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.

CYPHER · EXPANSION QUERY

MATCH (u:User)-[:KNOWS]->(n)
WHERE u.id = "user_001"
RETURN n.name, n.id

Different architecture. Different performance.

Same query language. Same protocol. Fundamentally different engines.

	Memgraph	Neo4j
Architecture	In-memory, C++	Disk-based, Java/JVM
Traversal Latency	Sub-millisecond multi-hop	Degrades on deep traversals under load
Write Throughput	1,000+ tx/sec concurrent reads & writes	Optimized for lower-velocity read workloads
Vector Search	Native, built-in - 85% less memory	Separate index required

Want the full technical comparison?

Query performance, dynamic algorithms, deep-path traversals, storage modes, and total cost of ownership, all in one document.

Download the whitepaper

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.

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 guide Data 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

Cypher

Bolt protocol

APOC

LangChain

Llamaindex

What teams are building with Memgraph

NASACedars-SinaiCapitec Bank

"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."

TALENT MANAGEMENT Behind the Missions: How NASA Manages Talent with a People Knowledge Graph Read case study HEALTHCARE AI How Cedars-Sinai Uses Memgraph for Knowledge-Driven Machine Learning in Alzheimer’s Research Read case study FRAUD DETECTION How Capitec Built a Graph-Powered Fraud Scoring Pipeline for 3.5M+ Daily Cases Read case study