Memgraph
Benchmark

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.

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
GraphRAG
Single atomic Cypher query
Multi-system orchestration
Pricing
Memory capacity only, all-inclusive
Per-query + compute + replicas
Want the full technical comparison?

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

Memgraph wins

Where Memgraph is the better choice.

For AI workloads

01
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.

02
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.

03
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

01
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.

02
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

01
Simple, predictable pricing

All-inclusive pricing that scales with memory capacity. No per-query charges, no compute fees, no charges for replicas or algorithms.

Trusted in production

What 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.”
David MezaNASA
Memgraph helped us capture the higher order relationships between genes, drugs, and clinical evidence to surface treatment possibilities like Temazepam and Ibuprofen.”
Jason H. MooreCedars-Sinai
“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.
Derick SchmidtCapitec Bank
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