Graph databases & knowledge graphs · ArangoDB Inc. (operating as Arango)

ArangoDB

Multi-model database combining graph, document and key/value storage under one engine and one query language, AQL.

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ArangoDB is a multi-model database: graph, document and key/value data live in the same storage engine, and a single query language — AQL (ArangoDB Query Language), not Cypher or Gremlin — can mix graph traversals with document filters and joins in one statement. That lets teams avoid running a separate document store alongside their graph database for the same application. SmartGraphs and full sharding support horizontal scaling for the Enterprise edition; the Community edition is free for self-hosted, non-production use but is not permissively open source. ArangoGraph is the managed cloud offering. The company has repositioned itself as Arango, layering an AI-oriented platform (AutoGraph automatic knowledge-graph construction, GraphRAG retrieval) on top of the core ArangoDB engine, which remains the underlying database.

At a glance

Vendor ArangoDB Inc. (operating as Arango)
Pricing model Quote only
Free tier Yes
Deployment Cloud, Self-hosted
Open source No (BSL-1.1)
Best for Teams that want graph traversals and document queries against the same dataset without running two databases.

Pricing

Community edition is free for non-production use; ArangoGraph cloud and Enterprise self-managed require contacting sales for a quote.

Pricing has not been verified yet — see the vendor's site.

Features

  • AQL: one query language across graph, document and key/value data
  • SmartGraphs for horizontally sharded graph workloads
  • Native full-text and vector search
  • AutoGraph automatic knowledge-graph construction
  • GraphRAG-style retrieval for AI applications
  • ArangoGraph managed cloud (multi-cloud)
  • ACID transactions across collections

Integrations

Profile last reviewed September 21, 2026

Alternatives

Neo4j Native property-graph database using Cypher, the language behind the new ISO GQL standard, for relationship-heavy analytics. TigerGraph Massively parallel native graph database with its own GSQL language, built for deep multi-hop analytics at scale. Dgraph Open-source, natively distributed property-graph database with a GraphQL-derived query language, now stewarded by Hypermode. NebulaGraph Open-source distributed property-graph database using nGQL, built for billion-edge-scale graphs at low latency.

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Terms to know

Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Semantic search Search that matches results to a query's meaning rather than its exact keywords, using vector similarity between embeddings.

Related guides

Graph and network analytics, explained How relationships between things become data — the algorithms that find structure in a network and where graph databases fit. How to choose a graph database Decide property graph vs RDF first, then managed vs self-hosted and how the engine scales — the query language follows from those, not the other way round.