Neptune vs Neo4J: A Head-to-Head Comparison

Ever wondered how Facebook knows who your friends are or how Netflix suggests those perfect binge-worthy shows? It's all about data relationships. These relationships are what enable these platforms to provide you with personalized and engaging experiences. And when your data isn't just isolated pieces of information but a network of datasets, it is managed through graph databases.

In this article, we will be talking about two popular graph databases: the battle-tested and most popular one, Neo4j, and the second one, Amazon Neptune. 

Amazon Neptune

Amazon Neptune is a cloud-based graph database service from AWS. It is built to handle large-scale, interconnected data. You can use it for building social network analysis, recommendation systems, or fraud detection. As you know, relationships between data points are the most crucial for building these kinds of systems. 

Neptune lets you explore how your data connects by using Apache TinkerPop Gremlin (a graph traversal language that helps you walk through relationships step by step) and SPARQL. 

Neptune works with two types of graphs.

1. RDF-based knowledge graphs with SPARQL

2. Property graphs 

These RDF-based knowledge graphs store information in small pieces, like “A is connected to B.” These pieces link together to show how things relate. They’re useful because they help computers understand data more like humans do. 

A property graph shows things like people or products as dots. These dots are also called vertices, which are connected through lines (called edges). This way, property graphs describe relationships. 

As an AWS product, it lets you manage and store data without getting into technical details. It can scale itself automatically. Plus, you can integrate it with other AWS services. It is used in building apps that query complex relationships. What does this mean? We can better understand it with this example below. 

Let’s say you are building an app to find the shortest path on the map.

The app needs to understand how different locations are connected. Figuring out the best route is what we can call "querying complex relationships."

Neptune is a purpose-built offering by AWS. This means it is designed to handle graph data only. 

While RDF graphs are a more specialized type of graph used to represent data following the Resource Description Framework (RDF) standard, their approach to information representation differs from that of property graphs.

In RDF, each resource is represented as a vertex. These vertices can be anything from web pages and documents to real-world entities such as people, places, or events. It's commonly used to describe the relationships and attributes of these resources in a machine-readable format, allowing computers to understand and process the data.

Neo4J

Neo4j is a graph database or a graph database management system (GDBMS). It stores data using nodes and edges. Nodes can be entities like people or things. Edges represent the relationships between those entities. Both nodes and edges can have properties (extra information about them). It lets you manage and query data using its exclusive query language, Cypher.

It can run queries 100x faster than relational databases, making it a strong choice for AI development that requires complex and fast computation. It is also compatible with property graphs.

As per DB engines's ranking, it is the most popular graph database management system  and is embraced by over 75% of Fortune 500 companies.

In terms of performance, it is almost as fast as its counterpart.  Also, it can be used on-premises or in the cloud, unlike Neptune, which is designed exclusively for the cloud environment. 

Neptune vs. Neo4J

When it comes to popularity, Neptune is not on the same level as Neo4j. However, it's important to consider that Neptune is relatively new to the scene. 

Source: DB-Engines Graph DBMS Ranking

Let's break it down with a quick head-to-head. We'll keep it brief and to the point, focusing on a concise comparison rather than delving into all the features.

1. Developer

Amazon Neptune is developed by Amazon Web Services (AWS), most popular cloud service-provider on the planet. Therefore, it can only be hosted exclusively on AWS cloud infrastructure. If you run your business or services on AWS environment, Neo4J is a perfect choice for you. 

Neo4j is an independent graph database platform developed by Neo4j, Inc. It supports flexible deployment across various cloud providers (e.g., AWS, Azure, GCP), as well as on-premises environments, giving developers more control over infrastructure.

2. Data Model

Neptune supports both property graphs and RDF (Resource Description Framework) models. However, Neptune restricts nodes to a single label, which can limit flexibility when querying based on multiple labels. 

Neo4j focuses exclusively on the property graph model, allowing multiple labels per node. This makes it a more flexible service for querying. 

3. Query Language

Neptune uses Apache TinkerPop Gremlin for property graph queries, a traversal-based language for programmatic graph exploration, and SPARQL for RDF graph queries, a standard for semantic data. 

Neo4j uses Cypher, a declarative query language optimized for graph data. Cypher’s SQL-like syntax is intuitive for developers, making it easier to express complex graph patterns (e.g., finding paths between nodes). It’s widely adopted and considered a standard for property graph querying.

Neptune also supports openCypher (a subset of Neo4j’s Cypher) for property graphs, though some clauses like MANDATORY MATCH are unsupported.

4. Programming Language Support

Neptune supports a wide range of programming languages through AWS SDKs and Gremlin/SPARQL clients, including C#, Go, Java, JavaScript, PHP, Python, Ruby, and Scala. 

Neo4j provides official drivers for .NET, Java, JavaScript, Go, and Python, with additional community-supported drivers for other languages. Its Java-based architecture makes it particularly strong for JVM ecosystems.

5. ACID Compliance

Neptune offers immediate consistency on the primary writer instance and eventual consistency on read replicas. Neo4j is fully ACID-compliant.

6. Data Loading

Neptune provides bulk loading capabilities via AWS APIs, supporting CSV and graph-specific formats like Gremlin and RDF. Neo4j supports batch data loading through tools like neo4j-admin import for high-performance imports and Cypher-based loading for smaller datasets. Its APOC library extends data import capabilities with support for JSON, CSV, and more.

7. Deployment Options

Neptune is a cloud-only, fully managed service within the AWS ecosystem. It cannot be deployed on-premises, limiting its use to AWS-based architectures. Neo4j offers flexible deployment: cloud, on-premises, or hybrid. 

8. Performance

Neptune is optimized for cloud workloads. It leverages AWS’s distributed storage for low-latency queries (milliseconds for billions of relationships). Its decoupled architecture separates compute and storage for dynamic scaling. It can handle over 100,000 queries per second in large-scale setups.

Neo4j is efficient for for complex traversals, due to its native graph storage and indexing. Performance depends on data modeling and query design, but it often excels in scenarios requiring real-time graph analytics.

9. Graph Algorithms

Neptune offers built-in graph algorithms. It integrates with AWS SageMaker for advanced analytics but lacks Neo4j’s extensive algorithm library. 

Neo4j’s Graph Data Science (GDS) library includes a wealth of algorithms (e.g., PageRank, shortest path, community detection). It is well-optimized for applications like recommendation engines and fraud detection.

10. Licensing

Neptune operates under a managed service model with pay-as-you-go pricing based on instance usage, storage, and I/O. There’s no free tier, and costs are tied to AWS’s pricing structure. Checkout Neptune pricing page to request quote.

Neo4j offers a Community Edition (open-source, GPL3-licensed) for free use and an Enterprise Edition with commercial licensing for advanced features like clustering and GDS. This dual model supports both experimentation and production use.

11. Custom Functions

Neptune supports user-defined functions in Gremlin and SPARQL, allowing developers to extend query logic for specific use cases, though customization is constrained by its managed nature.

Neo4j enables custom procedures and functions via its Java-based API and the APOC library, offering extensive flexibility to implement graph algorithms, utilities, or integrations. Python support is also available in some contexts.

12. Security Features

Neptune supports encryption at rest and in transit, offers VPC isolation and compliance with industry standards (e.g., GDPR, HIPAA).

Neo4j provides role-based access control, encryption, and authentication mechanisms. Its Enterprise Edition includes advanced security features like auditing for enterprise compliance.

13. Community and Support

Neptune benefits from AWS’s support infrastructure, including documentation, forums, and professional services. Its community is smaller, primarily tied to AWS users, but growing with recent integrations.

Neo4j has a large, active open-source community with extensive resources, tutorials, and third-party tools. Commercial support is available via Neo4j, Inc., and its ecosystem is bolstered by over 10 million downloads and 75% Fortune 500 adoption.

14. Data Visualization

Neptune lacks native visualization tools but supports querying and visualization via AWS SageMaker Jupyter notebooks or third-party tools like Metaphactory, Tom Sawyer, or Keylines. These require additional setup or costs.

Neo4j provides a built-in browser with D3.js-based visualization for querying and exploring graphs. It also integrates with third-party tools like Gephi and Tableau for advanced visualization.

15. Data Backup and Recovery

Neptune offers automated backups to Amazon S3, point-in-time recovery, and replication across Availability Zones for durability. Failovers typically occur in under 30 seconds with zero data loss.

Neo4j supports manual and automated backups with tools like neo4j-admin backup and provides recovery mechanisms for data restoration. Enterprise Edition includes online backups for production environments.

16. Pricing

Neptune follows a pay-as-you-go model, charging for instance hours, storage, and I/O. Costs vary by region and workload; for details, visit the AWS Neptune pricing page.

Neo4j’s Community Edition is free, while the Enterprise Edition requires commercial licensing with pricing based on features and deployment scale. For details, visit the Neo4j pricing page.

Use Cases and Industries

Neptune is suited for AWS-centric workloads like knowledge graphs, fraud detection, and recommendation engines. Its integration with AWS services (e.g., Lambda, S3) makes it ideal for industries leveraging cloud-native architectures, such as e-commerce and finance.

Neo4j is an excellent choice for social networks, recommendation systems, fraud detection, and network analysis. 

Companies Using Neptune

Neptune is used by a variety of companies, including Amazon, Cox Automotive, Siemens AG, Accenture, Netflix, Nike, and Capital One Financial. These companies use Neptune to power a variety of applications, such as fraud detection, recommendation engines, and social networking.

Companies Using Neo4J

Neo4j is used by Volvo, Marriott, Verizon, eBay, Airbus, JPMorgan, Cisco, LinkedIn, and Walmart for fraud detection, recommendations, supply chain, and network analysis. Over 75% of Fortune 100 companies leverage Neo4j’s graph database capabilities.