Milvus (vector database)
 | A major contributor to this article appears to have a close connection with its subject. (November 2024) |
| Milvus | |
|---|---|
 | |
| Developer | Zilliz |
| Release | October 19, 2019 |
| Stable release | v3.0.0
/ July 29, 2026.: |
| Written in | Go, C++ |
| Operating system | Linux, macOS |
| Platform | x86, ARM |
| Type | Vector database |
| License | Apache License 2.0 |
| Website | milvus |
| Repository | github |
Milvus is a distributed vector database developed by Zilliz. It is available as both open-source software and a cloud service called Zilliz Cloud.
Milvus is an open-source project under the LF AI & Data Foundation and is distributed under the Apache License 2.0.
History
Milvus has been developed by Zilliz since 2017.
Milvus joined Linux Foundation as an incubation project in January 2020 and became a graduate in June 2021. The details about its architecture and possible applications were presented at ACM SIGMOD Conference in 2021.
Milvus 2.0, a major redesign of the whole product with a new architecture, was released in January 2022.
Milvus 3.0, which introduces elements of data lake / data warehouse based data processing, was released in July 2026.
Features
Similarity search
Various similarity search-related features are available in Milvus:
- In-memory, on-disk and GPU indices,
- Single query, batch query and range query search,
- Support of sparse vectors, binary vectors, JSON and arrays,
- FP32, FP16 and BF16 data types,
- Euclidean distance, inner product distance and cosine distance support for floating-point data,
- Hamming distance and jaccard distance for binary data,
- Support of graph indices (including HNSW), Inverted-lists based indices and a brute-force search.
- Support of vector quantization for lossy input data compression, including product quantization (PQ) and scalar quantization (SQ), that trades stored data size for accuracy,
- Re-ranking.
Milvus' similarity search engine relies on modified forks of third-party open-source similarity search libraries, such as Faiss, DiskANN (including the AiSAQ technology from KIOXIA) and hnswlib.
Milvus includes optimizations for I/O data layout, specific to graph search indices.
Database
As a database, Milvus provides the following features:
- Support for column-oriented databases
- Four supported data consistency levels, including strong consistency and eventual consistency
- Data sharding
- Streaming data ingestion, which allows processing and ingestion of data in real-time as it arrives
- A dynamic schema, which allows insertion of data without a predefined schema
- Independent storage and compute layers
- Support for multi-tenancy scenarios (database-oriented, collection-oriented, partition-oriented)
- Memory-mapped data storage
- Role-based access control
- Multi-vector and hybrid search
Milvus 3.0 introduces the following features:
- Snapshots
- Nullable vector fields
- Evaluation rollbacks
Data lake
Milvus 3.0 introduces the following large scale operations, applicable for vectors:
Deployment options
Milvus can be deployed as an embedded database, standalone server, or distributed cluster. Zilliz Cloud offers a fully managed version.
GPU support
Milvus provides GPU accelerated index building and search using Nvidia CUDA technology via the Nvidia cuVS library, including the GPU-based graph indexing algorithm CAGRA.
Integration
Milvus provides official SDK clients for Java, NodeJS, Python and Go. An additional C# SDK client was contributed by Microsoft. The database can integrate with DataDog, Prometheus and Grafana for monitoring and alerts, as well as generative AI frameworks Haystack, LangChain, IBM Watsonx, and those provided by OpenAI.
Several storage providers have built integrations with Milvus to support AI workloads and large-scale vector search. These integrations aim to optimize performance, simplify inferencing workflows, and enhance data management capabilities:
Milvus is included in the SUSE AI platform product. Red Hat OpenShift AI self-managed product supports deploying Milvus.
See also
References
- 1 2 "Release notes for Milvus v3.0.0". GitHub.
- 1 2 "LF AI & Data Foundation Announces Graduation of Milvus Project". June 23, 2021.
- ↑ Liao, Ingrid Lunden and Rita (2022-08-24). "Zilliz raises $60M, relocates to SF". TechCrunch. Retrieved 2024-10-21.
- ↑ "Milvus: A Purpose-Built Vector Data Management System". SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data. June 18, 2021. pp. 2614–2627. doi:10.1145/3448016.3457550. ISBN 978-1-4503-8343-1.
- ↑ Guo, Rentong; Luan, Xiaofan; Xiang, Long; Yan, Xiao; Yi, Xiaomeng; Luo, Jigao; Cheng, Qianya; Xu, Weizhi; Luo, Jiarui; Liu, Frank; Cao, Zhenshan; Qiao, Yanliang; Wang, Ting; Tang, Bo; Xie, Charles (2022). "Manu: A Cloud Native Vector Database Management System". arXiv:2206.13843 [cs.DB].
- 1 2 3 "Milvus overview". Retrieved September 23, 2024.
- ↑ "Faiss". GitHub. Retrieved September 23, 2024.
- ↑ Douze, Matthijs; Guzhva, Alexandr; Deng, Chengqi; Johnson, Jeff; Szilvasy, Gergely; Mazaré, Pierre-Emmanuel; Lomeli, Maria; Hosseini, Lucas; Jégou, Hervé (2024). "The Faiss library". arXiv:2401.08281 [cs.LG].
- ↑ "DiskANN library". GitHub. Retrieved September 23, 2024.
- ↑ Subramanya, Suhas Jayaram; Kadekodi, Rohan; Krishaswamy, Ravishankar; Simhadri, Harsha Vardhan (8 December 2019). "DiskANN: fast accurate billion-point nearest neighbor search on a single node". Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc.: 13766–13776.
- ↑ "KIOXIA AiSAQ Technology Integrated into Milvus Vector Database". Retrieved May 14, 2026.
- ↑ "Hnswlib - fast approximate nearest neighbor search". GitHub. Retrieved September 23, 2024.
- ↑ Wang, Mengzhao; Xu, Weizhi; Yi, Xiaomeng; Wu, Songlin; Peng, Zhangyang; Ke, Xiangyu; Gao, Yunjun; Xu, Xiaoliang; Guo, Rentong; Xie, Charles (2024). "Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment". Proceedings of the ACM on Management of Data. 2: 1–27. arXiv:2401.02116. doi:10.1145/3639269.
- ↑ "Consistency levels in Milvus". Retrieved September 29, 2024.
- ↑ "Multi-tenancy strategies". Retrieved September 29, 2024.
- ↑ "Hybrid Search". Retrieved September 23, 2024.
- ↑ "Vector Lakebase: End the AI Data Silo". 2026-05-14. Retrieved 2026-05-14.
- ↑ "Zilliz cloud". Retrieved October 10, 2024.
- ↑ "What's New In Milvus 2.3 Beta - 10X faster with GPUs". Retrieved September 29, 2024.
- ↑ "Milvus 2.3 Launches with Support for Nvidia GPUs". 23 March 2023. Retrieved September 29, 2024.
- ↑ "NVIDIA cuVS library". GitHub.
- ↑ Ootomo, Hiroyuki; Naruse, Akira; Nolet, Corey; Wang, Ray; Feher, Tamas; Wang, Yong (August 2023). "CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs". arXiv:2308.15136 [cs.DS].
- ↑ "Install Milvus Go SDK". Retrieved September 29, 2024.
- ↑ "Get Started with Milvus Vector DB in .NET". March 6, 2024. Retrieved September 29, 2024.
- ↑ "Integration roundup: Monitoring your modern database platforms". 26 February 2025. Retrieved February 26, 2025.
- ↑ "Integration HayStack + Milvus". Retrieved September 23, 2024.
- ↑ "Milvus connector for LangChain". Retrieved September 23, 2024.
- ↑ "IBM watsonx.data's integrated vector database: unify, prepare, and deliver your data for AI". IBM. April 9, 2024. Retrieved September 29, 2024.
- ↑ "Getting started with Milvus and OpenAI". Mar 28, 2023. Retrieved September 23, 2024.
- ↑ "OpenAI and Milvus simple app". GitHub. Retrieved September 23, 2024.
- ↑ "Pure Storage Introduces New GenAI Infrastructure with NVIDIA and Run:ai". Pure Storage. 2024-06-25.
- ↑ "Cloudian AI Inferencing Platform". Cloudian. 2024-05-07.
- ↑ "Weka Debuts New Solution Blueprint to Simplify AI Inferencing at Scale". Weka. 2024-04-23.
- ↑ "Revolutionizing Biomedical GenAI with Hyperscale RAG: DDN Infinia, Milvus, and the Full PubMed Dataset". DDN. 2024-06-03.
- ↑ "Hitachi Vantara unveils AI agent-building iQ Studio". 2025-11-05. Retrieved 2026-05-14.
- ↑ "Vector Database Solution with NetApp". 2025-09-15. Retrieved 2026-05-14.
- ↑ "Connecting NAI Labs to an External Milvus Vector Database". Retrieved 2026-05-14.
- ↑ "The Data Foundation of the AI Factory: Enabling Agentic AI with Nutanix Unified Storage". 2026-03-16. Retrieved 2026-05-14.
- ↑ "Announcing SUSE AI: An Enterprise ready AI platform". 2024-11-17. Retrieved 2026-05-14.
- ↑ "Accelerating Innovation with HPE and SUSE: Secure, Scalable, and AI-Ready Infrastructure". 2025-08-04. Retrieved 2026-05-14.
- ↑ "Deploying a RAG stack in a data science project". Retrieved 2026-05-14.