Graph databases & knowledge graphs · Memgraph Ltd.

Memgraph

In-memory, Cypher-compatible property-graph database built in C++ for low-latency graph analytics and GraphRAG.

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Memgraph is an in-memory property-graph database written in C++, positioned for workloads that need millisecond graph queries rather than large-scale distributed storage: fraud detection, real-time recommendations, network monitoring and GraphRAG/agentic-AI memory. It speaks openCypher, the same query language family as Neo4j, which makes migration between the two comparatively easy. Memgraph Community is free and source-available; Enterprise pricing scales with total memory used by graph data (and, on the AI Platform tier, only by graph data — vector indexes are unlimited) rather than by query volume or compute, with no separate charges for replicas. Memgraph Cloud offers managed instances from 1GB to 32GB of RAM. It ships built-in graph algorithms (PageRank, community detection, dynamic algorithms that update incrementally) and a MAGE library of extensions.

At a glance

Vendor Memgraph Ltd.
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source No (BSL-1.1)
Best for Teams needing sub-second graph queries or GraphRAG/agent memory who want a Cypher-compatible engine without Neo4j's footprint.

Pricing

Community edition is free and source-available; Enterprise is priced on memory used by graph data, quoted per customer.

Plan Price Notes
Community Free self-hosted, open license, forever free per vendor

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • openCypher query language (Neo4j-compatible)
  • In-memory storage engine for low-latency queries
  • Built-in and MAGE library graph algorithms
  • Dynamic/streaming algorithm updates
  • Native vector indexing alongside graph data
  • Memgraph Cloud managed instances
  • Kafka and Pulsar streaming ingestion

Integrations

Profile last reviewed September 21, 2026

Alternatives

Neo4j Native property-graph database using Cypher, the language behind the new ISO GQL standard, for relationship-heavy analytics. ArangoDB Multi-model database combining graph, document and key/value storage under one engine and one query language, AQL. Dgraph Open-source, natively distributed property-graph database with a GraphQL-derived query language, now stewarded by Hypermode. NebulaGraph Open-source distributed property-graph database using nGQL, built for billion-edge-scale graphs at low latency.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Fraud detection Identifying transactions, claims, or behavior that are likely fraudulent, using rules, statistics, or machine learning. Community detection Identifying groups of nodes in a network that are more densely connected to each other than to the rest of the graph. Graph analytics Analyzing data modeled as nodes and edges to study connections, structure, and influence rather than isolated records. PageRank An algorithm that scores a node's importance by the number and importance of other nodes linking to it.

Related guides

Graph and network analytics, explained How relationships between things become data — the algorithms that find structure in a network and where graph databases fit. How to choose a graph database Decide property graph vs RDF first, then managed vs self-hosted and how the engine scales — the query language follows from those, not the other way round.