Qdrant - Vector Similarity Search Engine

High-performance vector database written in Rust for production RAG and semantic search.

When to use Qdrant

Use Qdrant when:

• Building production RAG systems requiring low latency
• Need hybrid search (vectors + metadata filtering)
• Require horizontal scaling with sharding/replication
• Want on-premise deployment with full data control
• Need multi-vector storage per record (dense + sparse)
• Building real-time recommendation systems

Key features:

• Rust-powered: Memory-safe, high performance
• Rich filtering: Filter by any payload field during search
• Multiple vectors: Dense, sparse, multi-dense per point
• Quantization: Scalar, product, binary for memory efficiency
• Distributed: Raft consensus, sharding, replication
• REST + gRPC: Both APIs with full feature parity

Use alternatives instead:

• Chroma: Simpler setup, embedded use cases
• FAISS: Maximum raw speed, research/batch processing
• Pinecone: Fully managed, zero ops preferred
• Weaviate: GraphQL preference, built-in vectorizers

Quick start

Installation

# Python client
pip install qdrant-client

# Docker (recommended for development)
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

# Docker with persistent storage
docker run -p 6333:6333 -p 6334:6334 \
    -v $(pwd)/qdrant_storage:/qdrant/storage \
    qdrant/qdrant

Basic usage

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

# Connect to Qdrant
client = QdrantClient(host="localhost", port=6333)

# Create collection
client.create_collection(
    collection_name="documents",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE)
)