milvus

Milvus is an open-source vector database built for AI applications that need fast and scalable similarity search. It stores and searches vector embeddings from text, images, audio, and other data. Milvus can support everything from local AI experiments to large production systems handling billions of vectors, making it useful for RAG, semantic search, recommendations, and AI agents.

Uses of Milvus AI

Milvus helps developers store and search AI-generated vector embeddings. Instead of searching only for exact words, applications can use vector similarity to find information with similar meaning. This makes Milvus useful for semantic search, knowledge bases, document search, image search, recommendation systems, and multimodal applications.

It is also widely used as the retrieval layer behind Generative AI applications. Developers can use Milvus to build RAG systems, AI chatbots, recommendation engines, and AI agents that need to retrieve relevant information from large collections of data before generating an answer. Milvus supports local development as well as Docker and Kubernetes deployments for larger workloads.

Milvus AI Models & Sub-Models

Milvus Lite is the lightweight version of Milvus designed for local development and quick experiments. It works as a Python library and is suitable for Jupyter Notebooks, laptops, and edge devices. It supports core vector search features while using the same API style as larger Milvus deployments.

Milvus Standalone runs Milvus as a single-machine server. It is designed for small to medium production workloads where developers want a simpler deployment without managing a Kubernetes cluster. It can scale to datasets of around 100 million vectors depending on available resources.

Milvus Distributed is the large-scale deployment option designed for Kubernetes. Its components can scale independently, making it suitable for high-traffic enterprise applications and datasets ranging from hundreds of millions to tens of billions of vectors.

Dense Vector Search searches vectors generated from AI embeddings. It is commonly used for semantic search, RAG, recommendation systems, and other applications where understanding similarity is important.

Sparse Vector Search is designed for sparse representations where only some dimensions contain meaningful information. It can work alongside dense vectors for more flexible information retrieval.

Hybrid Search combines different search methods, including dense and sparse vector search, to improve retrieval quality. This is especially useful when an application needs to understand both semantic meaning and important terms.

Multi-Vector Search allows applications to work with multiple vector representations for the same data. This can be useful for more advanced retrieval and multimodal AI systems. Milvus also supports filtering and structured data alongside vector search.

Pricing

Milvus is 100% free and open source under the Apache 2.0 license. Developers can self-host it locally, on their own servers, or through Kubernetes without paying for the Milvus software itself. For users who want a fully managed cloud service instead of managing the infrastructure themselves, Zilliz Cloud, the managed Milvus offering, is available with usage-based pricing.

Free: Yes, Milvus open-source software

Paid: Managed cloud option through Zilliz Cloud

Free Trial: Available through the managed cloud offering

Best Use Cases

Milvus is best suited for developers, AI engineers, startups, and enterprises building applications that need fast retrieval from large amounts of unstructured or multimodal data. It can be used for RAG chatbots, enterprise knowledge search, semantic search, recommendation engines, image and video search, document retrieval, AI agents, and personalized applications. Its ability to run locally, on a single server, or across Kubernetes clusters makes it suitable for projects ranging from small prototypes to large-scale AI systems.