Arango vs. Neo4j
Build AI Faster.
With Less Engineering.
Both are proven graph databases. The difference is what’s built in and what you’re left building yourself.
Neo4j scales reads by replicating the graph, but writes stay on one server. Arango shards natively with writes and multi-hop traversals that scale together, while Arango AutoGraph builds the knowledge graph automatically.
Scale without re-platforming. Build without the manual work.
How Arango and Neo4j compare
It comes down to architecture.
| Capability | Arango Contextual Data Platform | Neo4j | Advantage |
|---|---|---|---|
| Data foundation What the AI data layer is built on | Graph-native to the core: documents, vectors, key-value, and search all run in the same engine, not bolted on. | Neo4j adds vector and full-text retrieval to its property-graph model; both are native, built-in index types, not add-ons. | Arango |
| Unified querying How teams work across data models | AQL enables queries across graph, document, search, and multimodel workflows. AQLizer turns natural language into queries. | Cypher has no built-in natural-language layer. NL access exists only via a Labs research project (Text2Cypher), a licensed NeoDash extension, or MCP tooling. | Arango |
| Graph creation Work required before AI can use the graph | Arango AutoGraph is built into the platform, it automatically builds and maintains governed knowledge graphs | Neo4j’s graph-building tool is a separate open-source Labs project, focused on unstructured text; its officially-supported GraphRAG package can extract schema automatically. | Arango |
| Scale & distributed traversal How the graph performs as data and query load grow | SmartGraphs shard natively, co-locating connected data so multi-hop traversals scale with the cluster. | Clustering replicates the graph for reads/HA; a single primary handles writes. Sharding needs a separate composite-database layer. | Arango |
| GraphRAG / retrieval How AI retrieves relationship-aware context | AutoGraph structures each domain: graph or vector. AutoRAG auto-selects retrieval per query, vector to multi-hop GraphRAG, no pipeline required. | Neo4j provides a retriever library (vector, hybrid, text-to-Cypher) that developers choose and configure by hand for each use case. | Arango |
| Graph analytics Algorithms and analysis | GraphML and AI capabilities for graph-powered AI workflows. | Graph Data Science is a mature, widely recognized analytics library. | Neo4j |
| Visualization Graph exploration experience | Graph visualization capabilities included. | Bloom is a strong, well-known graph visualization experience. | Neo4j |
| Consolidation How many moving parts teams manage | One trusted data foundation for enterprise AI. | Often extended with additional databases, frameworks, or services as AI requirements expand. | Arango |
| Deployment flexibility Where enterprise AI can run | Cloud, hybrid, on-prem, edge, and air-gapped options. | Cloud and self-managed deployment options. | Arango |
What teams use Arango for:
Consolidating the stack
01
Everything in one platform.
Graph, vector, document, key-value and search live in one system. No stitching separate technologies together with pipelines and ETLs, each carrying its own operational and observability tools.
02
One query language.
document, search, and multimodel workflows, all backed by ACID transactions, security, and governance in the same platform.
03
Runs where you need it.
Cloud, hybrid, on-prem, edge, and air-gapped. One architecture across all of them.
What teams use Arango for:
Building AI on enterprise data
The graph builds itself
Hand-built knowledge graphs take months of modeling and mapping. Arango AutoGraph builds them automatically from your enterprise data.
Retrieval without the pipeline
GraphRAG pipelines are hard to build. Arango AutoRAG handles relationship-aware retrieval across graph, documents, vectors, and search.
Ready when AI is
Arango AutoGraph, AutoRAG, GraphRAG, GraphML, and MCP ship with the platform. If AI isn’t your workload yet, it’s the same database when it is.
See Arango AutoGraph build a graph from your data.
What teams are building with Arango
Digital twins, operational systems, real-time state, and production AI, all running where connected data, performance and scale decide whether the system holds up.
Orange Telecom
51M
Digital twins using graph, key-value, and geospatial data with ACID transactions at carrier scale.
HPE
10k+
Transactions per second with graph and search for supercomputing-scale operational workloads.
Zscaler
Live
Incident-resolution and support AI running in production on the Contextual Data Platform.
Cloud Imperium Games
Scale
Horizontal scale for real-time game state in a massive persistent universe.
Scale beyond billions of nodes, edges, and relationships.
Stop stitching databases together.
Tell us what you’re building. We’ll show you how much engineering comes off the table.