I am thrilled to announce that we have completed $25 million in funding. As part of this round, Nnamdi Okike, co-founder and Managing Partner at 645 Ventures joined us as a new board observer for Cube's Board of Directors. I am honored that Nnamdi and 645 Ventures have doubled down on their investment and belief in what we can achieve at Cube.

Databricks, our strategic partner, joined this funding round. The Databricks team shares our vision that the semantic layer is a necessary component of the AI-enabled future of the data stack. With this new investment, we are deepening our relationship with Databricks and reaffirming our commitment to our roadmap to support an AI-enabled data intelligence future.

Andrew Ferguson, VP, Databricks Ventures, said it best “The semantic layer is critical to data intelligence and provides necessary context for AI agents to analyze data with higher accuracy.”

Solving data problems with the universal semantic layer

In 2019, Pavel and I started Cube to create a single place to manage data models, security, and caching. As we talked to more people and worked with customers, the problem that needed to be solved became clearer. Data models are spread across too many BI tools, and data engineers need an open and universal semantic layer to deliver trusted data. The data community needs to create a single source of truth that can feed metrics and defined data to any data app, BI tool or AI agent.

How we got here

We launched the first version of open source Cube on HackerNews in 2019. Cube Core grew rapidly in the first several months, crossing 10,000 stars on GitHub and 1,000s of data engineers and developers joining the community almost overnight. Today, Cube’s open source community has grown to over 10,000 developers in the Cube Slack community, and it has 17,000 stars on GitHub. In 2021, the team built and launched the commercial version, Cube Cloud, and signed up customers online or through a small sales team. Today, Cube Cloud is experiencing 3x growth year over year in both customers and bookings.

What today's semantic layer looks like

Today, Cube Cloud is a universal semantic layer that is an independent yet interoperable part of the data stack between your data sources and data apps. And being universal is critical: Cube is vendor- and technology-agnostic. Cube Cloud connects to every cloud data warehouse on one side and to every data consumption tool on the other side – whether it is BI tools, Excel, embedded analytics, or AI agents – using the same semantics and underlying data. This leads to consistent and trusted insights, ultimately driving informed business decisions.

We are committed to being technology agnostic and helping our customers avoid a vendor lock-in. Implementing a universal semantic layer means you can connect to any cloud data source and any visualization tool, and are never locked into one technology. In the rapidly evolving modern data stack, flexibility must be a top priority because you need the ability to add, remove, or change solutions according to your evolving requirements. When business definitions are institutionalized in a universal semantic layer, migrations to new platforms are as simple as pointing to the new endpoint.

We keep building Cube Cloud with these critical core tenants:

  • Cube is code-first and believes in empowering the data engineer by applying software engineering best practices and processes to data management, following CI/CD software practices, creating isolated environments, and delivering easy version control.
  • Cube is interoperable so you can model once, deliver anywhere with a robust set of deployment options, data connectivity, and native APIs to any data endpoint.
  • Cube is fast and optimizes data workloads with a combination of in-memory cache and pre-aggregations to enable interactive analytics with the flexibility of live connections to the cloud data warehouse.

What's next

With new funding, we are doubling down on our roadmap in our core areas: data modeling, interoperability, and performance. We are also investing in supporting an AI-enabled data intelligence future:

  • Data modeling. We’re expanding our data modeling capabilities to support more complicated calculated metrics and introducing low-code tools to empower more data professionals to build semantic models in Cube.
  • Interoperability. We already support dozens of BI and data consumption tools. Earlier this year, we introduced support for the Microsoft suite of products. Integrations and interoperability remain our big priority, and more integrations are coming. This includes BIs, spreadsheet applications, and other data consumption tools.
  • Performance. We believe users shouldn’t need to choose between “extracts” and “live queries.” With our intelligent caching, we can provide the best of both worlds: interactive analytics with live drill-down capabilities to the cloud data warehouse.
  • AI-enabled data intelligence. The semantic layer provides critical context for AI agents to analyze data with higher accuracy. We’ve recently released AI API to enable AI agents to access data in the cloud data warehouses. For that, we've built a robust retrieval augmented generation (RAG) architecture on top of Cube’s semantic layer to provide the most relevant context about the data to LLMs. Looking into the future, there are a lot of opportunities in applying Gen AI not only to data consumption but to the development and maintaince of the semantic layer itself.

Be on the lookout for what’s to come as we add more functionality with every release. I can’t wait to see what you build on top of Cube!

Come talk to us and we will help you get started.