Cube

The second major feature group revolves around Cube's deep context capabilities, which enforce consistent semantics across the organization. Cube Cloud's semantic layer acts as a single source of truth for metric definitions, ensuring that every tool and team uses the same calculation logic. Agents generate SQL directly from these semantic models and associated policies, so every query reflects governed rules. Additionally, Cube allows for dynamic creation of new governed metrics on demand. If a business user needs a metric not already defined, an agent can derive it based on trusted existing measures, preserving consistency and governance. This approach solves the common problem of metric discrepancy where different departments report conflicting numbers. The outcome is a unified analytical language that scales from ad-hoc questions to executive dashboards, all built on a foundation of trust.

Cube's third feature group provides full visibility and control over AI agent behavior. Every insight returned includes an explanation of the underlying SQL, data sources, and assumptions used, so users can easily validate the results. This transparency builds confidence in AI-generated outputs. Teams also maintain full control over what agents can do—they follow defined rules and permissions, preventing unauthorized data access or logic. Moreover, Cube enables administrators to certify agent reports, turning trusted outputs into reusable assets that pass regulatory audits. This certification process ensures that important reports are reviewed and locked, similar to a publication workflow. For regulated industries or large enterprises, this feature is critical for maintaining compliance while still leveraging the speed of AI. It balances agility with control.

Cube's overall approach combines an agentic analytics interface with a semantic layer as the core engine. The workflow begins when a user submits a natural language question through the Cube frontend. The AI agent interprets the intent and maps it to the appropriate entities and measures defined in the semantic model. Using governance policies, the agent then generates syntactically correct SQL, executes it against the underlying data sources (e.g., lakehouse, data warehouse), and returns results formatted as charts, tables, or summaries. Users can iteratively refine via follow-ups, with each interaction building on previous context. All queries are logged and accessible for auditing, and outputs can be saved as reusable workbooks. The integration layer allows embedding via iframe or connecting with tools like MCP and A2A. This architecture ensures speed, consistency, and compliance.

Concrete use cases for Cube span multiple scenarios. In embedded analytics, product teams use Cube to deliver consistent, secure dashboards within their applications, as seen with Brex and Drata. For real-time analytics, organizations trust Cube's stack for speed and consistency, enabling live dashboards that update with fresh data. Cube also serves as an LLM and AI semantic layer, providing context to AI chatbots so they answer accurately based on governed data. Customer stories highlight outcomes: Drata's CSMs regain dozens of hours each quarter by using AI-driven quarterly business reviews; Alcon avoids writing 20 different queries per metric by defining it once in Cube's model; Welbee integrates Cube into a multi-agent system to dynamically interpret data. Across these cases, the common outcome is faster insights, reduced manual effort, and consistent metrics.

Cube targets analytics engineers, data scientists, BI teams, product managers, and customer success managers who need to scale data analysis without increasing headcount. It integrates with the existing data stack, including lakehouses like Databricks, and supports embedding via iframe, MCP, or A2A protocols. While detailed pricing is available through a link, the platform offers a cloud service (Cube Cloud) with managed infrastructure. Cube's open-source core (Cube Core) also exists for self-hosted scenarios. The takeaway is that Cube empowers modern data teams to automate ad-hoc analysis, enforce governance, and deliver trustworthy insights at scale. By combining AI-driven natural language with a robust semantic layer, it transforms how organizations interact with their data—making it faster, more consistent, and more accessible.