Sigma Computing was founded in 2014 to solve a stubborn problem in analytics: traditional BI tools force business users to either consume static dashboards or wait on data teams for every new question. Sigma's answer is a cloud-native workbook that feels like a spreadsheet but runs entirely against the cloud data warehouse, meaning analysts can pivot, filter, join, and calculate across billions of rows live, with no extracts, no row limits, and no data leaving the governed warehouse perimeter.
The platform is built for the modern data stack. It connects natively to Snowflake, Databricks, Amazon Redshift, Google BigQuery, and PostgreSQL, pushing all computation down to the warehouse so performance scales with the customer's existing infrastructure. On top of that foundation, Sigma adds collaborative workbooks, embedded analytics, input tables for write-back, and data applications that let teams build interactive, parameter-driven tools rather than read-only reports.
In recent years Sigma has leaned heavily into AI. Its agentic analytics and natural-language query features let business users ask questions in plain English and get governed, traceable answers grounded in the warehouse, while the company's AI roadmap focuses on copilots for analysts and automated insight generation. This positions Sigma against incumbents like Tableau, Looker, and Power BI with a thesis that BI should be cloud-first and AI-native rather than retrofitted.
Sigma raised a $200 million Series D in May 2024 co-led by Spark Capital and Avenir Growth Capital, reaching a roughly $1.5 billion valuation, and followed with an $80 million round in May 2026 as it pivoted toward agentic analytics. Backers include Snowflake Ventures, Sutter Hill Ventures, D1 Capital Partners, Altimeter Capital, and NewView Capital. The company serves thousands of organizations and is widely adopted by Snowflake and Databricks customers seeking a business-user-friendly layer over their warehouse.