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Key features

Comprehensive Similarity Metrics

Milvus offers frequently used similarity metrics, including Euclidean distance, inner product, Hamming distance, Jaccard distance, etc, allowing you to explore vector similarity in the most effective and efficient way possible.

Leading-Edge Performance

Milvus is built on top of multiple optimized Approximate Nearest Neighbor Search (ANNS) indexing libraries, such as faiss, annoy, and hnswlib, ensuring that you always get the best performance across various scenarios.

Dynamic Data Management

No longer troubled by static data, you can operate data with insertion, deletion, search and update whenever needed.

Data is available for search almost immediately after being inserted and updated. Milvus does the heavy lifting in your best interests in terms of both result accuracy and data consistency.


Milvus harnesses the parallelism of modern processors and enables billion-scale similarity searches in milliseconds on a single off-the-shelf server.

Rich Data Type and Advanced Search (coming soon)

Milvus supports various data types for fields in a record. You can also use advanced search methods, such as filtering, sorting and aggregation for one or multiple fields.

Highly Scalable and Robust

You can deploy Milvus in a distributed environment. To increase the capacity and reliability of a Milvus cluster, you can simply add more nodes.

Cloud Native

We make it easy for you to run Milvus on public cloud, private cloud, or anywhere in between.

Ease of Use

Milvus provides easy-to-use SDKs in Python, Java, Go and C++, as well as RESTful APIs.

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