Qdrant Has a Free Vector Database Optimized for AI and Semantic Search

Qdrant is a vector similarity search engine built in Rust. It stores and searches high-dimensional vectors for AI applications like RAG, recommendations, and semantic search.

What You Get for Free

• HNSW indexing — fast approximate nearest neighbor search
• Filtering — combine vector search with payload filters
• Quantization — reduce memory usage by 4x
• Distributed mode — horizontal scaling
• REST + gRPC APIs — multiple client libraries
• Snapshots — backup and restore
• Multi-tenancy — tenant isolation via payload

Quick Start

docker run -p 6333:6333 qdrant/qdrant

Store and Search Vectors (Python)

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

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

client.create_collection('docs', vectors_config=VectorParams(size=384, distance=Distance.COSINE))

client.upsert('docs', points=[
    PointStruct(id=1, vector=[0.1]*384, payload={'text': 'Hello world'}),
])

results = client.search('docs', query_vector=[0.1]*384, limit=5)

Qdrant vs Pinecone

Feature	Qdrant	Pinecone
Price	Free (OSS)	Freemium
Hosting	Self-hosted + cloud	Cloud only
Filtering	Rich payload filters	Metadata filters
Performance	Rust (fast)	Managed

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Need vector search setup? Check my work on GitHub or email [e-mail] for consulting.