Memgraph vs Neo4j
Both are property graph databases. Both support Cypher. The architecture underneath is fundamentally different, and for real-time AI workloads, that matters.
Different architecture. Different performance.
Same query language. Same protocol. Fundamentally different engines.
Go deeper on the comparison.
The cheat sheet for the summary. The whitepaper for query performance, deep-path traversals, dynamic algorithms, storage modes and total cost of ownership.
Where Memgraph is the better choice.
For AI workloads
Speed in the LLM critical path
GraphRAG, AI memory, agentic workflows, when graph traversals sit in the critical path of an LLM pipeline, in-memory C++ architecture delivers sub-millisecond response times that disk-based systems cannot match under load.
Atomic GraphRAG
Memgraph executes the entire GraphRAG retrieval pipeline - search, expansion, ranking, prompt assembly, as a single atomic Cypher query. No multi-system orchestration, no distributed pipeline to debug.
Native vector search
Similarity and structure in a single engine with 85% less memory for vector storage (Single Store Vector Index). No separate vector index or external vector database required.
For real-time workloads
High-velocity write environments
Transaction monitoring, streaming data, real-time fraud detection — workloads where disk-based architecture introduces latency through checkpoints, garbage collection and IO contention.
Analytics that stay current
MAGE runs against the live graph, and dynamic algorithm variants recompute only the affected region when a write lands. GDS runs in batch over projections, so results are stale by design until the next run.
Weighted paths inside Cypher
WSHORTEST and ALLSHORTEST run inside the pattern itself, with filter lambdas and accumulators. In Neo4j, the moment a path carries weight you leave Cypher for GDS or APOC.
For both
Simple, predictable pricing
All-inclusive pricing that scales with memory capacity. No per-query charges, no compute fees, no charges for replicas or algorithms.
Migration is straightforward.
Memgraph speaks the same language. Your existing queries, drivers, and application code work with minimal changes.
Cypher migration
Most Cypher queries work without changes. Review our compatibility matrix for edge cases and syntax differences.
Migration guideData export/import
Export from Neo4j via CSV or APOC procedures, import into Memgraph via LOAD CSV or the Memgraph Lab import wizard.
Data migration docs
CypherBolt protocolAPOCLangChainLlamaindexWhat teams are building with Memgraph
“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.”
