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.
Memgraph
- In-memory, C++ architecture
- Sub-millisecond multi-hop traversals
- 1,000+ tx/sec concurrent reads and writes
- Native vector search built in
- Single-query execution model (Atomic GraphRAG)
Neo4j
- Disk-based
- Java/JVM architecture
- Performance degrades on deep traversals under load
- Optimized for lower-velocity read-heavy workloads
- Vector search via separate index
Same query language. Same protocol. Fundamentally different engines.
Want the full technical comparison?
Query performance, dynamic algorithms, deep-path traversals, storage modes, and total cost of ownership — all in one document.
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 with 1,000+ writes per second where disk-based architecture introduces latency through checkpoints, garbage collection, and IO contention.
Deep multi-hop queries under load
Queries that traverse 5, 10, 15+ hops across the graph. In-memory architecture handles these without the latency spikes disk-based systems produce when page cache is under pressure.
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.
CypherBolt protocolAPOCLangChainLlamaIndexMemgraph 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.
Data export/import
Export from Neo4j via CSV or APOC procedures. Import into Memgraph via LOAD CSV or the Memgraph Lab import wizard.
Why teams switch.
“We were able to pivot over to Memgraph as it was more cost effective and came with a minimal learning curve. Plus, my team is half Python, half R so they could easily go and replicate what we needed. ”
David Meza, Branch Chief of People Analytics team at NASA
How Capitec Built a Graph-Powered Fraud Scoring Pipeline for 3.5M+ Daily Cases
How Cedars-Sinai Uses Memgraph for Knowledge-Driven Machine Learning in Alzheimer’s Research
Behind the Missions: How NASA Manages Talent with a People Knowledge Graph
