Vearch
Vearch is an open-source, cloud-native distributed vector database for efficient similarity search over embedding vectors in AI applications. It is designed to combine vector retrieval with scalar filtering, support large-scale data, and scale reliably through replication and elastic expansion.
At a Glance
- AI and machine-learning application developers
- Teams building vector search, semantic search, RAG, and recommendation systems
- Organizations needing self-hosted or Kubernetes-deployed vector infrastructure
- Large-scale visual-search and embedding-retrieval workloads
- +1 more
AI Tools by Vearch
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Vearch
Open Source Distributed Vector Database
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Latest News
Vearch v3.5.9 release: real-time searching, slow-search isolation, pagination, partition-name search, core-engine Python SDK, GPU IVFPQ search, Prometheus metrics, and fixes.
Vearch v3.5.8 release.
Vearch v3.5.7 release.
Vearch v3.5.6 release.
Products & Services
Apache-2.0 open-source, cloud-native distributed vector database/search system. It provides REST APIs for upsert, delete, search, and query; stores vectors and scalar fields; and uses Master, Router, and PartitionServer components with the Gamma search engine based on Faiss.
Python client/SDK for Vearch, including support for development and testing and a core-engine Python SDK in the v3.5.9 release.
Go client SDK for interacting with Vearch.
Java client SDK for interacting with Vearch.
Market Position
Vearch positions itself as an open-source, cloud-native distributed vector database emphasizing scalable deployment, hybrid vector-plus-scalar search, real-time indexing, hardware flexibility, and replication. A December 2024 comparison places it alongside purpose-built vector databases such as Qdrant and distinguishes its scalability, real-time indexing, and CPU/GPU/index flexibility from alternatives including Qdrant; the broader vector-database set also includes Milvus, Chroma, Pinecone, and Weaviate.
Founding Story
Vearch traces its origin to a real-time visual-search system for the JD E-commerce Platform. The project’s cited 2019 paper describes the system’s design and implementation, while the current project presents Vearch as a general-purpose distributed vector-search/database system for AI applications.
Business Model
Revenue Model
Vearch is distributed as open-source software under the Apache License 2.0. Public project materials describe self-hosted deployment through Docker Compose, Docker/source compilation, or Kubernetes/Helm; they do not describe a paid hosted service or subscription model.
Target Markets
- AI and machine-learning application developers
- Teams building vector search, semantic search, RAG, and recommendation systems
- Organizations needing self-hosted or Kubernetes-deployed vector infrastructure
- Large-scale visual-search and embedding-retrieval workloads
- Developers using Python, Go, Java, or Rust clients
- Visual search and image retrieval
- Semantic search and document retrieval
- Retrieval-augmented generation and AI knowledge bases
- Recommendation systems
- Embedding-based similarity search
- AI application memory backends
- JD E-commerce Platform