A increasing number of companies are adopting data models – abstract models that organize data elements and standardize their relationships. However, as the hype around data analytics and AI pushes organizations to expect more from data models, traditional methods are proving to be difficult to manage and extremely fragile.
At least, this is what Artyom Keydunov and Pavel Tiunov noticed through their work experience. When the duo founded Starsbot, a data analytics startup in 2016, they often consulted with struggling organizations trying to get their data in order.
Cube was born in 2019 as an open-source project, offering a “universal semantic layer” for organizing corporate data. This data could then be fed into databases, business intelligence tools, and even AI-powered chatbots. Fast forward five years, and Keydunov and Tiunov have a thriving business with their subscription-based service, Cube Cloud. Along with Cube, the service offers automated workflows, enterprise-focused governance, and deployment tools.
“There’s no shortage of data,” Keydunov told TechCrunch. “The demand for data continues to grow among employees, partners, and customers, driven by the idea that data-driven decisions lead to operational efficiency, increased customer satisfaction, and a competitive edge. Technologies like AI, machine learning, and the Internet of Things are reshaping the data landscape and revolutionizing how organizations collect, process, and derive value from data. Now, even machines need data, not just humans.”
Despite the challenges of data modeling, surveys suggest that few businesses are successfully deriving value from their data. According to a 2022 poll by Gartner, less than half of data analytics leaders believe their teams are effectively providing value to their employers. Surprisingly, companies are still spending an average of over $5 million on data management, governance, and analytics initiatives.
So what’s the solution? For Keydunov and Tiunov, the answer was to create a platform that could serve as a unified source of truth for all a company’s data and metrics.
“Cube Cloud is a universal semantic layer that acts as an independent, yet interoperable, part of the modern data stack. It sits between data sources and data consumers,” explained Keydunov. “The universal semantic layer allows all data endpoints – whether they are 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 various applications and utilities. The service offers role-based access controls, data caching, single sign-on, and can scale up infrastructure as needed. In addition, enterprise-tier customers have access to consultants who can train their data engineers to use Cube Cloud and provide on-demand support. These customers can also have their initial Cube Cloud instance built on either Cube-owned servers or their own premises, customized to fit their business needs.
“Cube Cloud automatically adapts queries and adds the necessary security context, such as user or role details, to ensure only the right users have access,” added Keydunov. “Through Cube’s performance insights, customers can identify redundant queries and opportunities for caching and pre-aggregating query results, reducing the amount of computing required.”
Cube’s main competitors are AtScale, which also offers a semantic layer for data modeling and serving, and the recently acquired Transform by Dtb Labs. However, Cube seems to be holding its own in the market, with over 200 Fortune 1000 brands as customers and nearly 5 million users. Keydunov shared that the open-source Cube project has exceeded 10 million downloads, while Cube Cloud is installed on approximately 90,000 servers. Bookings have tripled from 2023 to 2024, while the average deal size has grown three-fold.
Their success attracted new investment, as San Francisco-based Cube announced raising $25 million in funding this week. The round was backed by Databricks Ventures, Decibel, Bain Capital Ventures, Eniac Ventures, and 645 Ventures, bringing the total raised by the 40-employee startup to $48 million. According to Keydunov, the new funds will support Cube’s go-to-market and marketing efforts while expanding Cube Cloud’s capabilities.
“Our investors encouraged us to raise equity to support the expansion of our go-to-market team and take advantage of the surge in demand for AI and the semantic layer,” Keydunov continued. “We have noticed that enterprise businesses are becoming more cautious in their evaluations, which can slow down the sales process. However, this gives us more time to prove our value over the competition. With our new funding round, we are well-equipped to continue growing the company and reach our next milestone.”
