Mode vs Sigma Computing: Notebooks vs Spreadsheet BI (2026)

Mode and Sigma Computing both sit between raw data and business decisions, but they serve different people in fundamentally different ways. Mode is a SQL notebook platform for analysts who think in queries and need Python for the hard parts. Sigma is a spreadsheet interface that runs directly on your cloud warehouse for business users who think in formulas and pivot tables. The choice is rarely about features — it's about who in your organization needs to touch the data.

How do they compare at a glance?

Who should use which?

    Who needs to analyze the data?
    |
    +-- Analysts who write SQL daily
    |   +-- Also need Python (stats, ML, custom transforms)?
    |   |   └── Mode (SQL + Python notebooks)
    |   └── Pure SQL, want report-style output?
    |       └── Mode (multi-query reports)
    |
    +-- Business users who live in Excel/Sheets
    |   +-- Data is in a cloud warehouse?
    |   |   └── Sigma (spreadsheet on warehouse)
    |   └── Data is in Postgres/MySQL/files?
    |       └── Sigma won't work — consider Metabase or Fastero
    |
    +-- Mixed team (analysts + business users)
    |   +-- Can afford two tools?
    |   |   └── Mode for analysts + Sigma for business users
    |   └── Need one tool for everyone?
    |       └── Neither is ideal — consider Fastero or Power BI
    |
    +-- Nobody writes SQL or formulas
        └── Neither — consider Fastero (AI agent) or ThoughtSpot (search)

The query interface gap

This is the fundamental difference, and everything else flows from it.

Mode gives you a SQL editor. You write SELECT, FROM, WHERE, GROUP BY. You use CTEs, window functions, subqueries — the full SQL dialect your warehouse supports. You can chain multiple queries in a single report, referencing earlier results in later queries. The output is a result set that you visualize with Mode's chart builder or process further in a Python notebook.

This is powerful. There is no ceiling on what you can express. But it requires someone who can write SQL, which immediately narrows your user base to the data team.

Sigma gives you a spreadsheet. You see columns and rows. You write formulas like Sum([Revenue]) or CountIf([Status], "paid"). You drag columns to create groupings. You right-click to add calculated fields. The spreadsheet runs queries against your warehouse under the hood, but the user never sees SQL.

This is accessible. Anyone who has used Excel can build analyses in Sigma within an hour. But the formula language is less expressive than SQL — no recursive CTEs, no complex window functions, no arbitrary subqueries. For the 90% of business questions that are aggregations, filters, and groupings, Sigma's formulas are enough. For the 10% that require real SQL gymnastics, they aren't.

Python: Mode's clear advantage

Mode has Python notebooks attached to its SQL reports. Write a SQL query, get a result set, pass it to a Python notebook as a DataFrame, and do whatever pandas, scikit-learn, or matplotlib can do. Statistical tests, regression models, custom visualizations, text processing — the Python ecosystem is available.

Sigma has no Python support. The spreadsheet formula language is the ceiling. If your analysis requires k-means clustering, time-series decomposition, or anything beyond what spreadsheet formulas can express, Sigma can't do it. You'd need to export data to a Jupyter notebook or another tool — breaking the "everything in one place" promise.

For analytics teams that regularly blend SQL with Python — and in 2026, most serious analytics teams do — this is Mode's strongest advantage.

Collaboration and sharing

Mode's model: reports. A Mode report bundles queries, charts, and markdown into a shareable URL. Stakeholders open the link and see charts and narrative. They can re-run the queries to get fresh data. Comments and annotations live on the report. This is a document-centric model — the deliverable is a report, not a live workspace.

Sigma's model: workbooks. A Sigma workbook is a live spreadsheet that multiple people can edit simultaneously. Think Google Sheets, but backed by a warehouse. Business users can open a workbook, change filters, add columns, pivot data — all without touching the analyst's original work. Version history tracks every change.

The practical difference: Mode reports are read-mostly artifacts that analysts produce and stakeholders consume. Sigma workbooks are read-write artifacts that anyone can modify. If your workflow is "analyst builds, stakeholder consumes," Mode fits. If your workflow is "analyst sets up the workbook, business user customizes it for their needs," Sigma fits.

Embedded analytics

Both tools support embedding analytics into external products, but the approaches differ.

Mode offers API access and iframe-based embedding. You can embed individual reports or charts in your product. The API lets you programmatically trigger report runs and retrieve results. It works, but the embedding experience is report-shaped — your users see a Mode report inside your product.

Sigma embeds workbooks with full interactivity. Your product's users can filter, sort, pivot, and explore within the embedded workbook. Row-level security controls what each user sees. The spreadsheet interaction model translates well to embedded contexts — users expect to interact with tables and charts in a product.

For products where end users need to explore data (multi-tenant SaaS dashboards, customer portals), Sigma's interactive embedding is stronger. For products where you're presenting analytical results (reports, summaries), Mode's approach is sufficient.

The ThoughtSpot factor

Mode was acquired by ThoughtSpot in 2023. This matters.

Product development has shifted toward ThoughtSpot integration features rather than Mode-standalone improvements. Python support, the SQL editor, and the notebook experience haven't seen major updates recently. If you're evaluating Mode as an independent product, you should be comfortable that in 2-3 years, "Mode" might just be a tab inside ThoughtSpot.

Sigma is independent (Series D, $810M valuation as of 2025). Product direction is focused on the spreadsheet-on-warehouse vision. New features — like the AI formula assistant and improved materialization — serve Sigma's own roadmap, not an acquirer's strategy.

For long-term platform bets, Sigma's independence is an advantage. For organizations already using ThoughtSpot, Mode's integration is a feature, not a bug.

Pricing

Both tools land in the same ballpark — roughly $35-50/user/month — but the buyer profile is different.

Mode at ~$35/user/month is typically purchased for analyst teams of 5-30 people. Business stakeholders view reports for free (viewer seats). The cost scales with the number of analysts, not the number of consumers.

Sigma at ~$35-50/user/month (custom pricing) often has a broader deployment. Because business users are the primary audience, the seat count tends to be higher. A 10-person analytics team using Mode might cost $350/month. A 50-person department using Sigma might cost $2,000+/month. Same per-seat price, very different total cost based on who uses it.

When neither tool is quite right

Mode and Sigma represent two ends of a spectrum: code-first (Mode) vs spreadsheet-first (Sigma). Teams that don't fit neatly on either end — where business users need answers but nobody wants to learn formulas or SQL — increasingly look at AI-driven alternatives.

Fastero takes a different approach entirely: describe what you want to understand in plain English, and an AI agent writes the queries, interprets results, and delivers analyses with charts. No SQL notebooks, no spreadsheet formulas — the agent handles the analytical work. This matters most for cross-source analysis (databases + files + APIs), where neither Mode's SQL editor nor Sigma's warehouse-only spreadsheet can query across sources without pre-built pipelines.

FAQ

Can I use Mode and Sigma together?

Yes, and some organizations do — Mode for the analyst team (deep SQL + Python work) and Sigma for business users (self-serve spreadsheet exploration). The cost of two tools is the obvious downside. The upside is that each group gets the interface designed for them, and neither compromises.

Does Mode work without a cloud warehouse?

Yes. Mode connects to any SQL database — Postgres, MySQL, Redshift, BigQuery, Snowflake, and more. It doesn't require a cloud warehouse. Sigma requires Snowflake, BigQuery, or Databricks specifically.

Is Sigma's formula language as powerful as SQL?

For aggregations, filters, and groupings — roughly equivalent. For complex analytical queries (recursive CTEs, lateral joins, advanced window functions), SQL is more expressive. Sigma does support writing raw SQL in custom SQL data sources, but the primary experience is formula-driven.

Which tool is better for embedded analytics?

Sigma, for most use cases. The interactive spreadsheet embeds well — end users can filter, sort, and explore. Mode's embedding is more static, suited for presenting pre-built reports. See our embedded analytics guide for a broader comparison.

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