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We chose Milvus because it is very user-friendly. Without Milvus, our semantic search wouldn’t have been 10x smarter!

by Rahul Yadav, Principal Engineer at Tokopedia


  • supporting


    Milvus improves upon high-performance storage and index libraries such as Faiss and hnswlib, guaranteeing time and resource efficient query speeds. Using a variety of acceleration methods (e.g., CPU, SIMD, GPU, and FPGA), Milvus can retrieve vector data on trillion-scale datasets in milliseconds.

  • search

    On-demand Performance

    Milvus is cloud native and can be horizontally scaled with ease. The platform is capable of handling storage and computation at any scale, and has a microservice design that supports on-demand and automatic scaling.

  • deployment

    Built for Unstructured Data

    Milvus helps users focus on the semantic meaning of unstructured data rather than complexities such as sharding, data persistence, and load balancing. Milvus supports high-performance, hybrid search of vector and scalar data, opening up new possibilities for unstructured data processing.

  • storage


    Milvus is a graduate of the LF AI & Data Foundation's incubator program and has been adopted by 1,000+ organizations worldwide. The platform's vibrant open-source community welcomes contributions from everyone.

  • autoscaling


    Milvus supports multiple data types and provides comprehensive multi-language SDKs. Through the Python-ORM API, Milvus offers an integrated user experience across laptops, local clusters, and the cloud. A wealth of deployment and visualization tools are available to help users get Milvus up and running faster.

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  • 爱奇艺
  • 快手
  • 小米
  • line
  • moj
  • museum
  • Smart News
  • Compass
  • axa
  • Daily Hunt
  • Message Bird

Join the community

Milvus is open-sourced on GitHub.

Contributions and feedbacks are welcome!