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Cube, a San Francisco-based startup has raised $25 million in its latest funding round as it aims to enhance its semantic layer platform, which enables efficient data querying, transformation, and visualization.
The latest funding round was led by Databricks Ventures and joined by all the previous investors including Decibel, Bain Capital Ventures, Eniac Ventures, and 645 Ventures. Including all rounds, Cube’s total external funding has reached $48 million. The previous funding round was in 2021 when the startup raised $15.5 million to bring its first commercial product to market.
Businesses often store their data across multiple systems and platforms, making it challenging for data teams to gather and analyze the data. Each system may need a different API, requiring developers to be familiar with each unique API to retrieve data, significantly complicating the process of data integration.
Different systems may store data in different ways, further adding to the complexity, and making it difficult for analytics tools to reliably ingest information.
Cube’s semantic layer platform addresses some of these challenges by organizing data in a single and consistent format that can be easily ingested by analytics tools. Once the data has been organized, the platform makes it accessible through a centralized API designed to remove the need for developers to learn the programming interfaces of multiple systems.
Founded in 2019 as a single place to manage data models, caching, and security, Cube has grown to now power data experiences at 20% of the Fortune 1000, with nearly 5 million users. The company’s universal semantic layer, Cube Cloud, is vendor- and technology-agnostic, allowing it to connect to all types of cloud data warehouses and data consumption tools.
“We look forward to further advancing Cube Cloud’s capabilities and reaching more companies struggling to create consistent data to fuel their BI tools, AI programs, and data apps,” said Artyom, Co-founder and CEO of Cube. “This funding will pave the way for us to help more companies bring consistency, context, and trust to all their analytics.”
One of the key reasons for the success of Cube is its ability to optimize data workloads combining pre-aggregations and an in-memory cache system that helps enable up-to-date and interactive analytics with the flexibility to drill down into live data as needed.
According to Keydunov, the new funding will go toward expanding the platform’s core capabilities: data modeling, interoperability, and performance.
The data modeling capabilities will be enhanced to support complex metrics and introduce low-code tools to empower more data professionals. While Cube already supports a wide range of BI and data consumption tools, Keydunov aims to use the funding to expand integrations and interoperability, including more spreadsheet applications, BIs, and other data consumption tools.
In terms of performance, Keydunov aims to enhance the platform’s intelligent catching system enabling users to perform interactive analytics without the typical performance trade-offs.
The in-memory cache system of the platform saves the results of frequently repeated calculations, eliminating the need to recompute the results from scratch. This can help an organization manage their resources and reduce their costs.
Cube recently released an AI API to enable AI agents. This API is built on RAG architecture on top of Cube’s semantic lawyer to deliver to most relevant context data for LLMs. One of the future goals of Cube is to expand GenAI application beyond data consumption and use it for the development and maintenance of the semantic layer itself. With the inflow of fresh capital, Cube has plenty of runway to advance to the next milestone.
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