Graph Databases Compared: Neo4j, Amazon Neptune, ArangoDB, and TigerGraph
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Not all graph databases are created equal. They differ fundamentally in how they store data, how they query it, and what problems they solve well. Choosing the wrong one — or worse, choosing without a framework — leads to painful migrations, unexpected performance cliffs, and query languages that cannot express your questions.
This article compares the four most prominent graph databases on the market today — Neo4j, Amazon Neptune, ArangoDB, and TigerGraph — across six dimensions: data model, query language, performance and scalability, deployment model, ecosystem and tooling, and developer experience. We close with a decision matrix to match your use case to the right database.
If you are new to graph databases, start with What Are Knowledge Graphs? and Graph Theory for Software Engineers first.
Neo4j is the oldest and most mature graph database, first released in 2007. It pioneered the property graph model — nodes with labels, relationships with types and direction, and key-value properties on both — and remains the market leader by adoption.
Neptune is a fully managed graph database service on AWS, launched in 2018. It is unique in supporting two graph models simultaneously: property graph (via Gremlin or openCypher) and RDF (via SPARQL).
ArangoDB is a multi-model database that combines graph, document, key-value, and search in a single engine. First released in 2014, it is the most flexible option on this list.
FOR v, e, p IN 1..3 OUTBOUND 'users/1' GRAPH 'social'TigerGraph is the youngest entrant, founded in 2017, designed from the ground up for deep-link analytics — traversing 10+ hops on billion-node graphs in real time.
| Dimension | Neo4j | Amazon Neptune | ArangoDB | TigerGraph |
|---|---|---|---|---|
| Data model | Property graph | Property graph + RDF | Multi-model (doc, graph, KV, search) | Property graph |
| Query language | Cypher | Gremlin, openCypher, SPARQL | AQL | GSQL |
| Native graph storage | Yes (index-free adjacency) | No (shared-volume) | No (document store + edge indexes) | Yes (parallel adjacency) |
| ACID compliance | Full ACID | Full ACID | Full ACID (single server) | Full ACID |
| Deep traversal (10+ hops) | Excellent | Moderate | Moderate | Best-in-class |
| Cloud managed | AuraDB (multi-cloud) | Yes (AWS-only) | ArangoGraph | TigerGraph Cloud |
| Self-hosted | Yes (Community/Enterprise) | No | Yes (Community/Enterprise) | Yes |
| Open source | Community edition | No (proprietary) | Community edition | Community edition (limited) |
| Enterprise features | Clustering, security, multi-DC | Multi-AZ, IAM integration | SmartGraphs, Datacenter-to-DC | GraphStudio UI, multi-graph |
| Best for | General graph apps, GraphRAG, startups | AWS-native stacks, RDF/Linked Data | Multi-model apps, polyglot persistence | Deep-link analytics, fraud, real-time |
The most significant architectural difference is how each database handles deep traversals — following 5, 10, or 20 hops from a starting node.
Neo4j's index-free adjacency means each hop is a pointer dereference, not a lookup. A 10-hop traversal touches exactly the nodes and relationships in the traversed path, regardless of total graph size. This makes Neo4j predictable at depth.
TigerGraph takes this further with parallel adjacency and compiled query execution. A GSQL query that traverses 15 hops across a billion-node graph can return in seconds because the query planner compiles the traversal into C++ and executes it across all available cores.
Neptune and ArangoDB use index-based lookups for each traversal step. For small to medium graphs (under 10 million nodes), the difference is negligible. At scale, index lookups compound with each hop, making deep traversals exponentially more expensive than native-storage alternatives.
Bottom line: If your workload is mostly 1-3 hop lookups (common in GraphRAG and content graphs), any of the four works well. If you need 10+ hop pattern matching (fraud rings, supply chain, bioinformatics), TigerGraph or Neo4j are the serious contenders.
The query language is often the deciding factor. Here is the same query — find a user's friends-of-friends who purchased a product in the last 30 days — in each language:
MATCH (u:User {id: $userId})-[:FRIENDS_WITH]->()-[:FRIENDS_WITH]->(fof:User)
MATCH (fof)-[:PURCHASED]->(p:Product)
WHERE p.purchasedAt > datetime() - duration('P30D')
RETURN fof.name, collect(DISTINCT p.name) AS products
CREATE QUERY friendsOfFriendsPurchases(STRING userId) FOR GRAPH social_graph {
SumAccum<INT> @productCount;
Start = {User.*};
// Find friends-of-friends in two hops
FOF = SELECT t
FROM Start:s -(FRIENDS_WITH:e)- :t
WHERE s.id == userId
ACCUM t.@tag = 1;
FOF2 = SELECT t
FROM FOF:s -(FRIENDS_WITH:e)- :t
WHERE s != t
ACCUM t.@tag = 1;
// Join to purchases within 30 days
Result = SELECT p
FROM FOF2:fof -(:e)- Product:p
WHERE p.purchasedAt > datetime_add(now(), -30, "DAY")
ACCUM fof.@productCount += 1;
PRINT Result;
}
FOR u IN users
FILTER u._key == @userId
FOR v, e, p IN 2..2 OUTBOUND u GRAPH 'social'
// v is the friend-of-friend at depth 2
FOR purchase IN purchases
FILTER purchase._from == v._id
AND purchase.date > DATE_SUBTRACT(DATE_NOW(), 30, 'day')
RETURN {
friend: v.name,
products: UNIQUE(purchase.productName)
}
g.V().has('User', 'id', userId).
repeat(out('FRIENDS_WITH')).times(2).dedup().
out('PURCHASED').
has('purchasedAt', gte(datetime.now().minus(Duration.ofDays(30)))).
group().
by('name').
by('productName').fold()
Each language reflects its database's design philosophy. Cypher is the most readable for pattern matching. GSQL is the most powerful for complex algorithms. AQL blends naturally with document queries. Gremlin is the most flexible but the hardest to read.
Here is a decision matrix for choosing a graph database based on your primary use case:
| Use Case | Recommended | Why |
|---|---|---|
| GraphRAG / AI applications | Neo4j | Largest ecosystem, Cypher is easiest for pattern matching, AuraDB for managed deployment, native GraphRAG integrations |
| Fraud detection (deep link) | TigerGraph | Best deep-traversal performance, GSQL accumulators for aggregating across rings, real-time ingestion |
| AWS-native stack / RDF data | Amazon Neptune | Tight IAM integration, SPARQL for W3C-compliant RDF, serverless scalability |
| Polyglot application | ArangoDB | Single database for documents, graphs, and search reduces operational complexity |
| Startup / limited budget | Neo4j Community | Free, mature, best documentation and community support |
| Knowledge graph with ontologies | Neo4j or Neptune | Neptune for RDF/OWL standards; Neo4j for broader tooling ecosystem |
There is no universal "best" graph database — only the best fit for your data shape, query patterns, and operational constraints.
For a deeper look at the Neo4j ecosystem, see our guides on Graph Data Modeling Patterns, Cypher Query Optimisation, and Neo4j Database Adapters Compared. For a survey of GraphRAG approaches built on these databases, see GraphRAG Variants Compared (2026).