Memgraph
Memgraph is a real-time graph database engine headquartered in London, founded in 2016 by Marko Budiselić and Dominik Tomić.
Profile
Memgraph provides an in-memory, ACID-compliant graph database engine designed for real-time AI context retrieval and graph analytics.
Memgraph is a real-time graph database engine headquartered in London, founded in 2016 by Marko Budiselić and Dominik Tomić. The company positions its in-memory, ACID-compliant graph database as a reasoning engine for AI workloads, particularly GraphRAG, AI memory, and agentic AI systems. Its core pitch is that graph traversals can provide structured, multi-hop context that vector similarity search alone cannot, with sub-millisecond query times and support for graph sizes from 100 GB to 4 TB.
Memgraph’s website lists logos of major organizations including NASA, Capitec Bank, Microchip Technology, BNP Paribas, IBM, Siemens, Johnson & Johnson, Stripe, Netflix, Tencent, and AMD, though the nature and depth of these relationships are not publicly detailed. The company has not disclosed its revenue, headcount, or funding rounds in the provided dossier; the search for recent funding news returned only a TechCrunch article listing 17 US-based AI companies that raised over $100M in 2026, which did not mention Memgraph. The dossier also contained several unrelated earnings reports (Medtronic, Agilent, McGraw Hill, Dell Technologies) that were mislabeled as Memgraph results.
As of mid-2026, Memgraph is actively marketing its graph engine for AI context, but lacks public financial disclosures, funding announcements, or independent reporting on its business performance. The company’s open-source community edition is available on GitHub, and it competes with Neo4j, TigerGraph, and ArangoDB in the graph database space, while also vying for a slice of the GraphRAG market against startups like Kùzu and FalkorDB.
Who buys this
- Financial services firms needing real-time fraud detection and risk analysis
- AI/ML teams building GraphRAG, agentic AI, or AI memory systems
- Enterprise data engineering teams requiring low-latency graph analytics on streaming data
- Cybersecurity vendors using graph-based threat detection and investigation
- Healthcare and life sciences organizations for connected data analysis (e.g., patient journeys, drug discovery)
Strengths and what to watch
Strengths
- Sub-millisecond graph traversal performance for real-time AI context retrieval, as claimed on the company website
- ACID compliance with persistence, enabling transactional workloads alongside analytics on the same engine
- Open-source community edition with a GitHub presence, lowering adoption barriers for developers
Watch for
- No public revenue, funding, or headcount data disclosed; the company's financial health and scale are opaque
- Customer logos on the website (e.g., NASA, Netflix, Stripe) are unverified; no case studies or named references in independent reporting confirm these relationships
- Intense competition from established graph databases (Neo4j, TigerGraph) and newer GraphRAG-focused startups, with Memgraph's market share unclear
Key Information
- Industry
- Graph DBs
- Founded
- 2016
- Headquarters
- London
Frequently Asked Questions
What is Memgraph used for?
Memgraph is an in-memory, ACID-compliant graph database engine designed for real-time AI context retrieval and graph analytics. It supports GraphRAG, AI memory, agentic AI systems, fraud detection, risk analysis, and low-latency graph analytics on streaming data.
How fast is Memgraph for graph queries?
Memgraph claims sub-millisecond graph traversal performance for real-time AI context retrieval. This speed enables structured, multi-hop context that vector similarity search alone cannot provide, supporting graph sizes from 100 GB to 4 TB according to the company website.
Is Memgraph open source?
Yes, Memgraph offers an open-source community edition available on GitHub. This lowers adoption barriers for developers who want to try the in-memory, ACID-compliant graph database engine for real-time analytics or AI workloads before committing to a commercial license.
Who are Memgraph's main competitors?
Memgraph competes with established graph databases like Neo4j and TigerGraph, as well as newer GraphRAG-focused startups such as Kùzu and FalkorDB. The company also vies for market share in the broader graph database space against ArangoDB.
Does Memgraph have any notable customers?
Memgraph's website lists logos of major organizations including NASA, Netflix, Stripe, and IBM. However, these relationships are unverified, and no independent case studies or named references confirm the depth of these partnerships as of mid-2026.
Has Memgraph disclosed its funding or revenue?
No, Memgraph has not publicly disclosed its revenue, headcount, or funding rounds. A search for recent funding news found no mention of the company among AI firms that raised over $100M in 2026, leaving its financial health and scale opaque.
Sources
- memgraph.com — Company product claims, customer logos, use cases (GraphRAG, AI Memory, Agentic AI), and technical specs (sub-ms traversals, 100 GB–4 TB graph sizes, ACID compliance)
- techcrunch.com — Context on AI funding landscape in 2026; Memgraph is not listed among companies that raised $100M+
- techcrunch.com — General TechCrunch homepage; no Memgraph-specific articles found in the dossier
- news.medtronic.com — Irrelevant Medtronic earnings; included in dossier but not related to Memgraph