Qdrant Has a Free API You Should Know About

Qdrant is an open-source vector database designed for AI applications. It stores and searches high-dimensional vectors with blazing speed — perfect for semantic search, recommendations, and RAG.

Why Qdrant Powers Modern AI Apps

A startup building a RAG chatbot was storing embeddings in PostgreSQL with pgvector. At 10M vectors, queries took 3 seconds. They switched to Qdrant — same queries now take 15ms.

Key Features:

• Fast Search — HNSW algorithm for sub-millisecond queries
• Filtering — Combine vector similarity with metadata filters
• Payload Storage — Store arbitrary data alongside vectors
• Quantization — Reduce memory usage by 4-32x
• Distributed — Horizontal scaling with sharding

Quick Start

docker run -p 6333:6333 qdrant/qdrant

Python SDK

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

client = QdrantClient("localhost", port=6333)

client.create_collection(
    collection_name="articles",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE)
)

client.upsert(
    collection_name="articles",
    points=[{"id": 1, "vector": embedding, "payload": {"title": "My Article"}}]
)

results = client.search(
    collection_name="articles",
    query_vector=query_embedding,
    limit=5
)

Why Choose Qdrant

1. Blazing fast — optimized for production AI workloads
2. Rich filtering — combine vector search with metadata queries
3. Open source — self-host or use Qdrant Cloud

Check out Qdrant docs to get started.

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Building AI applications? Check out my Apify actors or email [e-mail] for data extraction solutions.