Keywords AI
ClickHouse vs Neo4j
Compare ClickHouse and Neo4j side by side. Both are tools in the Vector Databases category.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | freemium | Freemium |
| Best For | Teams needing analytics and vector search in one store | Enterprises that need a mature, production-grade graph database |
| Website | clickhouse.com | neo4j.com |
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| Use Cases | — |
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When to Choose ClickHouse vs Neo4j
- Knowledge-augmented RAG systems
- Fraud detection and network analysis
- Recommendation engines
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About ClickHouse
High-performance analytical database with vector search. Standard for observability + embeddings.
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.
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.