At a Glance:
Milvus is a high-performance, open-source vector database built in Go and C++ for scaling AI applications, featuring a distributed K8s-native architecture, hardware acceleration, and support for dense and sparse vectors for hybrid search.
Overview:
Milvus is a vector database purpose-built for managing and searching massive volumes of unstructured data, such as text, images, and multi-modal information, for AI applications. It is written in Go and C++ and implements a distributed, cloud-native architecture that separates compute from storage, enabling horizontal scaling to handle tens of thousands of queries on billions of vectors with real-time streaming updates. It supports deployment in a fully-distributed mode, a standalone mode for single machines, and a lightweight library version called Milvus Lite. The project is a graduated stage project of the LF AI & Data Foundation and is distributed under the Apache 2.0 license.
Key Decision Points:
Deployment flexibility: Offers a fully-distributed, K8s-native architecture for horizontal scaling, a Standalone mode for single-machine deployment, and Milvus Lite for local use via
pip install.Search capabilities: Natively supports dense vector semantic search, full-text search (BM25), and learned sparse embeddings (SPLADE, BGE-M3), which can be combined in a single hybrid search query.
Multi-tenancy model: Supports isolation through databases, collections, partitions, or partition keys, allowing a single cluster to manage workloads from hundreds to millions of tenants.
Hardware acceleration: Implements CPU/GPU acceleration and supports GPU indexing, such as NVIDIA's CAGRA, to optimize search performance.
Access control and security: Implements mandatory user authentication, TLS encryption, and Role-Based Access Control (RBAC) for fine-grained permissions.
Core Features:
Hybrid search: Allows combining dense vector semantic search with sparse vector full-text search (BM25/SPLADE) in a single operation with result reranking.
Distributed architecture: A fully-distributed system with separated compute and storage, enabling independent scaling of query and data nodes based on workload.
Multi-vector support: Stores and manages multiple vector types within a single collection, including dense embeddings and sparse vectors for different retrieval strategies.
Multi-tenancy: Provides flexible tenant isolation strategies at the database, collection, and partition level within a single cluster.
Hot/cold storage: Supports tiered data storage where frequently accessed data resides on memory or SSDs and less-accessed data on cost-effective storage.
Ecosystem integrations: Integrates with AI development tools like LangChain, LlamaIndex, OpenAI, and HuggingFace, and supports data pipelines through connectors for Spark, Kafka, Fivetran, and Airbyte.
Use Cases:
Developers building Retrieval-Augmented Generation (RAG) applications can use Milvus as the vector store integrated with frameworks like LangChain and LlamaIndex.
AI application builders can implement a hybrid search experience that combines semantic and full-text search for tasks like product discovery or document retrieval.
Researchers and developers working with large-scale, multi-modal data can perform image, text, and multi-vector similarity searches within a single database system.
Open-Source Alternative Value:
As an open-source vector database under the Apache 2.0 license and hosted by the LF AI & Data Foundation, Milvus provides an alternative to managed proprietary vector search services. Its value lies in its ability to be deployed across multiple environments, from a local Python library to a fully distributed K8s cluster, using a single codebase. The native support for both dense and sparse vector search within one database system offers a transparent, integrated approach to building hybrid search pipelines without relying on separate services for full-text and vector retrieval.
