Memgraph provides an in‑memory graph database that combines ACID‑compliant transactions with sub‑millisecond graph analytics in a single engine. It supports AI workloads such as GraphRAG and multi‑hop reasoning via atomic Cypher queries, offers hybrid storage options, built‑in vector search, and real‑time triggers, and can be deployed on‑premise, in containers, or as a managed cloud service.
Funding
$18.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CCIVIQIE+1Founders
Product
Problem
Many organizations require real-time analysis of highly connected data for AI agents, fraud detection, network monitoring, and operational decision‑making, but existing graph databases either suffer from high latency, limited scalability, or require separate systems for transactional and analytical workloads.
Solution
Memgraph delivers an in‑memory graph database that combines ACID‑compliant transactional processing with ultra‑low‑latency graph analytics in a single engine. The platform supports sub‑millisecond traversals at thousands of transactions per second, enabling AI workloads such as GraphRAG and multi‑hop reasoning to run as atomic Cypher queries. Its hybrid storage modes let users choose between pure in‑memory, persisted, or disk‑backed configurations to match workload demands while maintaining data durability. Built‑in MAGE algorithms, vector search, and integrations with LangChain and LlamaIndex provide out‑of‑the‑box support for AI‑driven use cases. Memgraph can be deployed on‑premise, in containers, or as a fully managed cloud service, offering seamless scaling and high availability without data duplication.
Target Audience
Primary customers are data‑intensive enterprises and AI developers who need millisecond‑scale graph analytics for fraud detection, infrastructure monitoring, knowledge‑driven machine learning, and real‑time decision engines.
Features
- In‑memory architecture with ACID transactions and optional on‑disk persistence for fast, reliable reads and writes
- Sub‑millisecond graph traversals supporting >1,000 tx/sec and graphs up to several terabytes
- Pre‑optimized graph algorithms (BFS, DFS, weighted shortest path, all‑pairs shortest paths) and MAGE library for custom analytics
- Hybrid storage modes (in‑memory, persisted, disk‑backed) to balance performance and capacity needs
- Real‑time triggers and dynamic algorithm updates that recalculate only affected portions of the graph
- Native Cypher query language, Python client, and compatibility layers for Neo4j, Kafka, Pulsar, and Redpanda
- Integrated vector search and long‑term memory graph structures (semantic, episodic, procedural) for AI reasoning pipelines
- High‑availability features: automatic failover, replication, multi‑tenancy, role‑based access control, SSO, encryption, and Prometheus monitoring