Qdrant Has a Free Vector Database — Lightning-Fast Similarity Search for AI Applications

Qdrant is a high-performance vector search engine built in Rust for AI applications.

What You Get for Free

• 1GB storage — Qdrant Cloud free tier
• Vector search — cosine, dot product, Euclidean distance
• Filtering — combine vector search with metadata filters
• Sparse vectors — hybrid search (dense + sparse)
• Multi-tenancy — isolate data with payload-based filtering
• Quantization — reduce memory usage by 4-32x
• Snapshots — backup and restore collections
• REST + gRPC APIs — high-performance clients
• SDKs — Python, JavaScript, Go, Rust, Java
• Self-hosted — free, unlimited storage

Quick Start

# Docker
docker run -d -p 6333:6333 qdrant/qdrant

# Or Qdrant Cloud: cloud.qdrant.io (1GB free)

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

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

# Create collection
client.create_collection("docs", vectors_config=VectorParams(size=1536, distance=Distance.COSINE))

# Insert vectors (from OpenAI embeddings, etc.)
client.upsert("docs", points=[
    {"id": 1, "vector": embedding, "payload": {"text": "document content"}}
])

# Search
results = client.query_points("docs", query=query_vector, limit=5)

Why AI Developers Choose It

pgvector is basic. Pinecone is expensive ($70/mo+):

• Purpose-built — not a database extension, designed for vectors
• Rust performance — handles billions of vectors efficiently
• Filtering — Pinecone charges for metadata filtering
• Self-hosted — free unlimited storage on your server

An AI startup's RAG app used pgvector with 10M embeddings — queries took 2 seconds. They switched to Qdrant — same queries in 15ms, and the filtering capabilities let them implement multi-tenant vector search without separate databases.

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