Qdrant

Qdrant

Qdrant is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points — vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for neural-network or semantic-based matching, faceted search, and RAG applications.

Is Qdrant free to self-host?

Yes — it is open source and runs on your own hardware.

Does Qdrant need a lot of resources?

A server with 1–2GB RAM is enough for most workloads.

How do I deploy Qdrant?

Easiest via Docker — see the installation guide for commands.

Where does my data go?

Nowhere — everything is processed locally on your server.

Qdrant

Qdrant is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points — vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for neural-network or semantic-based matching, faceted search, and RAG applications.

Alternatives to Qdrant

If Qdrant isn't quite right, these are common self-hosted alternatives in the same category:

Qdrant

Qdrant is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points — vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for neural-network or semantic-based matching, faceted search, and RAG applications.

Strengths

• Full self-hosted control over your data
• Straightforward Docker-based deployment
• Open-source license

Weaknesses

• Initial setup requires Docker familiarity
• You are responsible for maintenance and updates
• Resource needs can grow under heavy load

Verdict

Qdrant is a solid self-hosted choice — its strengths outweigh the usual maintenance overhead.

Qdrant

Qdrant is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points — vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for neural-network or semantic-based matching, faceted search, and RAG applications.

Prerequisites

• Docker installed (version 24.0+)
• Docker Compose (version 2.20+)
• At least 1GB RAM (2GB recommended)

Quick start with Docker

# Pull the image
docker pull qdrant/qdrant:latest

# Run the container
docker run -d --name qdrant -p 8080:8080 qdrant/qdrant:latest

Key features

• Self-hosted and open source
• Docker-based deployment
• License: Apache-2.0
• Repository: https://github.com/qdrant/qdrant
• Docker image: qdrant/qdrant:latest

Qdrant

Qdrant is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points — vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for neural-network or semantic-based matching, faceted search, and RAG applications.

Key features

• Self-hosted and open source
• Docker-based deployment
• License: Apache-2.0
• Repository: https://github.com/qdrant/qdrant
• Docker image: qdrant/qdrant:latest

What it's good for

Qdrant runs entirely on your own infrastructure — your data never leaves your server.