Reference · Glossary
Qdrant
Last updated
An **open-source vector database** written in Rust, known for fast filtered search and built-in vector quantization to shrink memory use at scale. Available self-hosted or as a managed cloud.
#When to use
High-throughput search with heavy metadata filtering (e.g. "similar products, in stock, under $50"), or when you want quantization to cut hosting costs on large collections.
#When not to
A one-off prototype where the ops overhead of running/monitoring a service isn't worth it yet — start with an in-process store first.
#Example
from qdrant_client import QdrantClient
client = QdrantClient(url="http://localhost:6333")
client.upsert(
collection_name="docs",
points=[{"id": 1, "vector": [0.1, 0.2, 0.3], "payload": {"source": "faq.md"}}],
)
hits = client.query_points(collection_name="docs", query=[0.1, 0.2, 0.3], limit=3)