Keywords AI

Neo4j vs Qdrant

Compare Neo4j and Qdrant side by side. Both are tools in the Vector Databases category.

Quick Comparison

	Neo4j	Qdrant
Category	Vector Databases	Vector Databases
Pricing	Freemium	Open Source
Best For	Enterprises that need a mature, production-grade graph database	Engineering teams who need a fast, self-hosted vector database with strong filtering
Website	neo4j.com	qdrant.tech
Key Features	Native graph database with Cypher query language ACID-compliant transactions Graph data science library GenAI integrations Enterprise clustering and scaling	High-performance open-source vector search Written in Rust for speed Advanced filtering with payload indexes Distributed deployment support On-disk storage for cost efficiency
Use Cases	Knowledge-augmented RAG systems Fraud detection and network analysis Recommendation engines Identity and access management Supply chain optimization	High-throughput vector search Production RAG with complex filtering Self-hosted vector infrastructure Real-time similarity matching Cost-efficient large-scale deployments

When to Choose Neo4j vs Qdrant

Choose Neo4j if you need

Knowledge-augmented RAG systems
Fraud detection and network analysis
Recommendation engines

Pricing: Freemium

Choose Qdrant if you need

High-throughput vector search
Production RAG with complex filtering
Self-hosted vector infrastructure

Pricing: Open Source

How to Choose a Vector Databases Tool

Key criteria to evaluate when comparing Vector Databases solutions:

Query performanceSearch latency and throughput at your expected data scale and query volume.

ScalabilityHow well the database handles growing from thousands to billions of vectors.

Hosting modelFully managed cloud, self-hosted, or embedded options depending on your infrastructure needs.

Filtering supportAbility to combine vector similarity search with metadata filters efficiently.

Integration ecosystemNative integrations with popular frameworks like LangChain, LlamaIndex, and Haystack.

About Neo4j

Neo4j is the world's leading graph database, widely used for building knowledge graphs that power AI applications. Its native graph storage and Cypher query language enable complex relationship queries, pattern matching, and path finding. Neo4j's GenAI integrations include vector search, LLM-powered knowledge graph construction, and GraphRAG capabilities that combine structured graph data with LLM reasoning for more accurate, explainable AI.

View Neo4j profile →Visit website

About Qdrant

Qdrant is a high-performance open-source vector database written in Rust, optimized for speed and reliability. It supports advanced filtering with payload indexes, quantization for memory efficiency, and distributed deployments for horizontal scaling. Qdrant offers a managed cloud service and is popular with teams that need production-grade vector search with fine-grained control over indexing and query parameters.

View Qdrant profile →Visit website

What is Vector Databases?

Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.

Browse all Vector Databases tools →

Frequently Asked Questions

What is a vector database?▾

A vector database stores high-dimensional numerical representations (embeddings) of data like text, images, or audio, and enables fast similarity search across millions or billions of vectors using approximate nearest neighbor algorithms.

Do I need a dedicated vector database or can I use pgvector?▾

For small to medium datasets (under 10 million vectors), pgvector in PostgreSQL works well and avoids adding another service. For larger datasets or when you need advanced features like hybrid search and real-time indexing, a dedicated vector database is recommended.

How do I choose the right embedding model for my vector database?▾

Match the embedding model to your use case. For general text search, models like OpenAI text-embedding-3 or Cohere embed-v3 work well. For domain-specific applications, consider fine-tuned models. Always benchmark with your actual data.