Keywords AI

Chroma vs Neo4j

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

Quick Comparison

	Chroma	Neo4j
Category	Vector Databases	Vector Databases
Pricing	Open Source	Freemium
Best For	Python developers who want a simple, embedded vector database for prototyping	Enterprises that need a mature, production-grade graph database
Website	trychroma.com	neo4j.com
Key Features	Lightweight embedded vector database Python-native API Runs in-process without a server Simple document and query interface Open-source and free	Native graph database with Cypher query language ACID-compliant transactions Graph data science library GenAI integrations Enterprise clustering and scaling
Use Cases	Local development and prototyping Small to medium RAG applications Embedded vector search in Python apps Research and experimentation Serverless and edge deployments	Knowledge-augmented RAG systems Fraud detection and network analysis Recommendation engines Identity and access management Supply chain optimization

When to Choose Chroma vs Neo4j

Choose Chroma if you need

Local development and prototyping
Small to medium RAG applications
Embedded vector search in Python apps

Pricing: Open Source

Choose Neo4j if you need

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

Pricing: Freemium

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 Chroma

Chroma is an open-source embedding database designed for simplicity and developer experience. It provides a lightweight, easy-to-use API for storing, querying, and filtering embeddings locally or in the cloud. Chroma is the default vector store in many LLM frameworks like LangChain and LlamaIndex, making it extremely popular for prototyping and building RAG applications quickly.

View Chroma profile →Visit website

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

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.