Qdrant
High-performance vector database for similarity search
About
Qdrant is a vector similarity search engine and database written in Rust for low-latency, high-load workloads. It stores points that combine a vector with a JSON payload and supports extended filtering on that payload during search, which suits semantic matching, faceted search, and recommendations. It runs as a self-hosted service or managed cloud and exposes REST and gRPC APIs. Released under the Apache 2.0 license.
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Details
- Category
- RAG & Document Retrieval
- Price
- Freemium
- Platform
- Hybrid
- Difficulty
- Easy (2/5)
- License
- Apache-2.0
- Added
- Jan 29, 2026
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Mentioned in
RAG Is Dead, Long Live RAG: Where Retrieval Is Going
The 'RAG is dead' meme misses what is actually happening. Hybrid retrieval, late-interaction models, agentic...
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Vector Database Benchmarks: Qdrant vs Milvus vs Weaviate vs LanceDB
A qualitative comparison of four popular open-source vector databases across architecture, hybrid search,...
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Building a Private RAG Stack with Ollama, Qdrant, and AnythingLLM
An end-to-end blueprint for a fully self-hosted RAG system using Ollama for inference, Qdrant for the vector...
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