Qdrant Has a Free Vector Database — Semantic Search and AI Retrieval at Scale

Qdrant is an open-source vector database — store embeddings, perform semantic search, and power RAG applications.

What You Get for Free (Self-hosted / Free Cloud Tier)

• Vector search — cosine, dot product, euclidean similarity
• Payload filtering — combine vector search with metadata filters
• Collections — organize vectors by purpose
• Batch operations — upsert millions of vectors efficiently
• Sparse vectors — hybrid search (dense + sparse)
• Multi-tenancy — isolate data per user/tenant
• Snapshots — backup and restore collections
• REST + gRPC — fast APIs for any language
• Cloud free tier — 1GB RAM cluster, no credit card

Quick Start

docker run -p 6333:6333 qdrant/qdrant

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

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

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

# Add vectors
client.upsert("articles", points=[
    {"id": 1, "vector": embedding, "payload": {"title": "Web Scraping Guide", "category": "tutorial"}}
])

# Search
results = client.search("articles", query_vector=query_embedding, limit=5,
    query_filter={"must": [{"key": "category", "match": {"value": "tutorial"}}]})

Why Developers Switch from Pinecone

Pinecone is cloud-only and gets expensive with scale:

• Self-hosted — run on your server, control your data
• Payload filtering — filter by metadata during search (not post-filter)
• Sparse vectors — hybrid search for better accuracy
• Free cloud tier — 1GB cluster for development

A RAG app on Pinecone cost $70/month for 1M vectors. After Qdrant self-hosted: same vectors, same search quality, $0/month on existing infrastructure.

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