Milvus

Milvus is the world's most popular open-source vector database, purpose-built for the AI era to power next-generation applications requiring semantic search, recommendation systems, and retrieval-augmented generation (RAG). As machine learning models transform unstructured data like text, images, audio, and video into high-dimensional vector embeddings, Milvus provides the specialized infrastructure to store billions of these vectors and perform similarity searches at scale with sub-millisecond latency. Developed by Zilliz and open-sourced in 2019, Milvus has been adopted by thousands of organizations worldwide, from AI startups building RAG applications to enterprises deploying production recommendation engines processing billions of daily queries.

Common Use Cases

AI developers building Retrieval-Augmented Generation (RAG) systems use Milvus to store document embeddings and retrieve relevant context for large language models, enabling chatbots and question-answering applications that provide factually accurate responses grounded in company knowledge bases. E-commerce platforms leverage Milvus for visual product search and recommendation engines, allowing customers to find similar items by uploading images or browsing personalized suggestions based on behavioral embeddings. Content platforms implement semantic search powered by Milvus to help users discover relevant articles, videos, or music even when search queries don't match exact keywords, understanding intent rather than just text matching. Security and fraud detection teams use Milvus to identify similar patterns in transaction embeddings, detecting anomalies and potential threats by finding nearest-neighbor matches to known suspicious activities. Drug discovery researchers query molecular structure embeddings in Milvus to find similar compounds, accelerating pharmaceutical research by identifying promising candidates from millions of molecular representations.

Key Features

• High-performance vector similarity search with sub-millisecond latency
• Support for multiple index types: HNSW, IVF, FLAT, SCANN, DiskANN
• GPU acceleration for indexing and search operations
• Hybrid search combining dense vectors, sparse vectors, and metadata filtering
• Horizontal scalability with separated compute and storage architecture
• Multi-tenancy support through databases, collections, and partitions
• ACID transactions for data consistency
• Dynamic schema with flexible metadata fields
• Built-in data replication and high availability
• RESTful and gRPC APIs with clients for Python, Java, Go, Node.js
• Time travel queries for historical data access
• Attu web UI for visual database management and query execution
• MinIO console for object storage management
• Support for billions of vectors with billions of queries per day

Why deploy Milvus on Hostinger VPS

Deploying Milvus on Hostinger VPS provides dedicated computational resources essential for vector indexing and high-dimensional similarity calculations, ensuring consistent query performance even under heavy AI workload demands. Your vector embeddings and metadata remain completely private on your infrastructure, critical for AI applications processing sensitive user data, proprietary documents, or confidential business information that cannot be sent to third-party vector database services. The VPS environment delivers sufficient memory for loading vector indexes into RAM where Milvus achieves its fastest search performance, while dedicated CPU and optional GPU resources accelerate index building and query processing. You gain full control over Milvus configuration including index parameters, cache sizing, and performance tuning, with the ability to scale storage as your vector collections grow from millions to billions of embeddings. The professional infrastructure supports the reliability requirements of production AI applications, while direct server access enables custom deployment architectures like dedicated query nodes or specialized index configurations optimized for your specific embedding dimensions and search patterns.