# Distributed

> Browse 40 items tagged with Distributed.

## Items

- [YugabyteDB with pgvector](https://vectordb.ever.works/it/items/yugabytedb-with-pgvector) — PostgreSQL-compatible distributed database with pgvector support and USearch integration, proven to handle billions of vectors with 96.56% recall and sub-second query latency.
- [Apache Cassandra Vector Search](https://vectordb.ever.works/it/items/apache-cassandra-vector-search) — Distributed NoSQL database with vector search capabilities via Storage-Attached Indexes (SAI) in Cassandra 5.0+. Uses Lucene HNSW for approximate nearest neighbor search. This is an OSS database under Apache 2.0 license.
- [YugabyteDB](https://vectordb.ever.works/it/items/yugabytedb) — PostgreSQL-compatible distributed SQL DB with HNSW vector search + keyword/full-text + relational/graph joins/aggs for hybrid queries. Scales ACID vector workloads for multimodal RAG. Unifies vectors with SQL unlike pure vector DBs like Qdrant.
- [Milvus Distributed](https://vectordb.ever.works/it/items/milvus-distributed) — Milvus Distributed is the cluster mode of the scalable open-source vector database for AI embeddings search, supporting HNSW, IVF, and NGT indexes in high-availability distributed setups. It provides GPU support, billion-scale capacity, real-time upsert/query capabilities, and multi-modal vector handling. Suited for RAG, recommendations, and image/video search at enterprise scale. Self-hosted unlike Pinecone's managed offering, and more ANN-centric than Weaviate.
- [TiDB Vector Search](https://vectordb.ever.works/it/items/tidb-vector-search) — Open-source distributed SQL database with integrated vector search for storing embeddings alongside relational data, offering strong SQL-based filtering, hybrid search, and high scalability for production RAG and AI applications.
- [Vearch](https://vectordb.ever.works/it/items/vearch) — Distributed vector engine for embedding similarity search.
- [pgEdge](https://vectordb.ever.works/it/items/pgedge) — Distributed PostgreSQL extension for edge deployments enabling low-latency vector processing closer to users across multi-region nodes with consistency. Supports on-device vector ops in edge environments vs centralized cloud DBs like Qdrant.
- [Swirl](https://vectordb.ever.works/it/items/swirl-metasearch) — Open-source federated search platform for privacy-preserving vector similarity search across distributed enterprise data sources without data migration or central storage, unlike centralized vector DBs like Pinecone that require uploading all data to a single service. Enables multi-node federation querying 100+ heterogeneous sources simultaneously, using LLM embeddings for re-ranking unified results while keeping data local for enhanced privacy and compliance. Ideal for federated learning scenarios and data-sovereign AI applications.
- [Milvus](https://vectordb.ever.works/it/items/milvus) — Milvus is an open-source vector database designed for scalable similarity search on massive datasets, supporting billions of vectors with high performance. Key features include distributed architecture, support for multiple indexes like HNSW and IVF, hybrid search, and integrations with popular ML frameworks. Ideal for RAG pipelines, recommendation systems, and AI agents; self-hosted alternative to managed services like Pinecone with better cost control for large-scale deployments.
- [Crate](https://vectordb.ever.works/it/items/crate) — Crate is an open-source, self-hosted distributed SQL database with native vector data types and similarity search for AI applications. Key features include horizontal scaling, PostgreSQL compatibility, Lucene-based indexing, and Apache 2.0 license. Ideal for RAG and real-time analytics, providing free self-hosting vs managed vector DBs like Pinecone for cost control.
- [Havenask](https://vectordb.ever.works/it/items/havenask) — Havenask is an open-source, self-hosted distributed search engine from Alibaba with vector search for large-scale AI applications. Key features include high QPS/TPS, millisecond latency, SQL queries, and free use. Suited for production RAG and search, self-hosted for cost efficiency vs managed like Pinecone.
- [BagelDB](https://vectordb.ever.works/it/items/bageldb) — Collaborative vector database platform described as 'GitHub for AI data'. Features distributed storage, HNSW indexing, and supports private, collaborative, and public vector datasets. This is a commercial platform with open collaboration features.
- [Baidu VectorDB](https://vectordb.ever.works/it/items/baidu-vectordb) — Enterprise-level distributed vector database from Baidu Intelligent Cloud, built on the proprietary Mochow kernel, supporting up to 10 billion vectors with millions of QPS and millisecond latency.
- [Tencent Cloud VectorDB](https://vectordb.ever.works/it/items/tencent-cloud-vectordb) — Fully managed, enterprise-level distributed vector database from Tencent Cloud, supporting billion-scale vector search with millisecond latency and millions of QPS using the self-developed Olama engine.
- [YDB](https://vectordb.ever.works/it/items/ydb) — YDB is an open-source distributed SQL database with vector search capabilities under Apache License 2.0. It supports high-performance vector similarity search for AI and machine learning applications.
- [BatANN: Passing the Baton: High Throughput Distributed Disk-Based Vector Search](https://vectordb.ever.works/it/items/batann-passing-the-baton-high-throughput-distributed-disk-based-vector-search) — BatANN system by Dang et al. for high-throughput distributed disk-based vector search. Supports scalable ANN in distributed environments.
- [CoTra: Towards Efficient and Scalable Distributed Vector Search with RDMA](https://vectordb.ever.works/it/items/cotra-towards-efficient-and-scalable-distributed-vector-search-with-rdma) — CoTra system by Zhi et al. for efficient distributed vector search using RDMA. Published in SIGMOD 2026 proceedings.
- [Manu — A Cloud Native Vector Database Management System](https://vectordb.ever.works/it/items/manu-a-cloud-native-vector-database-management-system) — VLDB 2022 paper introducing Manu, a cloud-native vector database management system designed for scalable similarity search in cloud environments with separated storage and compute architecture.
- [AtlasDB](https://vectordb.ever.works/it/items/atlasdb) — Distributed, transactional key-value store developed by Palantir Technologies, designed for general-purpose data storage with high performance and horizontal scalability across multiple nodes.
- [BLISS — A Billion Scale Index using Iterative Re-partitioning](https://vectordb.ever.works/it/items/bliss-a-billion-scale-index-using-iterative-re-partitioning) — SIGKDD 2022 paper introducing BLISS, a billion-scale indexing method using iterative re-partitioning for large-scale approximate nearest neighbor search.
- [DIMS — Distributed Index for Similarity Search in Metric Spaces](https://vectordb.ever.works/it/items/dims-distributed-index-for-similarity-search-in-metric-spaces) — TKDE 2024 paper presenting DIMS, a distributed indexing method for efficient similarity search across metric spaces. The approach enables parallel processing of vector similarity queries at scale.
- [ELPIS — Graph-Based Similarity Search for Scalable Data Science](https://vectordb.ever.works/it/items/elpis-graph-based-similarity-search-for-scalable-data-science) — VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.
- [Passing the Baton: High Throughput Distributed Disk-Based Vector Search with BatANN](https://vectordb.ever.works/it/items/passing-the-baton-high-throughput-distributed-disk-based-vector-search-with-batann) — A distributed, disk-based vector search system designed for high-throughput approximate nearest neighbor queries at scale. BatANN provides an architecture and methods applicable to large-scale vector databases that need efficient storage beyond memory, enabling cost-effective approximate nearest neighbor search for high-dimensional embeddings.
- [SmallPond](https://vectordb.ever.works/it/items/smallpond) — A distributed data processing framework for vector data operations, providing lightweight parallel processing capabilities for embedding pipelines and data preparation workflows.
- [Co-partitioned Vector Index](https://vectordb.ever.works/it/items/co-partitioned-vector-index) — Indexing strategy where vector indexes are stored in the same partitions as corresponding table rows, ensuring data locality and operational advantages in distributed databases.
- [MVCC Vector Indexing](https://vectordb.ever.works/it/items/mvcc-vector-indexing) — Multi-Version Concurrency Control for vector indexes enabling transactional guarantees and consistent reads in distributed vector databases like YugabyteDB.
- [Vector LSM](https://vectordb.ever.works/it/items/vector-lsm) — YugabyteDB's pluggable vector indexing architecture that separates vector search logic from the database engine, enabling integration with multiple ANN backends like USearch.
- [d-HNSW](https://vectordb.ever.works/it/items/d-hnsw-disaggregated-memory) — An efficient vector search system designed for disaggregated memory architectures. d-HNSW optimizes HNSW for environments where compute and memory are separated, typical in modern cloud and distributed systems.
- [Scalable Distributed Vector Search](https://vectordb.ever.works/it/items/scalable-distributed-vector-search) — A research paper on accuracy-preserving index construction for distributed vector search systems. Published in 2025, it addresses the challenge of maintaining search quality while distributing vector indexes across multiple nodes.
- [Consistency Levels](https://vectordb.ever.works/it/items/consistency-levels) — Configuration options in distributed vector databases that trade off between data consistency, availability, and performance. Critical for understanding read/write behavior in production systems with replication.
- [NebulaGraph](https://vectordb.ever.works/it/items/nebulagraph) — Open-source distributed graph database designed for super large-scale graphs with billions of vertices and trillions of edges. Outperforms Neo4j on larger datasets while providing graph database capabilities for AI applications.
- [Apache Kvrocks](https://vectordb.ever.works/it/items/apache-kvrocks) — Distributed key-value NoSQL database with experimental vector similarity search. Redis-compatible with RocksDB storage engine, adding HNSW-based vector indexing for large-scale vector data management.
- [Vector Database Sharding](https://vectordb.ever.works/it/items/vector-database-sharding) — Distributing vector data across multiple nodes for horizontal scaling. Enables handling billions of vectors by partitioning data and parallelizing queries.
- [ScyllaDB Vector Search](https://vectordb.ever.works/it/items/scylladb-vector-search) — High-performance NoSQL database with vector search capabilities built on USearch library and shard-per-core architecture, storing vector embeddings alongside structured data in unified tables.
- [BatANN](https://vectordb.ever.works/it/items/batann) — Distributed disk-based approximate nearest neighbor system achieving near-linear throughput scaling. Delivers 6.21-6.49x throughput improvement over scatter-gather baseline with sub-6ms latency on 10 servers.
- [CockroachDB](https://vectordb.ever.works/it/items/cockroachdb) — CockroachDB is a cloud-native, distributed SQL database that now supports vector data, combining traditional SQL queries with efficient vector search capabilities, ensuring data resilience, availability, scalability, and strong consistency.
- [Apache Cassandra](https://vectordb.ever.works/it/items/apache-cassandra) — Apache Cassandra is a distributed NoSQL database that is adding native support for high-dimensional vector storage and approximate nearest neighbor search, making it a scalable choice for AI and vector search workloads.
- [citrus](https://vectordb.ever.works/it/items/citrus) — A distributed vector database designed for scalable and efficient vector similarity search. It is purpose-built for handling large-scale vector data and search workloads.
- [SPTAG](https://vectordb.ever.works/it/items/sptag) — SPTAG is a distributed approximate nearest neighbor (ANN) library for building and searching large-scale vector indexes, supporting efficient and scalable vector search scenarios.
- [Transwarp Hippo](https://vectordb.ever.works/it/items/transwarp-hippo) — Transwarp Hippo is an enterprise-grade, cloud-native distributed vector database designed for scalable vector operations, including similarity search and clustering, targeting massive datasets and real-time recommendation systems.

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