Overview
Qdrant (read: "quadrant") is an open-source vector similarity search engine and vector database. It stores points - vectors paired with an extra payload - and lets you search them by similarity, with filtering on the payload data. It exposes a service with an API so you can store, search, and manage these vectors from your application.
It is written in Rust, which helps it stay fast and reliable under high load. Qdrant is built for teams that turn embeddings or neural-network encoders into real applications for matching, searching, and recommending. Its extended filtering support also makes it useful for faceted search and other semantic matching tasks.
As a vector database, Qdrant sits between your embedding model and your app. You run it as a server (locally via Docker or self-hosted), or use the fully managed Qdrant Cloud, which includes a free tier. A lightweight option, Qdrant Edge, can run inside your application process for offline or low-latency use.
What it does
- Vector similarity search with an extended filtering layer over the payload attached to each point
- Written in Rust for speed and stability under high request load
- Official client libraries for Python, JavaScript/TypeScript, Go, Rust, .NET/C#, and Java
- Production-ready service with a convenient API to store, search, and manage points
- Qdrant Edge runs inside your application process for offline and low-latency use on edge devices
- Available self-hosted via Docker or as the fully managed Qdrant Cloud with a free tier
Getting started
Run Qdrant locally with Docker, then connect to it with one of the official clients such as Python.
Start the Qdrant server
Run the container to start Qdrant locally on port 6333. Note this starts an insecure deployment without authentication, so secure your instance before production.
docker run -p 6333:6333 qdrant/qdrantConnect with the Python client
Point the client at your running server to begin storing and searching vectors.
from qdrant_client import QdrantClient
client = QdrantClient(url="http://localhost:6333")Read the docs before production
Before deploying to production, follow the official installation and security guides at qdrant.tech, and check the Quick Start guide for a full walkthrough.
Commands and code are distilled from the project's own documentation — always check the official repo for the latest.
When to use it
- Build a semantic text search engine that finds meaning rather than matching keywords
- Power recommendation systems by matching embeddings from a neural-network encoder
- Run similar-image search, such as visual food discovery, without relying on text descriptions
- Add faceted or filtered semantic search by combining vector similarity with payload filters
How Qdrant compares
Qdrant alongside other open-source vector databases tools AI/TLDR tracks, ranked by GitHub stars.
| Tool | Stars | What it does |
|---|---|---|
| Supabase | ★ 105k | Managed Postgres backend whose Vector toolkit (pgvector) stores, indexes, and queries embeddings next to transactional data. |
| Redis Cloud | ★ 75k | Fully-managed Redis with built-in vector search, offering low-latency similarity and hybrid queries over any embeddings. |
| Milvus | ★ 44.9k | A distributed vector database for storing and searching billions of embeddings at scale, with multiple index types and Kubernetes-native deployment. |
| FAISS | ★ 40.4k | A library from Meta for efficient similarity search and clustering of dense vectors, with both exact and approximate indexes. |
| Qdrant | ★ 32.5k | A Rust vector search engine with rich payload filtering for semantic search |
| Chroma | ★ 28.5k | A developer-focused vector database designed for quickly building retrieval and RAG features with a simple Python and JavaScript API. |
| pgvector | ★ 21.8k | A PostgreSQL extension that adds a vector data type and similarity search so you can store and query embeddings inside an existing Postgres database. |
| Weaviate | ★ 16.4k | A vector database with built-in hybrid search and LLM-provider integrations for building semantic search and retrieval applications. |