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Blog · Alternatives and comparisons

Sigma vs Looker: what each is built for, and when neither fits

Sigma vs Looker compared: what each is built for, what it needs before the first answer, and a file-first option when neither fits.

The short answerBoth query a cloud warehouse live. Sigma lets spreadsheet users work on warehouse tables directly; Looker has a data team define metrics in LookML first. Sigma suits companies that already keep their data in a cloud warehouse such as Snowflake, Databricks or BigQuery; Looker suits organisations that want one governed set of metric definitions shared across teams and tools. When the need is one desk's question answered from the exports it already has, a file-first tool may fit better than either.

Sigma and Looker are both good at what they are built for. Both query a cloud warehouse live. Sigma lets spreadsheet users work on warehouse tables directly; Looker has a data team define metrics in LookML first. This guide sets them side by side, fairly, and ends with the case where a team needs neither.

At a glance

Sigma Looker Covirage
Built for Companies that already keep their data in a cloud warehouse such as Snowflake, Databricks or BigQuery; Spreadsheet-literate business teams who want to analyse large tables without learning SQL Organisations that want one governed set of metric definitions shared across teams and tools; Companies with a SQL data warehouse, particularly BigQuery users already on Google Cloud The person waiting for the answer, in a big or medium company
How it gets data Sigma requires a connection to a supported data platform and queries it live: Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, AlloyDB, MySQL, Azure SQL Database, Starburst, and SQL Server 2022 or Azure SQL Managed Instance. Sigma sends queries over that connection and works with the result sets returned, rather than holding its own copy of the data. Live query: Looker generates SQL from the LookML project and submits it to the customer's database connection (BigQuery, Snowflake, Redshift, PostgreSQL, SQL Server, Oracle and many other dialects). SQL Runner also allows direct SQL against those connections. Files and sheets now; scheduled delivery by API, S3, Azure, SFTP or email on Business; connections built with your team on Enterprise
Needs before the first answer A supported cloud data warehouse or database, already loaded with the data to analyse; A connection from Sigma to that warehouse, with permissions set up by an administrator; Published data models or curated sources for Sigma Assistant to choose from A SQL database or warehouse holding the data; LookML developers to model tables, joins and measures before business users explore; Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting The exports your systems already produce. No warehouse, no modelling language, no data team
AI Sigma Assistant answers natural-language questions by selecting a data source from the warehouse tables, published data models and curated sources the user can access, writing and running SQL, and returning charts, tables or written insights. Users can view the SQL and the steps it took, and open results in a workbook. Assistant, the formula assistant and 'Explain this chart' need an AI provider: a model hosted in the customer's data platform (such as Snowflake Cortex or Databricks) or an external provider such as OpenAI or Azure OpenAI. Conversational Analytics uses Gemini for Google Cloud to interpret a natural-language question, map it to fields in the LookML model, and compose a Looker query through the model's Explores rather than writing free-form SQL. It then analyses the query results to answer the question and can create visualisations. Administrators must enable the Gemini in Looker setting. The AI model chooses a measure and writes the explanation; our tools do the arithmetic and check every total
Published pricing Sigma does not publish prices. A third-party guide from July 2026 describes four licence tiers (View, Act, Analyze and Build) plus usage credits for selected billable events, sold through sales quotes. A free trial is available. Platform editions (Standard, Enterprise and Embed) and user licences (Developer, Standard and Viewer) are sold on annual commitments priced through Google Cloud sales; no list prices are published. Conversational Analytics is metered in data tokens, with monthly allowances included per platform tier (for example 60M input and 1.2M output tokens on Standard); overage is listed at $3.00 per 1M input and $20.00 per 1M output tokens, though Google states billing is not yet enforced during a promotional period. Data Studio (formerly Looker Studio) is free, with a paid Pro tier offering a 30-day trial. Three tiers on one sheet, shared in a conversation

Sigma in brief

Sigma is a cloud analytics and business intelligence platform that works directly on a company's cloud data warehouse. Users build workbooks, dashboards and data apps through a spreadsheet-style interface with familiar formulas, and Sigma turns that work into queries that run live in the warehouse. It also supports writeback, approvals and AI-assisted workflows on top of warehouse data.

  • Sigma was founded in 2014 by Jason Frantz and Rob Woollen and is headquartered in San Francisco.
  • Sigma launched its cloud analytics service in 2018.
  • Sigma reports more than 1,900 organisations building on its platform.
  • Sigma requires a connection to a supported data warehouse and queries it live.

Looker in brief

Looker is Google Cloud's enterprise business intelligence platform. Data teams describe their database in LookML, Looker's modelling language, and Looker then generates SQL from that model and runs it against the connected database to power Explores, dashboards, scheduled deliveries and embedded analytics. It is separate from Google's free report builder, formerly Looker Studio and now called Data Studio.

  • Looker was founded in 2012 in Santa Cruz, California by Lloyd Tabb and Ben Porterfield.
  • Google announced its $2.6 billion acquisition of Looker on 6 June 2019 and completed it in February 2020.
  • Looker's documentation describes it as a tool that generates SQL queries from a LookML project and submits them against a database connection.
  • Looker platform editions are sold on annual commitments through Google Cloud sales, with no published list price.

Where Sigma fits best

  • Companies that already keep their data in a cloud warehouse such as Snowflake, Databricks or BigQuery.
  • Spreadsheet-literate business teams who want to analyse large tables without learning SQL.
  • Teams building interactive data apps with input and writeback on warehouse data.

Where Looker fits best

  • Organisations that want one governed set of metric definitions shared across teams and tools.
  • Companies with a SQL data warehouse, particularly BigQuery users already on Google Cloud.
  • Product teams embedding governed analytics into customer-facing applications.

Where they differ

Data

Sigma. Sigma requires a connection to a supported data platform and queries it live: Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, AlloyDB, MySQL, Azure SQL Database, Starburst, and SQL Server 2022 or Azure SQL Managed Instance. Sigma sends queries over that connection and works with the result sets returned, rather than holding its own copy of the data.

Looker. Live query: Looker generates SQL from the LookML project and submits it to the customer's database connection (BigQuery, Snowflake, Redshift, PostgreSQL, SQL Server, Oracle and many other dialects). SQL Runner also allows direct SQL against those connections.

AI

Sigma. Sigma Assistant answers natural-language questions by selecting a data source from the warehouse tables, published data models and curated sources the user can access, writing and running SQL, and returning charts, tables or written insights. Users can view the SQL and the steps it took, and open results in a workbook. Assistant, the formula assistant and 'Explain this chart' need an AI provider: a model hosted in the customer's data platform (such as Snowflake Cortex or Databricks) or an external provider such as OpenAI or Azure OpenAI.

Looker. Conversational Analytics uses Gemini for Google Cloud to interpret a natural-language question, map it to fields in the LookML model, and compose a Looker query through the model's Explores rather than writing free-form SQL. It then analyses the query results to answer the question and can create visualisations. Administrators must enable the Gemini in Looker setting.

What each needs before the first answer

Sigma:

  • A supported cloud data warehouse or database, already loaded with the data to analyse.
  • A connection from Sigma to that warehouse, with permissions set up by an administrator.
  • Published data models or curated sources for Sigma Assistant to choose from.

Looker:

  • A SQL database or warehouse holding the data.
  • LookML developers to model tables, joins and measures before business users explore.
  • Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting.

When neither fits

Both assume a team that will build and maintain a model of the data before questions get answered. Some teams have a narrower need: one desk, one recurring question such as "what is driving my costs" or "where is my revenue coming from", and exports that already exist. For them, Covirage reads those files, answers the question with a bridge that sums to the change, and is set up for them within a week. The AI model chooses the measure and explains it; our tools do the arithmetic.

See the full pages on Sigma alternatives and Looker alternatives, or how to evaluate an analytics vendor.

Sources

Facts about other products were checked on 24 September 2026 from the pages below. Product names are trademarks of their owners; Covirage is not affiliated with them.

Questions people ask

What is the main difference between Sigma and Looker?

Both query a cloud warehouse live. Sigma lets spreadsheet users work on warehouse tables directly; Looker has a data team define metrics in LookML first.

Which is easier to start with, Sigma or Looker?

It depends on what is in place. For Sigma: A supported cloud data warehouse or database, already loaded with the data to analyse; A connection from Sigma to that warehouse, with permissions set up by an administrator; Published data models or curated sources for Sigma Assistant to choose from. For Looker: A SQL database or warehouse holding the data; LookML developers to model tables, joins and measures before business users explore; Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting. Check both against your team and your data before comparing licence prices.

Is there an alternative to both?

For a desk that needs to explain why its numbers moved, from exports it already produces, Covirage answers from the file and is set up for you within a week. It does not replace a platform for the whole organisation.