Qdrant

Vector Databases & Memory

Open Source AI

Vector database written in Rust for speed and reliability. Advanced filtering on metadata, hybrid search combining vectors and keywords, and built-in payload storage. Multi-tenancy support with collection-level isolation. Cloud or self-hosted.

Why Use Qdrant

Rust implementation delivers exceptional performance and memory efficiency. Advanced filtering lets you combine vector similarity with business logic. Hybrid search beats pure vector search by 30-40% on real-world tasks. Great for combining agent memory with structured queries. Generous free tier with 1GB clusters.

Use Cases for Builders

Practical ways to use Qdrant in your workflow

Build hybrid search combining semantic meaning with filters
Implement agent memory with complex metadata queries
Create multi-tenant applications with strict data isolation
Develop recommendation systems with business rule constraints
Deploy production RAG with filtering by date, category, or custom fields

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