Neo4j vs Amazon Neptune

Feature comparison

Neo4j is the established graph database leader, designed from the start as a graph-native engine with index-free adjacency for traversal efficiency. The Cypher query language — now standardised as GQL through ISO — is the most expressive and widely adopted graph query language. Neo4j Enterprise supports causal clustering for high availability, role-based access control, and the Graph Data Science library for centrality, community detection, path-finding, and embedding algorithms. Neo4j Aura, the managed service across AWS, Azure, and Google Cloud, provides a polished operational experience with the same engine.

Amazon Neptune is a fully managed graph database service exclusive to AWS. The service supports three query languages on the same underlying engine: Gremlin (Apache TinkerPop) for property graphs, openCypher for property graphs with Cypher-style syntax, and SPARQL for RDF knowledge graphs. The dual property-graph and RDF support is genuinely differentiated; few managed services cover both paradigms. Neptune ML integrates with SageMaker for graph neural networks, supporting node classification, link prediction, and edge regression.

Architecturally, Neo4j stores data using native graph storage with index-free adjacency, traversing relationships in constant time per hop regardless of total dataset size. Neptune is built on a distributed log-structured storage layer shared with Aurora, supporting up to 64TB per cluster with six-way replication across three Availability Zones. The architectural difference manifests in query performance: deep multi-hop traversals tend to favour Neo4j's storage model; large queries on dense graphs with read replicas tend to favour Neptune's distributed read scale.

For developer experience, Cypher and GQL are widely considered more readable and productive than Gremlin for property-graph workloads. Neptune's openCypher support narrows this gap considerably for teams who prefer Cypher syntax but want AWS-managed deployment. SPARQL on Neptune remains essential for organisations with RDF knowledge graphs, semantic web data, or W3C-standard ontologies.

For graph machine learning, Neo4j Graph Data Science library offers a comprehensive in-database library covering centrality, community, similarity, embedding, and link-prediction algorithms. Neptune ML integrates with SageMaker for GNN-based workflows, leveraging AWS's broader machine learning stack. Both are viable; choice typically follows existing ML platform investment.

Pricing comparison

Neo4j Community Edition is free under GPLv3 with single-instance deployment and Cypher support. Neo4j Enterprise prices per cluster or per instance; typical commercial deployments range $20,000–$200,000 annually for production clusters with HA, role-based access, and Graph Data Science. Neo4j Aura Professional starts at approximately $65 per month for small instances; AuraDB Enterprise multi-cloud production tiers typically range $1,500–$25,000 per month. Amazon Neptune prices by instance class (db.t3, db.r5, db.r6g, db.r7g) with on-demand or reserved pricing. Typical production Neptune instances range $300–$6,000 per month per instance before storage ($0.10 per GB-month) and IO charges. Neptune Serverless prices by Neptune Capacity Units (NCUs) similar to Aurora Serverless.

Five-year cost of ownership for a moderate graph workload: Neo4j Aura Enterprise $500K–$2M; Neo4j self-managed Enterprise $300K–$1.5M plus staffing; Amazon Neptune $400K–$1.8M depending on instance class and replica count. The primary buying-side caveat for Neo4j is that Community Edition lacks clustering, role-based security, and the Graph Data Science library at production scale — Enterprise pricing is typically required for serious deployments. The Neptune caveat is AWS lock-in and the gap between marketing claims and real-world deep-traversal performance on dense graphs; benchmarking with representative queries is essential before commitment. Pricing as of May 2026.

When to choose Neo4j

Choose Neo4j when the application is graph-first and Cypher depth matters, when in-database graph data science algorithms are valuable, when multi-cloud deployment via Aura is preferred over AWS lock-in, when the team values the largest graph database community and ecosystem, or when deep multi-hop traversals on dense graphs are core to the workload. Neo4j suits fraud detection, knowledge graphs, identity and access analytics, recommendation engines, supply chain provenance, and any application where graph queries dominate and Cypher expressiveness translates to development velocity.

When to choose Amazon Neptune

Choose Amazon Neptune when the application is AWS-native and tight integration with Lambda, SageMaker, and other AWS services matters, when RDF and SPARQL support is required for semantic web or knowledge graph workloads, when both property-graph and RDF paradigms must be served from one managed service, or when Neptune ML's GNN integration with SageMaker simplifies graph machine learning. Neptune suits AWS-centric data platforms, regulated industries already on AWS, semantic web and bioinformatics workloads using RDF standards, and applications needing graph reasoning with AWS's broader analytics stack.

Alternatives to both