

USearch is a high-performance vector search engine implementing HNSW algorithms for approximate nearest neighbor queries across C++, Python, JavaScript, Rust, Java, Go, and more. It supports user-defined distance metrics, memory-mapped persistence for datasets larger than RAM, and filtered search with predicates. Used by YugabyteDB and ScyllaDB as their production vector indexing backend.

Open-source embedding database — the AI-native way to store and query embeddings.

Chroma is an open-source embedding database designed for simplicity and developer experience. Runs in-memory, as a Python library, or as a client-server deployment. Popular for prototyping RAG applications, local development, and lightweight vector search. Integrates natively with LangChain, LlamaIndex, and OpenAI.

Ultra-fast SIMD-accelerated embedded vector database with TurboQuant compression

High-performance Rust and Python vector search index utilizing TurboQuant 2-to-4-bit data-oblivious quantization for ultra-compact memory footprint and zero-training ANN retrieval.

Microsoft's distributed approximate nearest neighbor search library and billion-scale vector engine

Microsoft's distributed approximate nearest neighbor search library and online vector serving engine, utilizing space-partition trees and relative neighborhood graphs for billion-scale semantic retrieval.

High-performance vector database written in Rust for similarity search at scale.

Qdrant is a high-performance vector similarity search engine and database written in Rust. Designed for production-grade AI applications with advanced filtering, payload indexing, and distributed deployment. Supports billion-scale vector collections with sub-second query times. Popular choice for RAG, recommendation systems, and anomaly detection.

Fully managed vector database built for AI applications at production scale.

Pinecone is a leading managed vector database designed for high-performance similarity search at scale. Purpose-built for AI applications including RAG, recommendation systems, and semantic search. Offers managed serverless infrastructure with automatic scaling, filtering, hybrid retrieval, and namespacing. No infrastructure management required.

High-performance OLTP graph-vector database in Rust built on object storage for AI memory

HelixDB is an open-source, unified graph-vector database engineered in Rust that merges relational, graph, and vector workloads into a single OLTP engine, using LMDB local caching and S3 object storage for scalable agent memory.

Fully managed RAG-as-a-Service platform for enterprise AI applications

Ragie is a managed retrieval-augmented generation platform that handles document ingestion, indexing, and retrieval so developers can build grounded AI applications without managing vector databases or chunking pipelines. It connects to Google Drive, Notion, Slack, Confluence, and other enterprise data sources with simple APIs for hybrid search and entity extraction.