Cube
Cube is an AI agentic analytics platform for governed business intelligence and embedded analytics. Ideal for modern data teams and product teams, it helps deliver trusted, consistent answers and embedded analytics by centralizing definitions in a semantic layer.
About Cube
Cube is an AI-powered agentic analytics platform that combines business intelligence, embedded analytics, and a governed semantic layer. It helps organizations turn complex business data into reliable insights, interactive dashboards, and natural-language answers while keeping metrics and definitions consistent across teams, applications, and AI agents.
Built for data engineers, analysts, developers, and product teams, Cube connects to existing data infrastructure and creates a centralized semantic model for business metrics, relationships, and access rules. Instead of rebuilding queries and calculations across multiple tools, organizations can define their data logic once and reuse it across dashboards, applications, APIs, and AI workflows.
Cube's agentic analytics capabilities include Analytics Chat, Workbook Agents, and Dashboard Agents. Users can ask questions in natural language, explore datasets, generate queries, and create visual reports with AI assistance. Its Model Context Protocol (MCP) server also enables external AI assistants and custom agents to access governed business data, supporting analytics workflows through tools such as ChatGPT, Claude, Cursor, and Slack.
For software companies, Cube provides embedded analytics through APIs, customizable dashboards, conversational interfaces, and multi-tenant data access. Pre-aggregations, caching, and Cube Store help accelerate analytical queries, while centralized governance ensures that AI-generated insights and customer-facing analytics follow consistent business definitions and permissions.
Cube offers a free plan for smaller projects, with paid plans starting at $40 per developer per month. Premium plans add embedded analytics, expanded collaboration, and advanced deployment capabilities, while Enterprise provides custom infrastructure, security, and support options.
Environment ā AI-native business intelligence and embedded analytics platform for data teams, developers, SaaS companies, and enterprises.
š Integrations ā Snowflake, Databricks, Google BigQuery, dbt, SQL databases, data warehouses, ChatGPT, Claude, Cursor, Slack, MCP-compatible AI agents, and custom applications through APIs.
š Core Loops ā Connect data sources ā Define governed metrics and semantic models ā Query and analyze data using AI agents ā Generate workbooks, dashboards, and insights ā Embed analytics into applications ā Monitor performance and maintain consistent business definitions.
ā” Capabilities ā ā¢ Centralized semantic layer and business metric definitions ā¢ Natural-language analytics with Analytics Chat ā¢ AI-assisted SQL generation and data exploration ā¢ Workbook Agent for interactive analysis ā¢ Dashboard Agent for conversational reporting ā¢ Embedded analytics and customizable dashboards ā¢ MCP server for AI assistants and autonomous agents ā¢ Governed data access through APIs ā¢ Multi-tenant analytics for SaaS applications ā¢ Pre-aggregations and intelligent query caching ā¢ Cube Store for accelerated analytical workloads ā¢ Semantic model development and management ā¢ Data access controls and enterprise governance ā¢ Custom deployment and infrastructure options
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Who is using Cube?
Key Features
- Centralize metric definitions in a semantic layer to ensure consistent numbers everywhere
- Run natural-language queries with Analytics Chat that build governed SQL against your model
- Embed analytics via iframes, Creator Mode, or Core Data APIs for multi-tenant products
- Accelerate queries with pre-aggregations and caching for sub-second dashboard performance
- Augment analysts with Workbook and Dashboard Agents that draft and iterate queries and visuals
- Integrate LLMs and agents via the MCP server and use a Slack agent for in-context answers
- Ship custom UIs or full workbooks while governance and permissions flow from the model
Use Cases
- Embed conversational analytics and dashboards inside a SaaS product
- Provide governed BI and a single source of truth for finance and ops
- Enable natural-language data exploration for non-technical users
- Speed up analyst workflows with AI-assisted query writing and workbook creation
- Deliver multi-tenant, branded analytics to customers without rebuilding the BI stack