Features
- Written in Rust for maximum performance
- Rich filtering with payload-based conditions
- Quantization for reduced memory usage
- Distributed deployment with sharding and replication
Pros
- Excellent performance with low resource usage
- Powerful filtering system with nested conditions
- Open-source with managed cloud option
Cons
- Smaller ecosystem than Pinecone
- Self-hosted requires ops knowledge
- API is less mature than older databases
Overview
Qdrant is a high-performance vector similarity search engine and database written in Rust. It is designed for production-grade applications that need fast, accurate nearest neighbor search with rich filtering capabilities.
Qdrant’s filtering system is one of its strongest features. Each vector can have an associated payload (metadata), and queries can combine vector similarity with complex filter conditions including nested objects, arrays, geo-coordinates, and range queries. Filters are applied during the search, not after, ensuring accurate results even with strict filters.
The database supports scalar and product quantization to reduce memory usage by up to 4x with minimal accuracy loss, making it practical to store large vector collections on modest hardware. It can be deployed as a single node or as a distributed cluster with sharding and replication.
When to Use
Choose Qdrant when you need high-performance vector search with complex filtering requirements. It is ideal for production applications where query speed, memory efficiency, and filtering flexibility are critical.
Getting Started
docker run -p 6333:6333 qdrant/qdrantimport { QdrantClient } from '@qdrant/js-client-rest'
const client = new QdrantClient({ url: 'http://localhost:6333' })
await client.upsert('my_collection', {
points: [{ id: 1, vector: [0.1, 0.2, 0.3], payload: { city: 'Tokyo' } }]
})