Milvus is an open source similarity search engine for massive-scale feature vectors. Built with heterogeneous computing architecture for the best cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources.
Milvus provides stable Python, Java and C++ APIs.
Milvus provides stable Python, Java and C++ APIs. For more detailed introduction of Milvus and its architecture, see [Milvus overview](https://www.milvus.io/docs/en/aboutmilvus/overview/).
Keep up-to-date with newest releases and latest updates by reading Milvus [release notes](https://milvus.io/docs/en/release/v0.5.0/).
- Heterogeneous computing
Milvus is built with heterogeneous computing architecture for the best performance and cost efficiency.
- Multiple indexes
Milvus supports a variety of indexing types that employs quantization, tree-based, and graph indexing techniques.
- Intelligent resource management
Milvus automatically adapts search computation and index building processes based on your datasets and available resources.
- Horizontal scalability
Milvus supports online / offline expansion to scale both storage and computation resources with simple commands.
- High availability
Milvus is integrated with Kubernetes framework so that all single point of failures could be avoided.
- High compatibility
Milvus is compatible with almost all deep learning models and major programming languages such as Python, Java and C++, etc.
- Ease of use
Milvus can be easily installed in a few steps and enables you to exclusively focus on feature vectors.
- Visualized monitor
You can track system performance on Prometheus-based GUI monitor dashboards.