BI tool comparison

Sigma vs Looker: governed models or spreadsheet freedom?

Looker centers on a LookML model the data team owns. Sigma centers on a spreadsheet business users own. Here is how that plays out on Snowflake and BigQuery.

Linus TseGreyfield DataFacts last checked November 3, 2026

Contents 10 sections

The short answer

Pick Looker if you want every metric defined once in LookML, reviewed in git, and served the same way everywhere, and especially if you are on Google Cloud. Pick Sigma if the people asking questions want to explore and build in a spreadsheet without waiting on a model change, or need to enter data back into the warehouse.

How Looker and Sigma Computing differ

01
Model first vs workbook first
In Looker, analysts define LookML views and explores before anyone builds a chart. In Sigma, users start in a workbook on warehouse tables.
02
Write-back
Looker has no native write-back; Google’s own guide builds it with custom code. Sigma input tables are built in.
03
Google Cloud fit
Looker is part of Google Cloud, with Gemini features and BigQuery ties. Sigma runs on any of the major clouds.

Pricing at a glance

Looker

Google Cloud (acquired 2020)

Google Cloud's enterprise BI platform built around LookML, a code-based semantic modeling layer that generates SQL against the warehouse at query time. It suits data teams that want centrally governed metrics consumed through dashboards, Explores, embedded analytics and AI agents.

Pricing model: Platform fee by edition plus per-user licenses (Developer, Standard, Viewer), annual commitment
Published starting price: Not published (call sales)
Free tier: No - no Looker free tier is described on the pricing page; the general Google Cloud trial credit is not confirmed to apply. (Data Studio, formerly Looker Studio, is a separate free product.)
What drives cost: Three editions - Standard (under 50 users), Enterprise and Embed - each including one production instance, 10 Standard Users and 2 Developer Users, and differing in API call limits (1K / 100K / 500K query API calls per month). Cost is driven by edition, user mix (Developer > Standard > Viewer) and term. Conversational Analytics includes monthly token allowances per edition; listed overage rates are $3 per 1M input and $20 per 1M output tokens, but Google says overage billing has not started and 90 days' notice will be given.

Pricing source, checked November 3, 2026

Sigma Computing

Sigma Computing, Inc. (private, venture-backed; $80M Series E at a $3B valuation, May 2026)

Cloud BI and data-app platform that runs queries live against a cloud warehouse (Snowflake, Databricks, BigQuery, Redshift and others) through a spreadsheet-style interface, with write-back input tables, Python/SQL and AI agents. Built for companies that already keep their data in a cloud warehouse and want business users to work on it without extracts.

Pricing model: Annual subscription: platform tier plus per-user licenses (Creator / Explorer / Viewer, per third-party buyer data)
Published starting price: Not published
Free tier: No; 7-day free trial with sample data or your own warehouse
What drives cost: Sigma's pricing page shows no prices and sends buyers to sales. Vendr buyer data (third-party, directional only) lists Essential, Business and Enterprise tiers, Creator licenses at roughly $1,200-$5,000+ per year depending on tier, and a median contract of about $65K per year (range ~$15.6K-$145K). Warehouse compute is billed separately by your warehouse vendor.

Pricing source, checked November 3, 2026

Feature comparison

"Not verified" means we could not confirm it from public docs, not that the feature is missing. Each cell links to the page we checked.

Data and querying

Data and querying: Sigma Computing and Looker
Feature	Sigma Computing	Looker
Queries the warehouse live Reads the cloud warehouse at view time, without an extract or import step.	Yes Workbooks query the connected warehouse directly; no extract engine. source	Yes Looker generates SQL from LookML and runs it in the connected database at query time; persistent derived tables are optional caching in the warehouse. source
Spreadsheet-style editing Business users work in a grid and write formulas instead of a separate modeling language.	Yes Spreadsheet grid with 200+ functions on live warehouse data. source	Partly No spreadsheet grid. Table calculations use spreadsheet-like formulas on query results (permission-controlled), and Explores can be opened in Google Sheets via Connected Sheets. source
SQL editor Write SQL against the warehouse inside the product.	Yes Custom SQL elements in workbooks and data models. source	Yes SQL Runner for ad hoc SQL and derived table debugging (Standard and Developer users). source
Python Run Python inside the product.	Snowflake & Databricks only Only on Snowflake and Databricks connections; output cannot be materialized; only JWT-signed embeds are supported. source	No No in-product Python execution; an official Python SDK exists for calling the Looker API from outside. source
Writeback / input tables Users can enter or edit data that is saved back to the warehouse.	Yes Input tables write to a warehouse write schema. Not available on some sources (MySQL, SQL Server, Azure SQL DB, AlloyDB, Starburst, ClickHouse, MotherDuck). source	Partly Possible only through the Action API with a custom endpoint (e.g., Cloud Run function); no native input tables. source
Governed semantic layer Reusable, governed models and metric definitions.	Yes Data models with reusable metrics, relationships and RLS, also manageable as JSON/YAML via the API. source	Yes LookML models define dimensions, measures, joins and Explores centrally. source

Sharing and governance

Sharing and governance: Sigma Computing and Looker
Feature	Sigma Computing	Looker
Embedded analytics Embed reports in your own app or portal for external users.	Yes JWT-signed secure embeds that pass user attributes and teams for RLS. source	Embed edition Signed embedding for external users; requires Embed edition on Looker (Google Cloud core). source
Scheduled delivery Scheduled email, Slack, or file delivery of reports.	Yes Email, Slack, Teams, SharePoint, Google Sheets/Drive, webhook and cloud storage. source	Yes Email, Slack, webhook, S3, SFTP and Action Hub destinations. source
Row-level security Restrict which rows each user can see.	Yes Based on user, team or user attribute; works in embeds via JWT claims. source	Yes access_filter on Explores tied to user attributes adds a WHERE clause to every query (third-party guide; official docs page not opened). source
Excel / CSV export Export data to Excel or CSV.	Yes Excel and CSV up to 1M rows; larger exports go to cloud storage. source	Yes Looks and Explores deliver or download as CSV and XLSX; dashboards as PDF or CSV ZIP. source
Git version control Content or models can be versioned in git.	Version tags + API Version tags (with protected-tag approvals) and spec APIs; no built-in git sync, so teams build their own pipeline. source	Yes LookML projects are git-backed with branches, commits, deploys and webhook deploy secrets. source

Platform

Platform: Sigma Computing and Looker
Feature	Sigma Computing	Looker
AI assistant Natural-language questions or AI-assisted building, generally available.	Yes Sigma Assistant answers natural-language questions and shows its SQL; requires an AI provider set up for the org. Some AI surfaces, such as homepage chat, are still beta. source	Yes Conversational Analytics GA since Nov 13, 2025; Visualization Assistant GA; dashboard agents and Expression/Insight Assistants in preview. source
Open source / self-host An open-source or self-hosted option exists.	No SaaS only, hosted on AWS, Azure or GCP regions. source	Partly Not open source. Customer-hosted option exists only for Looker (original), with Gemini, managed MCP and some connectors unavailable. source
Needs a desktop app to author "Yes" means some authoring requires installing a desktop application.	No Authoring is entirely in the browser. source	No Browser-based authoring; the VS Code extension (GA Sept 2026) is optional for local LookML development. source

Governance

LookML is Looker’s biggest strength. Metric logic lives in code, versioned in git, and every dashboard and AI answer uses the same definitions. Teams that have been burned by conflicting numbers value that discipline.

The cost is speed. A new field or join often means a model change by the data team. Sigma has data models too, but users can also build directly in a workbook, which is faster and less controlled.

AI and recent changes

Looker’s conversational analytics became generally available in November 2025, and Google announced more agent features at Next ’26, some still in preview. Looker reports were deprecated in July 2026. Sigma has also shipped assistants, agents, and an MCP server through 2026.

Pricing

Neither publishes license prices. Looker sells editions with developer, standard, and viewer licenses on one- to three-year terms. Sigma sells annual contracts. Both run queries on your warehouse, so add that compute to either quote.

Where Looker is better

LookML is a mature, code-first semantic layer with git branching, development mode and CI validation, making governed metric definitions stronger and more reviewable than a workbook-centric model
Conversational Analytics (GA November 2025) is grounded in the LookML model, can join up to five Explores, and can be embedded in custom apps via UI or API
Strong developer tooling: GA VS Code extension for local LookML development, CI triggers from GitHub Actions/GitLab/Bitbucket, official API SDKs, and a Looker-managed MCP server (preview)
Broad scheduled delivery destinations: email, Slack, webhooks, Amazon S3, SFTP and Action Hub integrations
Semantic model can be consumed from Google Sheets (Connected Sheets) and other BI tools such as Tableau, Power BI and Excel through Looker connectors
A customer-hosted (self-hosted) deployment is still offered for Looker (original)
Deep Google Cloud integration (BigQuery, Gemini Enterprise, Knowledge Catalog)

Who should pick which

Pick Looker if

You want all metric logic in code, reviewed in git, served consistently everywhere.
You are on Google Cloud and BigQuery and want Gemini built in.
You have analytics engineers to maintain LookML.

Pick Sigma Computing if

Business users want to explore and build without waiting on model changes.
Users need to enter forecasts, approvals, or adjustments into the warehouse.
You want Python and SQL next to a spreadsheet in one workbook.

Recommendations by team

If you are	Pick	Why
Analytics engineering team on BigQuery	Looker	LookML in git and deep Google Cloud integration.
Finance or FP&A team	Sigma Computing	Spreadsheet work and input tables without model changes.
Team frustrated by slow model changes	Sigma Computing	Users can build in a workbook and promote what works.

Recent changes to Looker

2025-11 Conversational Analytics in Looker became generally available (Nov 13, 2025), after preview announced at Next in April 2025.source
2025-12 Self-service Explores on uploaded CSV/Excel files, tabbed dashboards and internal dashboard theming released in preview (Looker 25.20).source
2026-04 At Google Cloud Next '26: self-service Explores, tabbed dashboards, paginated reporting, Visualization Assistant, CI/CD and embedded conversational agents announced GA; dashboard agents, agentic workflows, a new interface and a managed MCP server in preview.source
2026-04 Google renamed Looker Studio back to Data Studio (separate free/Pro product, not Looker itself).source
2026-07 Looker reports (Studio-style reports inside Looker) deprecated; new reports can no longer be created.source
2026-09 Looker VS Code extension reached GA; Semantic Search GA (26.16).source
2026-10 Looker Data Apps entered preview: freeform dashboards built by local AI coding agents (26.18).source

Questions people ask

Does Sigma replace Looker?

Yes, if your users want more freedom than a model-first tool gives them and you can govern metrics in warehouse tables or Sigma data models. No, if centralized LookML governance is the reason you chose Looker. Teams that like LookML but want a newer tool also look at Omni.

Is Looker the same as Looker Studio?

No. Looker is Google’s enterprise BI platform built on LookML. Looker Studio is Google’s free dashboard tool and a separate product.

Sources and corrections

We checked these pages on the dates shown. Products change quickly. If you work at Looker and something here is wrong or out of date, email linus@greyfielddata.com and we will fix it.