Company Profile · Graph Data & AI
The tiger that hunts in graphs - Redwood City, CA
TigerGraph
The database that refuses to think in rows. It thinks in relationships.
The database that refuses to think in rows. It thinks in relationships.
Four credit cards, three phone numbers, one shared shipping address, and a device that has logged in from two continents in the same hour. In a spreadsheet, these look like unrelated rows. In a relational database, joining them takes a query so expensive the answer arrives after the money is gone. TigerGraph was built for exactly this moment - the moment when the truth is not in the data points, but in how they connect.
For more than a decade, TigerGraph has sold one stubborn idea: that the most valuable questions in business are relationship questions. Who is connected to whom? What flows through that connection, and how fast? The company turned that idea into a native parallel graph engine that can hold trillions of edges and walk across them in real time - and then, quietly, found itself sitting on exactly the infrastructure the AI era was about to demand.
Most databases store data in tables and chase connections with joins. TigerGraph stores the connections themselves as first-class citizens - and processes them in parallel across a distributed cluster. The result is a platform that answers deep, many-hop questions ("show me everyone three transactions away from this account") at a speed that makes them usable in production, not just in research.
TigerGraph was founded in 2012 as GraphSQL by Dr. Yu Xu, a computer scientist who had spent years where big data actually breaks: he was Teradata's Hadoop architect and worked on Twitter's data infrastructure when "massive scale" stopped being a slogan and became a daily emergency. He holds 26 patents in parallel data management. Alongside co-founders Mingxi Wu, Like Gao, Ruoming Jin and Li Chen, he bet that relational databases would never be the right tool for interconnected data - so he built a new one from the storage layer up.
The company stayed in stealth for five years, emerging in September 2017 as TigerGraph with its first major funding and a new name with teeth. The choice of a tiger was not subtle. Neither was the pitch: native parallel graph, designed to do in real time what other systems could only do overnight.
"Graphs are built to solve relationship-focused problems."- Rajeev Shrivastava, CEO, TigerGraph
TigerGraph is less a single product than a stack - a database, a language, a visual studio, and a data-science library that turns connected data into answers.
The native parallel graph database and analytics engine, built for hundreds of terabytes and HTAP workloads that mix transactions with analytics.
The cloud-native managed service launched in 2025, with ready-made kits for fraud and customer insight and connectors to Snowflake and Apache Iceberg.
A SQL-like, Turing-complete query language for expressive, parallel graph traversal - the difference between asking and computing.
A point-and-click UI to model schemas, load data, and explore the graph without writing a line of code.
Built-in graph algorithms and graph-neural-network tooling for analytics, recommendations, and feature engineering.
A free tier for developers, students and researchers to learn graph thinking before they buy it.
When the value hides in connections, the customer list gets eclectic. TigerGraph shows up in fraud detection and anti-money-laundering at financial institutions, customer-360 and recommendations in retail, supply-chain and IoT analysis in manufacturing, and entity resolution across healthcare and energy.
TigerGraph has pulled in roughly $194M across its life - climbing from a quiet seed to a headline-grabbing Series C led by Tiger Global in 2021, which reportedly valued the company near $611M. A further, undisclosed round followed in 2025 as the company repositioned graph as core AI infrastructure.
Large language models are fluent but forgetful, and they struggle to explain how they reached an answer. Graphs are the opposite: structured, traceable, relationship-aware. TigerGraph's recent pitch - GraphRAG and "agentic" retrieval - is that a graph can be the verifiable memory and reasoning layer underneath AI agents. The infrastructure it spent a decade building for fraud analysts turns out to be exactly what an AI agent needs to avoid making things up.
TigerGraph competes most directly with Neo4j, Amazon Neptune, Memgraph and ArangoDB, and increasingly against relational and vector databases reaching for connected-data and AI-retrieval workloads. Its differentiator has always been the same: native parallelism designed for the very large end of the scale, where simpler graph stores start to strain.
The four cards, the three numbers, the shared address, the impossible login - they were never really separate rows. They were a shape, and the shape was a graph. Somewhere a system walked the connections, scored the pattern, and flagged the account before the transaction cleared. The analyst did not write a heroic query. The graph already knew how everything was connected.
That is the quiet trick TigerGraph has been refining since 2012: turning "how is all of this related?" from a research project into something you can ask, and answer, in real time. The company has weathered restructuring, a revolving CEO door, and a market that took years to understand graphs. Now the AI wave is asking precisely the question the tiger was built to answer - and for once, the timing is on its side.