Memgraph vs ArangoDB
A graph-first engine against a multi-model platform. When traversal is the workload, not one model among five, the architecture underneath decides the latency floor.
Graph-first focus. Multi-model breadth.
Go deeper on the comparison.
The one-pager for the summary. The whitepaper for engine architecture, memory efficiency, query expressivity, extensibility, GraphRAG execution and cost.
Where Memgraph is the better choice.
For AI workloads
Atomic GraphRAG
The entire retrieval pipeline, embedding call, vector search, graph expansion, re-ranking, collapses into one sustained Cypher query inside the database. ArangoDB combines vector search and traversal through AQL; the orchestration still lives above the database.
Embeddings generated in-query
CALL embeddings.text( ... ) runs in the same Cypher flow as retrieval, so how we embed and how we retrieve stay one artifact. ArangoDB requires embeddings stored in documents, calculated via GraphML on Arango Managed Platform or external tooling.
Vector storage that doesn't duplicate
The Single Store Vector Index keeps each vector once with a reference in the property store, roughly 85% less overhead. Enabling vector indexing in ArangoDB permanently changes RocksDB data management for the deployment and cannot be disabled later.
For traversal-heavy workloads
Graph-native path logic
WSHORTEST, ALLSHORTEST expansions, all-shortest paths, traversal-time lambda filters and accumulators are first-class Cypher primitives. AQL traversals are capable but more verbose, without the same filter ergonomics.
Extensibility that survives the roadmap
Query modules in C, C++, Python and Rust read and modify the graph inside a transaction. ArangoDB's own data-plane UDFs run on Coordinators only, without full index visibility, and mark the feature for removal in 4.0.
Analytics in the core database
MAGE and dynamic algorithms run where the data already is; ArangoDB 3.12 removed Pregel from core, shifting algorithm workloads to a separate Graph Analytics Engine that requires loading graph data into its own memory.
For both
Scale up before scaling out
In-memory execution, compact representation and Parallel Runtime extract more from one high-core machine. High availability needs one main, one replica and a three-node coordinator quorum, cleaner than Agents + Coordinators + DB-Servers.
Cypher, not a proprietary dialect.
Memgraph uses Cypher and Bolt, so Neo4j-compatible drivers, integrations and existing team skills carry over. AQL is capable, but portable only within ArangoDB, which matters more now that ArangoDB 3.12 moved to BSL 1.1 with a 100 GiB Community Production limit.
CypherBolt protocolSQL-alignedLangChainLlamaindexMCPHow to move data
Migration modules
Load directly from relational databases, graph databases, files, streams, RPC sources and object storage through native classes and MAGE migration modules.
Migration docsQuery it where it lives
Where copying is undesirable, Memgraph Zero provides federated GQL access across supported backends without ETL.
Memgraph ZeroBuilt for graphs that never stop changing.
“Memgraph gave us a more cost-effective way to build on the graph capabilities we already knew, with a minimal learning curve for our Python and R team.”
“Memgraph helped us capture the higher order relationships between genes, drugs, and clinical evidence to surface treatment possibilities like Temazepam and Ibuprofen.”
“Being in memory, Memgraph is fast and really performant. We score 3.5 million-plus clients daily, and the entire infrastructure runs start to end in two hours on average.”
Behind the Missions: How NASA Manages Talent with a People Knowledge Graph
Read case studyHEALTHCARE AIHow Cedars-Sinai Uses Memgraph for Knowledge-Driven Machine Learning in Alzheimer’s Research
Read case studyFRAUD DETECTIONHow Capitec Built a Graph-Powered Fraud Scoring Pipeline for 3.5M+ Daily Cases
Read case studySee how Memgraph compares for your workload.
ArangoDB fits when one application genuinely needs graph, document, key-value, search and vector in the same database. Memgraph fits when graph traversal is the workload.
