High-performance vector database for similarity search, RAG, and AI applications with billion-scale data support.
Milvus is an open-source vector database designed to store, index, and search massive collections of vector embeddings. Built for AI workloads, it supports dense and sparse vector search, full-text search with BM25, and hybrid search with reranking. Milvus runs anywhere from a Jupyter Notebook (Milvus Lite) to a full Kubernetes cluster handling billions of vectors. It is free under the Apache 2.0 license, with Zilliz Cloud available as a fully managed option.
Yes. Milvus is fully open source under the Apache 2.0 license, so you can download, deploy, and run it on your own infrastructure at no software cost. You only pay for the compute, storage, and cloud resources you provision yourself. Milvus also has a managed cloud option called Zilliz Cloud, which offers flexible pricing plans and a free tier for getting started, though specifics (like storage and credits) vary by region and offer.
Milvus is open source and can be self-hosted, giving you full control over your data and infrastructure. Pinecone is a proprietary, fully managed service. Milvus supports more index types, hybrid search, and GPU acceleration. Pinecone is simpler to get started with since there's no infrastructure to manage. Choose Milvus if you need flexibility, cost control, or on-premise deployment. Choose Pinecone if you want a zero-ops managed service.
Milvus provides official SDKs for Python, Java, Go, Node.js, and C#. The Python SDK is the most feature-complete and widely used. You can also interact with Milvus through its RESTful API from any language.
Milvus Distributed on Kubernetes can handle billions of vectors. The architecture separates storage and compute, so you can scale each independently. Companies like Salesforce run Milvus clusters serving 100+ tenants with diverse workloads. For smaller use cases, Milvus Standalone handles millions of vectors on a single machine.
Zilliz Cloud is a fully managed vector database service built on Milvus. It eliminates most operational tasks by offering features like serverless and dedicated clusters, auto‑scaling, and pay‑as‑you‑go pricing. It’s designed to run across major cloud providers and simplifies deployment, scaling, and maintenance compared with self‑hosting.
Yes, RAG is one of the most common use cases for Milvus. You store your document embeddings in Milvus, and when a user asks a question, you search for the most relevant chunks and pass them as context to your LLM. Milvus integrates directly with LangChain, LlamaIndex, and other popular RAG frameworks.
Yes. You can combine vector similarity search with scalar filters on any stored field, including numeric ranges, string matches, JSON field queries, and array contains operations. Milvus evaluates filters during the search process rather than as a post-processing step, which keeps performance high even with complex filter conditions.
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