Milvus
The high-performance vector database built for scale
Milvus is an open-source vector database built for GenAI applications. Install with pip, perform high-speed searches, and scale to tens of billions of vectors with minimal performance loss.
Reviews for Milvus
Hear what real users highlight about this tool.
Milvus is highly praised for its scalability and performance in handling large volumes of vector data, making it ideal for GenAI applications. Makers from LLMWare commend its ability to manage hundreds of thousands of documents simultaneously, while cognee highlights its necessity for scalable solutions. Users appreciate its ease of integration, intuitive interface, and comprehensive feature set. The unified API and tailored functionalities are noted for enhancing user experience and meeting diverse business needs. Overall, Milvus is recommended for its robust capabilities and open-source advantages.
This AI-generated snapshot distills top reviewer sentiments.
We chose Milvus for its blazing performance at scale - outclassing alternatives in speed, scalability, and real-time vector retrieval.
Solid opensource DB but chews tons of memory
We also used Milvus Node.js SDK for vector database
Awesome vector database. Impressive performance.
Milvus powers our vector store, enabling lightning-fast similarity search across large knowledge bases. It’s been essential for making Saple agents context-aware.
Milvus powers Antayze's semantic search and AI retrieval systems by handling large-scale vector embeddings with high efficiency. It ensures fast and accurate responses to user queries in real-time conversations. Why it's better: Compared to alternatives like Pinecone or FAISS, Milvus offers superior performance at scale, flexible deployment options, and robust community support, making it ideal for production-grade AI applications.
Like it for fast response, open source framework allowing self-managed cost, and high customization.
Purpose-built for managing vector embeddings, ideal for applications like semantic search, recommendation systems, and AI-based content understanding. Open-source and designed for high scalability. Strong community support and integrations with tools like PyTorch, TensorFlow, and Hugging Face.
At Wisemelon AI, we chose Milvus as our vector database because it’s purpose-built for managing, indexing, and searching massive amounts of unstructured data with unparalleled speed and precision. Alternatives like Pinecone, Weaviate, and Qdrant were considered, but Milvus’s scalability, open-source flexibility, and high performance made it the clear choice for our needs.
Milvus excels in handling high-dimensional vector data, which is the backbone of AI models powering Wisemelon. With its robust architecture and seamless integration with machine learning pipelines, it enables us to provide lightning-fast, highly accurate search and retrieval capabilities for complex use cases. From powering recommendations and semantic search to supporting our Retrieval-Augmented Generation (RAG) workflows, Milvus ensures Wisemelon remains efficient and cutting-edge.
Unlike some alternatives that can struggle with scale or demand higher resource consumption, Milvus is designed to handle millions—even billions—of vectors while maintaining excellent query performance. This scalability is critical for Wisemelon as we continue to grow and serve diverse industries with massive datasets.
By leveraging Milvus, Wisemelon guarantees a responsive, scalable, and reliable infrastructure that underpins our AI solutions, ensuring every user interaction is as fast and intelligent as possible.
The unified API is a huge plus—no more rewriting code when scaling up. Highly recommend for anyone working with vector search on a local environment.
We use Milvus as our vector library. It not only supports multi-dimensional sparse embeddings but also has very good performance. It almost undertakes our most core modules.