Milvus is an open-source, high-performance vector database designed for AI applications. Its core capabilities are efficient storage, management, and retrieval of high-dimensional vector data, suitable for building recommendation systems, semantic search, image retrieval, and other use cases that require similarity matching.
Milvus provides four deployment options: Milvus Lite (a lightweight Python library for local development), Milvus Standalone (a single-machine server suitable for small-scale production), Milvus Distributed (a Kubernetes-based distributed cluster for large-scale production), and the fully managed Zilliz Cloud service.
Milvus offers high scalability; its distributed architecture can handle vector data from millions up to billions, depending on the deployment mode and hardware resources.
For prototyping, we recommend Milvus Lite. Simply install the Python SDK via `pip install pymilvus` to quickly perform vector data insertion, searching, and other operations in a local Jupyter Notebook or scripts.
Milvus primarily supports vector similarity search. It also supports hybrid search, combining vector similarity with scalar metadata (e.g., tags, timestamps) for filtering to obtain more precise results.
MongoDB is a modern document-oriented database platform. Its flagship cloud offering, MongoDB Atlas, provides a fully managed database service. Atlas includes native vector search capabilities to help developers build generative-AI-powered applications and to support enterprises in modernizing data management and system architecture.
Zilliz is a company focused on vector database technology, offering a fully managed cloud service built on the open-source Milvus project: Zilliz Cloud. This service helps enterprises efficiently process and analyze unstructured data, enabling AI applications such as retrieval-augmented generation (RAG) and semantic search through vector similarity search, while reducing the complexity of AI app development and operations.
