Sacra estimates that Sigma Computing hit $200M in annual recurring revenue in April 2026, up from an estimated $185M at the end of 2025.

Sigma grew roughly 3x from approximately $9M ARR at the end of 2022 to $27M in 2023, then more than tripled to $90M in 2024. The increase from $90M to $185M in 2025 came as more organizations moved analytics workloads onto cloud data warehouses like Snowflake, BigQuery, and Databricks.

Growth from $185M to $200M in the first four months of 2026 marked a slower rate of expansion as Sigma's revenue base increased and large enterprise deals took longer to close. Revenue comes from recurring subscriptions across multiple license tiers, with account expansion driven by seat additions, upgrades to higher-functionality tiers, departmental rollouts, and consumption of metered capabilities such as embedded analytics.

Sigma raised a $200M Series D in May 2024 at a $1.5B post-money valuation.

Prior rounds include an $8M Series A in 2014, a $20M Series B in 2018, a $30M Series B financing in 2019, and a $300M Series C in 2021. Sutter Hill Ventures has backed Sigma across multiple rounds.

The disclosed rounds total $558M. Sacra's company profile estimates approximately $652M in total funding as of August 2024, which includes financings not captured in the public round records.

Sigma is a cloud-native analytics platform that connects directly to a company's cloud data warehouse and exposes live data through a spreadsheet-like interface. Finance analysts and operations managers can work with warehouse data in a browser using familiar rows, columns, pivot tables, and formulas, without writing SQL or waiting for a data team to build a dashboard.

A user might open a workbook connected to a Snowflake dataset containing sales transactions, group revenue by region, add calculated fields, build pivot tables, and create charts. Sigma translates each action into a query that runs directly against the warehouse, keeping the data live and governed rather than exporting it to a disconnected spreadsheet.

Users can also build interactive data applications on the same warehouse data, including input forms for sales forecasts, write-back tables for budget assumptions, and approval workflows for flagged transactions. Sigma also allows companies to embed these dashboards and applications in their own products. For example, a SaaS vendor could add usage analytics to its customer portal without building a reporting engine.

Sigma's AI capabilities let users ask natural-language questions, generate formulas, and receive suggested analyses. Computation remains in the warehouse, so Sigma does not maintain a separate data store, reducing migration requirements relative to traditional BI tools.

Sigma sells B2B subscriptions to enterprises, with pricing based on user licenses across functionality tiers. Viewer seats, intended for dashboard and report consumption, are the least expensive. Higher-priced analyst and application-builder seats include data exploration, modeling, and app creation.

All computation runs in the customer's cloud data warehouse, so Sigma does not incur the cost of storing or processing customer data. This architecture keeps infrastructure costs lower than those of BI vendors that maintain proprietary query engines or data stores. Gross margins primarily depend on software delivery and support costs rather than compute.

Revenue expands within accounts as more departments adopt the platform. A customer might start with a data team building dashboards, then add finance teams for planning and forecasting and operations teams for interactive applications. Each deployment adds seats and may shift users to higher-priced tiers. Embedded analytics provides another expansion channel, with customers paying for Sigma capacity used by their end users and linking Sigma's revenue to customer product usage.

The model creates mutual platform dependency. Sigma relies on cloud warehouses such as Snowflake, BigQuery, Redshift, and Databricks, while those providers benefit when Sigma makes warehouse data accessible to nontechnical users. Warehouse ecosystem partnerships can extend distribution, but the dependency also exposes Sigma to changes in warehouse pricing and product roadmaps.

Sigma competes with legacy BI suites, modern self-service platforms, and AI-native analytics tools, while warehouse vendors expand into the consumption layer.

ThoughtSpot is Sigma's most direct competitor in enterprise self-service analytics. ThoughtSpot has raised $674M and was valued at $4.2B in 2021, giving it more resources for enterprise sales and product development. Sigma uses a spreadsheet interface familiar to Excel users, while ThoughtSpot focuses on search-based and conversational analytics.

The two companies target similar customers through different interaction models. Deal outcomes often depend on whether a buyer's user base skews toward spreadsheet-native finance and operations teams or executives and managers who prefer natural-language search.

Microsoft Power BI, Salesforce Tableau, and Google Looker represent the largest competitive threat by volume. They benefit from bundled pricing within broader enterprise agreements, existing IT procurement relationships, and large installed bases.

Power BI ships inside Microsoft 365 at near-zero incremental cost for many enterprises, making price competition difficult for standalone vendors. Tableau and Looker also draw on their parent companies' sales organizations and can be sold as part of broader CRM or cloud platform deals.

Sigma differentiates itself through its warehouse-native architecture and spreadsheet interface, which can reduce time-to-insight for business users without the data extracts and semantic layers required by traditional tools.

Mode, Hex, and Metabase compete for collaborative analytics workflows, particularly among technical teams seeking notebook-style interfaces or lightweight dashboards. Retool overlaps with Sigma's expansion into interactive application building on live data.

In finance-specific workflows, Sigma competes with modern spreadsheet alternatives such as Equals and Runway, which offer FP&A teams purpose-built planning tools. Vena instead enhances Excel within Microsoft 365, keeping users in a familiar environment while adding planning and budgeting capabilities.

AI-native analytics vendors such as Seek AI, AnswerRocket, and DataGPT compete in natural-language querying. Enterprise governance requirements and deployment maturity distinguish these tools from more established platforms such as Sigma.

Sigma's core opportunity is to expand beyond dashboard-based BI into a broader platform for analytics, planning, and operational applications running on live warehouse data.

Sigma's application-building capabilities extend the product from read-only analytics to interactive, write-back workflows. Finance teams can use Sigma for budgeting and forecasting instead of a dedicated FP&A tool such as Runway or Vena. Operations teams can build approval workflows and data entry forms that would otherwise require custom internal tools or platforms such as Retool.

This expands Sigma's addressable budget beyond BI software to operational software, FP&A, and internal tool development, categories that collectively exceed the traditional BI market.

Embedded analytics allows Sigma customers to integrate Sigma-powered dashboards and applications into their products. A SaaS company serving healthcare providers, for example, could embed Sigma reports in its customer portal rather than build and maintain a bespoke analytics layer.

This model creates consumption-driven revenue tied to customers' end-user growth. It also adds product and engineering teams as potential buyers in a market that includes Looker, Sisense, and purpose-built embedded tools.

As Snowflake, BigQuery, and Databricks grow and more business-critical data moves to cloud warehouses, Sigma's pool of potential users expands. Each new warehouse customer is a prospect for an analytics layer, while Sigma's integrations with these platforms make it a potential next step after data ingestion and transformation through tools such as Fivetran and dbt.

AI capabilities, including natural-language querying and automated analysis, can lower the technical barrier to working with warehouse data. If Sigma can make AI-assisted analytics reliable for governed enterprise use, it could serve more users within each customer than traditional BI tools, which typically serve analysts and power users.

Platform dependency: Sigma's product runs on cloud data warehouses such as Snowflake, BigQuery, and Databricks, so native analytics and application-building capabilities that meet most users' needs could materially reduce the value of Sigma's independent layer.

Suite bundling pressure: Microsoft Power BI ships at near-zero marginal cost within Microsoft 365 agreements covering hundreds of millions of enterprise users, while Salesforce and Google can bundle Tableau and Looker into broader platform deals, making it structurally difficult for a standalone analytics vendor to compete on price even if its product is superior.

AI commoditization: Natural-language querying and AI-generated analysis are becoming standard features across BI and analytics products, and convergence in capability quality would reduce Sigma's differentiation and shift competition toward interface design and workflow depth against larger incumbents with broader distribution.