Milvus is an open source similarity search engine for massive-scale vector datasets. Built with heterogeneous computing architecture for the best cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources.
Milvus is designed with heterogeneous computing architecture for the best performance and cost efficiency.
Milvus supports a variety of indexing types that employs quantization-based, tree-based, and graph-based indexing techniques.
Intelligent resource management
Milvus automatically adapts search computation and index building processes based on your datasets and available resources.
Milvus supports online / offline expansion to scale both storage and computation resources with simple commands.
Milvus supports WAL (Write-Ahead Logging), which ensures the atomicity and durability of data operations. Milvus is integrated with the Kubernetes framework so that all single point of failures could be avoided for distributed scenarios.
Milvus is compatible with almost all deep learning models. Milvus supports multiple programming languages such as Python, Java, C++, and Go. Milvus also supports RESTful API.
Ease of use
Milvus can be easily installed in a few steps and enables you to exclusively focus on feature vectors.
You can track system performance on Prometheus-based GUI monitor dashboards.