USearch

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.

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.

GPU-accelerated open-source vector database

Milvus is an open-source vector database with 33K+ GitHub stars for billion-scale similarity search. Features GPU-accelerated indexing, hybrid search combining vector and scalar filtering, multi-tenancy, partitioning, and horizontal scaling. Supports HNSW, IVF, DiskANN, and GPU index types. SDKs for Python, Java, Go, and Node.js. Zilliz Cloud offers a managed version. A production-grade foundation for RAG pipelines and recommendation systems at enterprise scale.

Open-source vector database for AI-native applications and semantic search.

Weaviate is an open-source vector database purpose-built for AI applications. Supports vector, keyword, and hybrid search with built-in vectorization modules for OpenAI, Cohere, Hugging Face, and more. Used for RAG pipelines, semantic search, recommendation engines, and multimodal search. Written in Go for high performance.

Cloud-native distributed vector search engine built for Kubernetes with automatic indexing and horizontal scaling.

Vald is a highly scalable distributed approximate nearest neighbor (ANN) vector search engine designed for cloud-native, Kubernetes-based architectures. Maintained by LY Corporation and listed in the CNCF Landscape, it uses the NGT algorithm (developed at Yahoo Japan), supports automatic incremental index backup, and handles billion-scale datasets across loosely coupled microservice components that scale horizontally via Helm.

Library for efficient similarity search and clustering of dense vectors at billion-scale.

FAISS is Meta AI Research's open-source library for efficient similarity search and clustering of dense vectors. It implements approximate nearest-neighbor algorithms designed to scale to billions of vectors, with optimized indexes that fit in RAM and GPU acceleration for the largest workloads. Engineering teams use FAISS as the retrieval primitive underneath custom RAG pipelines, recommendation systems, and large-scale embedding search infrastructure.

Header-only C++ implementation of HNSW for fast approximate nearest-neighbor search.

hnswlib is a header-only C++ library implementing the Hierarchical Navigable Small World (HNSW) graph algorithm for approximate nearest-neighbor search, with Python bindings and a tiny dependency footprint. Originally developed by the nmslib team, it has become the default HNSW implementation embedded inside many vector databases and search products. Engineers use it directly when they want HNSW retrieval without pulling in a heavyweight vector DB.

Embedding-first search and discovery engine for AI-powered product experiences.

Marqo is an open-source tensor search engine that combines embedding generation and vector search in a single API, removing the need to manage separate embedding pipelines and vector databases. Built for product discovery and multi-modal search, it lets teams index text, images, and structured data together, returning ranked results based on semantic similarity rather than keyword overlap.

High-recall Postgres vector search at billion scale

VectorChord is a Postgres extension from TensorChord that brings high-recall vector search to PostgreSQL. As the spiritual successor to pgvecto.rs, it combines IVF indexes with RaBitQ quantization to deliver Pinecone-class performance at billion-vector scale while keeping all data inside a single Postgres database — no separate vector store, no two-system sync, no rewrites when the workload grows.

AI-native database for hybrid RAG retrieval

Infinity is an AI-native database from InfiniFlow that unifies dense vectors, sparse vectors, tensors, and full-text search in a single engine. Built for retrieval-augmented generation (RAG) at scale, it powers hybrid search workflows where lexical matching, semantic similarity, and reranking all happen against one storage layer instead of four loosely coupled services.