Milvus

Milvus is a high-performance, cloud-native vector database with 46,400+ stars on GitHub, built for billion-scale similarity search over embeddings — the retrieval backbone behind many RAG and AI search systems.

Overview

Modern AI applications run on embeddings: representations of text, images, audio and video that need to be searched by similarity in milliseconds. Milvus, created by Zilliz and now a graduated LF AI & Data project, stores those embeddings and answers nearest-neighbor queries at very large scale using index families such as HNSW, IVF and DiskANN, with optional GPU acceleration. Its distributed architecture separates storage from compute, so collections scale horizontally on Kubernetes from a laptop prototype to billions of vectors, and it supports hybrid search that combines vector similarity with scalar filtering on metadata. Official SDKs cover Python, Java, Go, Node.js and more, and the database integrates with the mainstream AI stack — LangChain, LlamaIndex and embedding providers — for retrieval-augmented generation, semantic search, recommendation and multimodal retrieval. Milvus is Apache-licensed and under very active development in 2026.

Key Features

• Billion-scale ANN search – millisecond similarity queries over enormous embedding collections.
• Multiple index types – HNSW, IVF, DiskANN and others to trade recall, speed and memory.
• Cloud-native distribution – storage and compute scale independently on Kubernetes.
• Hybrid search – combine dense and sparse vectors with scalar metadata filters.
• GPU acceleration – GPU-based indexing and search for the largest workloads.
• Multi-language SDKs – Python, Java, Go, Node.js, C# and REST APIs.
• RAG-ready integrations – first-class support in LangChain, LlamaIndex and other frameworks.

💳 Pricing

Milvus itself is free and open source under the Apache License 2.0 for self-hosting. Zilliz Cloud offers a fully managed Milvus service with free and paid tiers for teams that prefer not to operate the database themselves.

Who Is It For

AI engineers and platform teams building semantic search, RAG pipelines, recommendation engines and multimodal retrieval that must stay fast as data grows from thousands to billions of vectors.