Milvus is a cloud-native, open-source vector database built to manage embedding vectors generated by machine learning models and neural networks. It extends the capabilities of best-in-class approximate nearest neighbor (ANN) search libraries (e.g. Faiss, NMSLIB, Annoy) and features on-demand scalability, unified Lambda structure, and high availability. The goal of Milvus is to simplify unstructured data management and provide a consistent experience across different deployment environments.

Milvus is widely used in scenarios such as computer vision, natural language processing, computational chemistry, personalized recommender systems, and more. It has been adopted by over 1,000 organizations worldwide including iQiyi, Kingsoft, Tokopedia, and Trend Micro. More than 2,300 developers have joined the Milvus open-source community on GitHub, Slack, mailing lists, and Wechat.

Milvus was released under the open-source Apache License 2.0 in October 2019, and its source code was made available on GitHub. In June 2021, Milvus graduated from the LF AI & Data Foundation's incubator program.


Learn to build.


Learn more.


blog card cover
Optimize Vector Databases, Enhance RAG-Driven Generative AI
In this article, you’ll learn more about vector databases and their benchmarking frameworks, datasets to tackle different aspects, and the tools used for performance analysis — everything you need to start optimizing vector databases.