A growing number of enterprises are adopting data models — abstract models that organize data elements and standardize how they relate to each other. But as the explosion of data analytics and artificial intelligence leads organizations to expect more from data models, many of the old paradigms are proving difficult to manage — and extremely fragile.
At least, this is what engineers and entrepreneurs Artyom Keydunov and Pavel Tiunov noticed in their work. While at Statsbot, a data analytics startup the pair co-founded in 2016, Keydunov and Tiunov often consulted with organizations struggling to get their “data house” in order.
Cube launched as an open source project in 2019, offering what Keydunov describes as a “universal semantic layer” for organizational data that can feed into databases, business intelligence (BI) tools, and even AI-powered chatbots. Now, five years later, Keydunov and Tiunov have a real business on their hands, having launched a Cube-based subscription service — Cube Cloud — that adds automated workflows and enterprise-focused governance and development tools.
“There is no shortage of data,” Keydunov told TechCrunch. “And the demand for data continues to grow among employees, partners and customers, who are motivated by the idea that data-driven decisions lead to improved operational efficiency, increased customer satisfaction and competitive advantage. Technologies such as artificial intelligence, machine learning, the Internet of Things and blockchain are reshaping the data landscape and revolutionizing the way organizations collect, process and derive value from data. It’s not just people who need data. now machines need data too.”
In addition to the challenges of data modeling, research shows that relatively few organizations are achieving even basic success in deriving value from their data. A 2022 Gartner voting of data analytics leaders found that less than half believe their teams are effective in delivering value to their employers. This is despite the fact that, according to the same survey, companies spend an average of more than $5 million on data management, governance and analytics initiatives.
So what should we do? For Keydunov and Tiunov, the answer was to try to create a platform that could serve as a unified source of truth for all of a company’s data and metrics.
“Cube Cloud is a universal semantic layer that is an independent, but interoperable, part of the modern data stack that sits between your data sources and data consumers,” Keydunov said. “The universal semantic layer enables every data endpoint – whether it’s BI tools, embedded analytics, or AI agents and chatbots – to work with the same semantics and underlying data.
Companies use Cube Cloud to build this semantic layer and connect it to their various applications and utilities, using role-based access controls, data caching, single sign-on, and scalable infrastructure as they require it. Enterprise-level customers have access to consultants who can train their data engineers to work with Cube Cloud and offer on-demand support, as well as build the initial Cube Cloud instance — either on Cube-owned servers or facilities, adapted to the business.
“Cube Cloud automatically adjusts queries and inserts the appropriate security framework – user or role credentials – to make sure only the right users have access,” Keydunov adds. “And through the performance insights in the Cube, customers can find redundant queries or other opportunities to cache and pre-aggregate query results, reducing the amount of computation required.”
Cube competes with AtScale, which also offers a semantic layer for data modeling and serving, and Dtb Labs’ recently acquired Transform. Still, Cube appears to be holding its own, with a customer base spanning more than 200 Fortune 1000 brands and a user base approaching 5 million people, the company says.
Keydunov says the Cube open source project has surpassed 10 million downloads, while Cube Cloud is now installed on about 90,000 servers. Bookings have increased 3x from 2023 to 2024, while the average offer size has increased 3x.
It is this success that has attracted new investment into the business, no doubt. San Francisco-based Cube announced this week that it has raised $25 million in funding from backers including Databricks Ventures, Decibel, Bain Capital Ventures, Eniac Ventures and 645 Ventures. Bringing the 40-employee startup’s total to $48 million, the new cash will go toward supporting Cube’s launch and marketing activities and expanding Cube Cloud’s capabilities, Keydunov said.
“Our investors have encouraged us to raise equity to support the expansion of our go-to-market team so we can take advantage of the massive growth in demand for AI and the semantic layer,” Keydunov continued. “We’ve seen businesses become more measured and careful in their evaluations, which can slow down the sales process a bit — but that gives us more time to prove our worth against the competition. We are well capitalized with our new round of funding and have plenty of runway to grow the company to its next milestone.”
