🧩 AI FrameworksOpen SourceFree

Qdrant

High-performance vector database — billion-scale similarity search

4.7/ 5⭐ 22,000 GitHub starsRustApache-2.0

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📋 Overview

Qdrant is a high-performance vector database designed for billion-scale similarity search — supporting dense, sparse, and multimodal vectors in a single engine. Unlike Chroma (embedded, single-node), Qdrant provides a Rust-based implementation with advanced filtering, quantization, and distributed search. It supports HNSW and IVF indexing, product quantization for 4x memory reduction, and payload-based filtering.

✨ Key Features

•27K+ GitHub stars — fastest Rust vector database
•Billion-scale similarity search
•Product quantization for 4x memory reduction
•Payload-based filtering
•Distributed mode
•Apache 2.0 licensed
•OpenAPI interface
•gRPC interface

🎯 The Problem It Solves

RAG and semantic search applications need vector databases that scale beyond millions of embeddings while maintaining low latency. Qdrant delivers billion-scale search with advanced filtering.

🔧 How It Works

Qdrant stores vectors with optional payload metadata and indexes them using HNSW or IVF algorithms. Search combines vector similarity with payload filtering. Product quantization compresses vectors 4x for memory efficiency.

🚀 Installation & Quick Start

Installation

See website

Quick Start

See documentation

✅ Pros

•Fastest self-hosted vector database
•Advanced payload filtering
•Billion-scale search
•Apache 2.0 licensed
•OpenAPI interface
•gRPC interface

❌ Cons

•Smaller community than Milvus
•No built-in embedding model
•Self-hosting requires ops
•Limited documentation

💬 Practitioner Verdict

“Qdrant is the best self-hosted vector database — the Rust implementation is fast and the filtering is unmatched. The trade-off: smaller community than Milvus, no built-in embedding model, and self-hosting requires ops knowledge.”