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 is a single-purpose graph engine but everything else is bolted on and synced by hand. Arango unifies graph, document, vector, and search natively, with Arango AutoGraph and AutoRAG turning raw data into AI-ready knowledge automatically. Query it all in AQL, or plain English with AQLizer. Less to integrate. Less to maintain.
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. | Graph-first, but vector and full-text search are add-ons, and everything past the graph depends on ecosystem integrations | 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 is built for graph querying only, no natural-language layer. | 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, and typically requires manual schema setup for production use, | 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.