Graph Databases Compared: Neo4j, Amazon Neptune, ArangoDB, and TigerGraph

The Graph Database Landscape

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.

The Contenders

Neo4j

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.

• Query language: Cypher (declarative, pattern-matching)
• Storage: Native graph storage with index-free adjacency — each node maintains direct pointers to its neighbours, so traversals are proportional to the subgraph visited, not the total graph size
• Editions: Community (open source, single-server), AuraDB (fully managed cloud), Enterprise (clustering, security, multi-datacenter)
• Notable: Strongest developer ecosystem, largest community, richest library of procedures via APOC and the Graph Data Science library

Amazon Neptune

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).

• Query languages: Gremlin (property graph traversal), openCypher (partial), SPARQL (RDF)
• Storage: Shared-volume architecture built on AWS Nitro — storage is decoupled from compute, enabling fast recovery and multi-AZ replication
• Deployment: Managed-only — no self-hosted option. Tightly integrated with AWS (IAM, CloudWatch, S3 bulk export, KMS encryption)
• Notable: Best choice for AWS-native stacks; tRIN (temporal RDF Inference) for time-aware RDF reasoning; supports the W3C RDF stack for interoperability

ArangoDB

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.

• Query language: AQL (ArangoDB Query Language) — SQL-like syntax with native graph traversal support using FOR v, e, p IN 1..3 OUTBOUND 'users/1' GRAPH 'social'
• Storage: Document-based with edge collections for relationships; graph traversals use skip-list indexes on edge collections
• Deployment: Self-hosted (Community or Enterprise), ArangoDB Cloud (managed), or ArangoGraph (fully managed platform)
• Notable: Multi-model means you can mix graph traversals with document queries, full-text search, and geospatial in a single query; SmartGraphs for sharded graph workloads across clusters

TigerGraph

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.

• Query language: GSQL — a Turing-complete, procedural language with built-in graph traversal primitives. Unlike Cypher or Gremlin, GSQL lets you write loops, conditionals, and accumulators directly in the query
• Storage: Native parallel graph processing with a Multi-Instance Storage Engine (MISE); each node is stored with its adjacency list, and queries compile to parallel C++ execution plans
• Deployment: Self-hosted or TigerGraph Cloud (managed, with free tier)
• Notable: Fastest at deep-link traversal and graph analytics; used for fraud detection at scale; GSQL is more verbose but more expressive for complex graph algorithms

Side-by-Side Comparison

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

Deep-Link Traversal Performance

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.

Query Language Comparison

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:

Cypher (Neo4j)

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

GSQL (TigerGraph)

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;
}

AQL (ArangoDB)

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)
    }

Gremlin (Neptune)

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.

Decision Framework

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

Closing Thoughts

There is no universal "best" graph database — only the best fit for your data shape, query patterns, and operational constraints.

• Neo4j remains the safest default for most teams. Its maturity, tooling, and community are unmatched.
• TigerGraph wins on raw deep-link performance but demands more from your team.
• Neptune is purpose-built for the AWS ecosystem and RDF workloads.
• ArangoDB is the polyglot's choice when graph is one of several data models you need.

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).