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Vector DBFreeOpen Source

VEARCH

Cloud-native vector database for AI-native similarity search

Apache-2.0
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ABOUT

Running vector search at scale requires a database that can store, index, and query billions of embeddings with low latency, while also supporting real-time updates, hybrid search (vector + scalar filters), and horizontal scaling. Most vector search libraries are single-node tools, and cloud-managed solutions can be expensive at scale. Vearch is a distributed, cloud-native vector database built for Kubernetes. It partitions and replicates data across shards automatically, supports IVF_PQ and HNSW indexing, and provides RESTful and gRPC APIs. It handles both dense embeddings and scalar attribute filtering, making it suitable for production RAG pipelines and recommendation systems that need to scale without a per-query high cost.

INTEGRATION GUIDE

1. Build a production RAG pipeline with hybrid search that filters by metadata before vector similarity 2. Deploy a distributed similarity search service on Kubernetes that scales horizontally as data grows 3. Power a real-time content recommendation engine with vectors updated as user preferences change 4. Store and query billions of image embeddings for a visual product search system 5. Run semantic document retrieval with metadata-aware filtering at enterprise scale

TAGS

vector-databasesimilarity-searchcloud-nativekubernetesdistributedhybrid-searchragembeddings
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