Graph Databases Comparison

Property graphs (Neo4j, Amazon Neptune) vs RDF triplestores (Apache Jena). Query languages (Cypher, Gremlin, SPARQL). Use cases: social networks, recommendation engines, knowledge graphs.

Database Types
8 entries
CC0 — public domain

JSON15,123 B
YAML11,590 B · −23%
TOON10,365 B · −31%

A comparison of graph database systems by data model, query language, storage and ideal use cases, including Neo4j, Amazon Neptune, TigerGraph and ArangoDB, with scaling notes that distinguish native graph stores from multi-model engines.

Use it when

You are choosing a graph database or a graph query language for relationship-heavy data.
You need to compare property-graph and RDF stores.

Watch out for

Graph databases pay off when queries traverse many relationships; for mostly tabular lookups a relational database is usually simpler.
Query language support (Cypher, Gremlin, SPARQL, GQL) differs by product.

The data

Databases

Name	Type	Query language	Storage model	Strengths	Ideal use cases	Scaling notes
Neo4j	Property Graph	Cypher (declarative), also supports Gremlin via plugin, and APOC procedures	Native graph storage (index-free adjacency). Each node/relationship stored as record with pointers to adjacent nodes. Property stores on nodes/relationships.	Mature ecosystem and largest community Excellent performance for depth-limited traversals (3-5 hops) Cypher is intuitive and SQL-like for graph queries Rich ecosystem of tools (Neo4j Browser, Bloom, Aura cloud) Strong ACID guarantees and transactions Built-in graph algorithms (via GDS library)	Social networks (friends-of-friends) Recommendation engines (people who bought X also bought Y) Fraud detection (pattern matching in transaction networks) Knowledge graphs and ontologies Network and IT operations management Master data management	Single-instance scales vertically (RAM/CPU). Causal cluster provides HA and read scaling (read replicas). Fabric allows sharding across multiple databases but not transparent. Neo4j Aura offers managed scaling.
Amazon Neptune	Property Graph + RDF Triplestore	Gremlin (property graph) and SPARQL (RDF). Supports open-source Gremlin and SPARQL 1.1.	Distributed storage engine optimized for graph traversals. Data stored on SSD for low-latency. Internal representation abstracts storage details.	Supports both property graph and RDF models Fully managed AWS service (no ops overhead) High availability (multi-AZ replication) ACID transactions within region Integration with AWS ecosystem (IAM, VPC, CloudWatch) Serverless option for variable workloads	Knowledge graphs with RDF/OWL semantics Applications already in AWS ecosystem Security and access control graphs Genomics and life sciences (RDF ontologies) Enterprise knowledge management Fraud detection networks	Serverless or provisioned capacity. Auto-scaling based on workload. Read replicas available. Single region typically; global database for cross-region reads (eventual consistency).
Azure Cosmos DB	Multi-Model (includes Graph API)	Gremlin (via Graph API), also SQL (Document), MongoDB API, Cassandra API, Table API	Atom-record-sequence (ARS) model. Data stored as JSON documents; graph is abstracted layer on top. Globally distributed with multiple consistency models.	Globally distributed with tunable consistency (5 levels) Multiple APIs (multi-model) from single backend Automatic indexing of all properties Turnkey global distribution (single-click region add) SLAs for latency, throughput, availability Serverless and provisioned capacity modes	Global applications needing low-latency everywhere Applications requiring multiple data models Real-time personalization and recommendations IoT device graphs and relationships Social graph in globally distributed app Gaming player networks	Horizontally scalable via partitions. Add regions for geographic distribution. Throughput provisioned per container (database/collection). Automatic partitioning based on partition key.
TigerGraph	Property Graph	GSQL (TigerGraph's Turing-complete query language, SQL-like syntax)	Native parallel graph storage. Distributed architecture with data partitioned by edge type and vertex. Parallel computation across cluster.	Native parallel graph processing (fast multi-hop) GSQL is expressive (supports iterative algorithms) Deep link analytics (10+ hops) efficiently Real-time graph analytics (sub-second) Enterprise features (security, HA, backup) Built-in graph algorithms (PageRank, shortest path, community detection)	Anti-money laundering (transaction chains) Real-time fraud detection (deep traversal) Supply chain network analysis Genomics (gene interaction networks) Complex network risk analysis Enterprise knowledge graphs with deep queries	Distributed cluster scales horizontally. Data partitioned across machines. Query execution parallelized. Single largest native parallel graph DB for deep analytics.
JanusGraph	Property Graph	Gremlin (Apache TinkerPop)	Pluggable storage backends: Cassandra, HBase, ScyllaDB, BerkeleyDB. Indexing via Elasticsearch, Solr, or Lucene.	Massive scalability (billions of vertices/edges) Distributed architecture (Cassandra/HBase backend) Supports full Gremlin syntax (Apache TinkerPop) Elasticsearch integration for full-text search Open source (Apache 2.0 license) Multi-datacenter replication (via storage backend)	Large-scale knowledge graphs (Wikipedia-scale) Fraud detection at massive scale Security analytics (threat intelligence graphs) Bioinformatics networks Use cases requiring Gremlin and massive scale Research and academic projects	Scales horizontally with storage backend. Cassandra backend recommended for production. Requires managing separate storage cluster (operational overhead). Index backend separate for query performance.
Apache Jena	RDF Triplestore	SPARQL 1.1 (query and update), also supports ARQ (query engine), and rule-based inference (RDFS, OWL)	RDF triples (subject-predicate-object). Can use TDB (native store), Fuseki server (HTTP SPARQL endpoint), or external triple stores. Supports named graphs.	Mature RDF/OWL/Semantic Web ecosystem Inference engine (reasoning over ontologies) SPARQL 1.1 standard with property paths, aggregates Open source (Apache 2.0) Integration with OWL APIs and reasoners Multiple storage backends (TDB, Fuseki, external)	Knowledge graphs with RDF/OWL semantics Linked data and semantic web applications Ontology-based reasoning and inference Scientific data integration (biomedical, genomics) Library and cultural heritage metadata Regulatory compliance with rich semantics	TDB scales to hundreds of millions of triples on single machine. Fuseki + TDB for HTTP service. For larger scale, use Jena with external triple store (Blazegraph, Stardog). Sharding RDF non-trivial.
Blazegraph	RDF Triplestore	SPARQL 1.1, also supports full-text search (Elasticsearch) and geospatial	RDF quad store (context support). Uses blobs for RDF literals and integers. High-performance index structures (B+ trees).	High-performance SPARQL query engine Scale to billions of triples Built-in full-text search and geospatial High availability with replication Used by Wikimedia (Wikidata query service) Supports reasoning (RDFS, OWL Horst)	Large-scale knowledge graphs (Wikidata-scale) Semantic search and question answering Life sciences (bio2rdf, chembl) Linked open data applications Enterprise knowledge graphs SPARQL endpoint services	Scale vertically with large RAM/SSD. Horizontal via replication (read scaling). True horizontal sharding limited; best for single large nodes. Used in high-query workloads (Wikidata handles 200M+ queries/day).
ArangoDB (Graph)	Multi-Model (Document + Graph)	AQL (ArangoDB Query Language) supports graph traversals, also supports GraphQL via Foxx	Multi-model: documents (JSON), edges (directed graphs), key-value. RocksDB storage engine. SmartGraphs for distributed graph partitioning.	Single database for multiple models AQL supports JOIN-like operations across models Graph queries with pattern matching Foxx microservices framework (JS in DB) SmartGraphs for multi-hop query scaling Flexible schema with document flexibility	Applications needing graph + document Recommendation engines with rich metadata Fraud detection with document context Content management with relationships User identity and access management graphs Mixed graph/relational queries	Cluster mode with SmartGraphs distributes graph by vertex collections. Local queries within shard; distributed queries across shards. Single instance scales vertically.

Fields

The shape of this dataset, read from the data itself: every column, its type and a real value. Agents can use this to plan a query without fetching the whole document.

Section	Field	Type	Example
databases	`name`	string	Neo4j
databases	`type`	string	Property Graph
databases	`query_language`	string	Cypher (declarative), also supports Gremlin via…
databases	`storage_model`	string	Native graph storage (index-free adjacency). Ea…
databases	`strengths`	array	[6 items]
databases	`ideal_use_cases`	array	[6 items]
databases	`scaling_notes`	string	Single-instance scales vertically (RAM/CPU). Ca…

Fetch the same bytes

The static files contain the same reference rows as the API, with no API rate limit. Live metadata, such as view counts, may differ. Use the API for content negotiation; use the static files for a cacheable snapshot.

curl "https://yjtoon.com/api/dataset/graph-databases-comparison?format=toon"

const res = await fetch(
  "https://yjtoon.com/static-data/dataset/graph-databases-comparison.toon"
);
const toon = await res.text();

Rate limit: 120 requests per minute per IP, no key and no signup. API reference →