Qdrant

Qdrant is a powerful open-source vector similarity search engine built specifically for production AI and machine learning applications. With over 28,500 GitHub stars, it has become the go-to solution for developers building semantic search, recommendation systems, and AI-powered applications that need to efficiently store and search high-dimensional vectors. Qdrant combines the flexibility of modern vector databases with the performance requirements of production systems.

Common Use Cases

AI application developers use Qdrant to build semantic search engines that understand meaning rather than just keywords, powering intelligent document search and content discovery systems. E-commerce platforms leverage Qdrant for product recommendations, finding similar items based on user behavior and product features encoded as vectors. Content platforms implement Qdrant to enable similarity-based content matching, duplicate detection, and personalized content feeds. Machine learning teams utilize Qdrant as a feature store for embedding vectors, enabling rapid similarity queries and nearest neighbor searches in high-dimensional spaces.

Key Features

• High-performance vector similarity search with sub-millisecond latency
• Support for multiple distance metrics (Cosine, Euclidean, Dot product)
• Advanced filtering and faceted search capabilities
• Real-time vector updates and deletions
• Horizontal scaling with distributed architecture
• Payload storage alongside vectors for rich metadata
• RESTful and gRPC APIs for easy integration
• Custom scoring functions and query-time filtering
• Snapshot and backup functionality
• Comprehensive monitoring and metrics
• HNSW and other advanced indexing algorithms
• Multi-vector support for complex AI applications
• Quantization support for memory optimization
• Built-in authentication and security features

Why deploy Qdrant on Hostinger VPS

Deploying Qdrant on Hostinger VPS provides dedicated computational resources essential for vector operations and similarity calculations, ensuring consistent performance for AI workloads. The VPS offers the memory and CPU control needed to optimize vector indexing and search performance based on your specific dataset size and query patterns. With persistent storage for your vector collections and the ability to scale resources as your AI applications grow, you maintain full control over your vector data without the limitations of managed services. This deployment is perfect for AI startups and enterprises that need reliable, high-performance vector search capabilities without the complexity and cost of cloud-based vector database services.