Qdrant is a vector database optimized for semantic search and similarity matching — storing embeddings at scale with sub-millisecond search latency, perfect for AI applications, recommendation engines, and semantic search.

What Is Qdrant?

• Type: Specialized vector database for embeddings.
• Performance: Sub-millisecond search on millions of vectors.
• Architecture: Optimized for HNSW (hierarchical navigable small world).
• Deployment: Cloud, self-hosted, hybrid.
• Scaling: Distributed clustering for unlimited scale.

Why Qdrant Matters

• Fast: Sub-millisecond search on massive datasets.
• Accurate: Built specifically for vector similarity (not retrofitted).
• Flexible Filtering: Combine vector search with metadata filters.
• Production-Ready: Used by enterprises for real-time inference.
• Open Source: Full control and transparency.
• Multi-Model: Store multiple embeddings per item.

Key Features

Efficient Storage: Compressed vectors reduce memory 30-50%. Filtering: Combine semantic search with exact matches. Payload: Store metadata alongside vectors. Replication: High availability and disaster recovery. Sharding: Distribute across multiple nodes.

Quick Start

from qdrant_client import QdrantClient

client = QdrantClient(url="http://localhost:6333")

# Create collection
client.create_collection(
    collection_name="documents",
    vectors_config={"size": 768}
)

# Add vectors
client.upsert(
    collection_name="documents",
    points={
        "id": 1,
        "vector": [0.1, 0.2, ...],
        "payload": {"title": "doc1"}
    }
)

# Search
results = client.search(
    collection_name="documents",
    query_vector=[0.1, 0.2, ...],
    limit=10
)

Alternatives: Pinecone, Weaviate, Milvus, Chroma.

Qdrant is the vector database for AI applications — sub-millisecond semantic search at scale.