BI tool comparison

Sigma vs Omni: spreadsheet-first or model-first BI?

Sigma and Omni both query your warehouse live and both have spreadsheet views. The real difference is where the logic lives and who maintains it.

Linus TseGreyfield DataFacts last checked November 3, 2026

Contents 10 sections

The short answer

Omni is the closer competitor to Sigma than any legacy BI tool. Pick Omni if your data team wants a code-defined semantic model in git, two-way dbt sync, and governed AI over that model. Pick Sigma if the people doing the work are finance and operations users who want a full spreadsheet on warehouse data and need to enter data back into it.

How Omni Analytics and Sigma Computing differ

01
Workbook-first vs model-first
In Sigma most logic starts in the workbook and moves into data models when it proves useful. In Omni the data team defines a shared model first and everything sits on it.
02
Git and dbt
Omni syncs its model to git with pull requests and writes back to dbt. Sigma has version tags and APIs, so git review is something you build.
03
Spreadsheet depth
Both have spreadsheet views. In Sigma the grid is the main way to work. In Omni, spreadsheet tabs cannot feed other queries or the shared model.

Pricing at a glance

Omni Analytics

Omni Analytics, Inc. (independent, venture-backed; Series C led by ICONIQ, April 2026)

Browser-based BI platform built around a code-defined, git-versioned semantic model, with point-and-click, spreadsheet, SQL and AI ways of querying the warehouse. Founded by former Looker team members and aimed at data teams that want governed self-service.

Pricing model: Per user (seat-based annual contract, quoted by sales)
Published starting price: Not published
Free tier: No free tier; free trial offered (length not stated)
What drives cost: Omni has no pricing page. The AWS Marketplace listing quotes by user count on 12-, 24- and 36-month terms. Third-party buyer data from Vendr (not verified by Omni) puts the median annual contract at about $28k, with per-seat estimates around $50-100/user/month. AI usage is tracked in credits.

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 Omni Analytics
Feature	Sigma Computing	Omni Analytics
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 Queries run against the connected warehouse, with an optional results cache. SQL tabs run directly on the database. 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	Yes Spreadsheet tabs support Excel-style cell formulas and sheet references, and stay linked to live query results. Some syntax differs from Excel. source
SQL editor Write SQL against the warehouse inside the product.	Yes Custom SQL elements in workbooks and data models. source	Yes Raw SQL tabs in the database dialect, which bypass the model. 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	Demoed, not GA An early Python notebook and the OmniFrames dataframe library were demoed in Aug 2026. No GA documentation found. 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	Yes Data input tables and editable input columns write to a scratch schema in the warehouse. Needs a scratch schema with writebacks enabled. 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 Shared semantic model with topics, plus a dbt semantic layer integration. source

Sharing and governance

Sharing and governance: Sigma Computing and Omni Analytics
Feature	Sigma Computing	Omni Analytics
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	Yes Signed-URL iframe embedding for external users, with user attributes driving row filtering. Embedded apps were in beta as of Aug 2026. 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 Schedules and conditional alerts to email, Slack, Google Sheets, S3, SFTP or a webhook. 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 filters limit rows by user attribute. 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 Dashboards download as XLSX, CSV (zipped per tile), PDF or PNG. 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 Model-level git sync with GitHub, GitLab, Bitbucket or Azure DevOps. Requires branch mode; pull requests can be enforced. source

Platform

Platform: Sigma Computing and Omni Analytics
Feature	Sigma Computing	Omni Analytics
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 Omni Agent chat, workbook agent, chat with dashboards, routines, skills and an MCP server. AI is metered in credits. source
Open source / self-host An open-source or self-hosted option exists.	No SaaS only, hosted on AWS, Azure or GCP regions. source	No Hosted service only (region selectable). Absence of self-hosting stated by a competitor's comparison page; Omni's deployment guide lists only hosted instance patterns. 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 Sold as SaaS and authored in the browser. source

Where the logic lives

Omni was started by people from Looker, and it shows. The center of the product is a shared semantic model that the data team defines, reviews in git, and syncs with dbt. Point-and-click, spreadsheet, and SQL views all sit on top of it.

Sigma also has data models with reusable metrics and row-level security, but the center of the product is the workbook. Many teams let analysts build logic directly in the spreadsheet grid and promote what proves useful into a data model later.

The spreadsheet experience

Both products have spreadsheet-style editing on live warehouse data. In Omni, spreadsheet tabs support Excel-style cell formulas, but results stay in that tab: they cannot feed other queries or be promoted to the shared model, and pivots and in-sheet charts are not supported.

In Sigma, the grid is the main way of working. Formulas, grouping, pivots, and charts all build on each other in the same workbook, which is why finance teams who live in Excel tend to adopt it faster.

Version control and dbt

This is where Omni is clearly ahead. Its model syncs to GitHub, GitLab, Bitbucket, or Azure DevOps, with branches and optional required pull requests, and it can write descriptions and models back to dbt.

Sigma offers version tags and APIs. Teams that want git review of BI content have to build that pipeline themselves.

Write-back

Both can save user input to the warehouse. Omni input columns are scoped to one workbook and cannot be promoted to the shared model. Sigma input tables are a core feature and are widely used for forecasting, approvals, and adjustments, though they are not supported on every database.

Pricing

Neither publishes prices. Third-party buyer data suggests Omni contracts tend to be smaller than Sigma contracts, but both are quoted by sales on your seat mix. Both run queries on your warehouse, so add that compute to either quote.

Where Omni Analytics is better

Git workflow for the model: branch mode, optional required pull requests, and support for GitHub, GitLab, Bitbucket and Azure DevOps. Omni's own comparison says Sigma offers only manual version tagging (vendor claim).
Two-way dbt integration: can push descriptions and exposures to dbt and author dbt models from Omni, where Omni says Sigma mostly pulls metadata in (vendor claim).
MCP server lets Claude, ChatGPT, Cursor, VS Code and Copilot query governed Omni data with the same permissions as in-app queries.
One workbook can mix point-and-click queries, Excel-style spreadsheet tabs with cell references, and raw SQL tabs, so users can switch approach without rebuilding.
AI features beyond Q&A: scheduled AI routines, saved skills, a modeling agent and evals.

Who should pick which

Pick Omni Analytics if

Your data team wants the semantic model in git with pull-request review.
You use dbt heavily and want changes to flow both ways between dbt and BI.
You are moving off Looker and want a familiar modeling approach.
You want AI clients like Claude or ChatGPT to query governed metrics through MCP.

Pick Sigma Computing if

Finance and operations users will build most of the analysis themselves in a spreadsheet.
Reports need to collect input that is saved to the warehouse and used elsewhere.
You want SQL, Python, and the spreadsheet grid in one workbook today.
You want planning and forecasting workflows in the same tool as reporting.

Recommendations by team

If you are	Pick	Why
Analytics engineering team on dbt	Omni Analytics	Git-reviewed model and two-way dbt sync.
Finance or FP&A team	Sigma Computing	A full spreadsheet on warehouse data and input tables for planning.
Team leaving Looker	Omni Analytics	A similar modeling approach from people who built Looker.
Operations team collecting approvals or adjustments	Sigma Computing	Input tables are a core, widely used feature.
Team wiring AI clients to governed metrics	Omni Analytics	MCP access to the same model and permissions the app uses.

Recent changes to Omni Analytics

2025-10 Acquired embedded-analytics vendor Explo. The Explo platform will run for 12 months while customers migrate to Omni.source
2026-04 Raised a $120M Series C at a $1.5B valuation, led by ICONIQ. Omni reports 4x year-over-year revenue growth.source
2026-04 Positioned the semantic model as context for AI, including MCP access from Claude, ChatGPT, Cursor and VS Code.source
2026-08 Demoed an early Python notebook (OmniFrames), write-back apps, a Metabase-to-Omni migration tool, and embedded apps in beta.source

Questions people ask

Does Sigma replace Omni, or the reverse?

They solve the same problem from different ends. Choose based on who builds most of the logic: a data team that wants code review (Omni) or business users working in a spreadsheet (Sigma).

Is Omni a Looker replacement?

It is a natural choice for teams leaving Looker, because the modeling approach is similar and the founders came from Looker. Sigma is also used to replace Looker, usually when business users want more freedom than a model-first tool gives them.

Do Sigma and Omni both query the warehouse live?

Yes. Both run queries against the connected warehouse. Omni adds an optional results cache.

Does Omni have Python?

Omni demoed an early Python notebook in August 2026, but we found no generally available documentation for it. Sigma supports Python in workbooks on Snowflake and Databricks connections.

Sources and corrections

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