Sigma Computing has raised $80 million in Series E funding at a $3 billion valuation, doubling its worth in a year. The round was led by Princeville Capital, with strategic investments from Databricks Ventures, ServiceNow Ventures, and Workday Ventures.TL;DR
Sigma Computing has raised $80 million in Series E funding at a $3 billion valuation, doubling its worth in a year and positioning the San Francisco-based company as one of the most aggressively valued players in the business intelligence market. The round was led by Princeville Capital, with new strategic investors Databricks Ventures, ServiceNow Ventures, and Workday Ventures also participating. Previous backers including Altimeter Capital, Avenir Growth Capital, D1 Capital Partners, Spark Capital, and Sutter Hill Ventures returned for the round, and JP Morgan acted as placement agent.
The valuation jump from $1.5 billion to $3 billion reflects a company that has doubled its revenue in the same period. Sigma announced in April that it had reached $200 million in annual recurring revenue, up from roughly $100 million a year earlier, with more than 2,000 customers and 1.1 million new active users added in the latest fiscal year. The customer list includes AMD, Duolingo, Colgate-Palmolive, and JPMorgan Chase.
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Sigma sells a cloud-native analytics platform that sits on top of data warehouses built by Snowflake, Databricks, and Google BigQuery. Its core proposition is that business users can query and analyse live warehouse data through a spreadsheet-like interface without writing SQL, while IT teams retain the governance and security controls that the warehouse provides. The platform supports spreadsheet operations, SQL, Python, and what the company now calls “AI Apps,” all running against the warehouse’s compute layer rather than copying data into a separate system.
That architecture is the foundation of Sigma’s pitch to enterprises: it does not move or duplicate data, which means the row-level security, column masking, and access controls that companies have already configured in their warehouse apply automatically to everything built in Sigma. In a market where data governance is both a regulatory requirement and an executive preoccupation, this is a meaningful differentiator against legacy BI tools that extract data into their own layers.
The Series E comes at a moment when Sigma is redefining what it sells. The company has moved beyond traditional business intelligence into what it calls “agentic analytics,” a category that barely existed two years ago and that every major enterprise software vendor is now racing to claim. SAP unveiled more than 200 AI agents at Sapphire 2026, Google positioned its entire Cloud Next conference around agentic AI, and Snowflake forged a $200 million partnership with OpenAI to embed AI agents directly in the data warehouse. Sigma’s argument is that it was already there.
The company’s flagship product in this space is Sigma Agents, customisable no-code AI agents that run inside the data warehouse’s existing security and governance framework. The agents can operate in three modes: interactive, where a user chats with the agent and approves actions; autonomous, where the agent monitors data and executes workflows on a schedule; and external, where the agent makes API calls to third-party systems. In the first quarter of the current fiscal year, Sigma Agents became the fastest-adopted product in the company’s history.
CEO Mike Palmer framed the strategy in terms that reflect the broader tension in enterprise AI. “IT needs technology that enables the enterprise to go fast in areas like vibe-coded apps and agentic development, while also going safe,” he said. The reference to vibe coding, the practice of building software through natural language prompts, is deliberate. As more business users build applications without traditional development skills, the risk of ungoverned, insecure outputs increases. The security vulnerabilities inherent in vibe-coded applications have already drawn scrutiny, and Sigma’s pitch is that its warehouse-native architecture provides the governance layer that vibe-coded tools lack.
The participation of Databricks Ventures, ServiceNow Ventures, and Workday Ventures is as notable as the headline valuation. All three are the venture arms of companies that are either Sigma’s infrastructure partners or its potential competitors, and their investment signals that they view Sigma as complementary rather than threatening. Databricks, whose lakehouse platform is one of the warehouses Sigma runs on, described the investment as supporting users who want to “begin with an easy-to-use spreadsheet interface, and scale up to the power of AI apps.” ServiceNow and Workday, both of which generate enormous volumes of enterprise data that their customers need to analyse, see Sigma as a layer that adds value on top of their own platforms.
Vivian Huang of Princeville Capital, who joins Sigma’s board, cited the company’s “warehouse-native architecture and strong operating discipline at scale” as the basis for the investment. The “operating discipline” language is worth noting: Sigma raised $80 million, not $800 million. In a market where AI companies routinely raise rounds ten times that size, the relatively modest amount suggests either that Sigma did not need more capital or that it chose to limit dilution while still securing strategic partners. With $200 million in ARR and what the company describes as more than 100 per cent year-over-year growth, the economics may support the restraint.
A $3 billion valuation at 15 times ARR is aggressive for a business intelligence company but not unreasonable for one that is growing at triple-digit rates and has successfully repositioned itself as an AI platform. The traditional BI market, dominated by Tableau (now owned by Salesforce), Microsoft Power BI, and Looker (owned by Google), has been slow to absorb the agentic AI shift. Sigma’s bet is that the transition from static dashboards to autonomous, AI-driven analytics represents a generational opportunity to take market share from incumbents that are bolting AI capabilities onto architectures designed for a different era.
Whether that bet pays off depends on execution. Sigma has the revenue growth, the strategic investors, and the product positioning. What it does not yet have is the scale of its largest competitors or the certainty that “agentic analytics” will become a durable category rather than a marketing label that every vendor adopts and dilutes. The $80 million will fund the next phase of that test.
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