Skip to content
Start free
Log inStart free
Competitor comparison

Omni vs Sigma

Both run on the cloud warehouse and both court spreadsheet users, so the choice comes down to whether metrics are governed in a central model or in workbooks.

Quick decision snapshot

Choose Sigma if the people who need data are analysts and operators who live in spreadsheets and want to explore warehouse tables directly in a familiar grid. Choose Omni if you want those same users working on top of a semantic model your data team controls, so a metric cannot quietly drift between workbooks. If what you need is answers in plain language without either a model or a spreadsheet, see the Basedash section.

Closer than most comparisons

Omni and Sigma are genuinely close substitutes. Both connect to warehouses such as Snowflake and BigQuery and query them live, and both offer spreadsheet-like interaction so finance and operations staff do not have to learn SQL. That overlap is why they show up on the same shortlist. The difference is philosophical: Sigma makes the spreadsheet the primary interface and governs through workbook standards, while Omni puts a semantic model underneath and lets the spreadsheet-style workbook sit on top.

Omni: the model comes first

Omni's approach, which draws on Looker's LookML, is to define joins and metrics centrally and have every workbook, dashboard, and AI chat answer inherit them. A revenue figure in one workbook is the same revenue figure in the next, because both read the same definition. Analysts still get Excel-style formulas and SQL in the same workbook, and an AI assistant can draft calculations.

The cost is sequencing. Someone with analytics-engineering skills has to build the model before business users see the benefit, and until then Omni can feel slower to roll out than a tool where people start in a grid on day one. Omni is also the younger company, with a smaller ecosystem around it.

Sigma: the spreadsheet comes first

Sigma's workbook looks and behaves like a spreadsheet, but every operation runs against the warehouse, so users can explore at warehouse scale without writing SQL. People who know Excel are productive quickly, which makes Sigma one of the easier tools to roll out to a wide group of analysts and operators. It also supports embedded analytics for SaaS products alongside internal use.

The risk is the flip side of that freedom. Without a central semantic layer, consistency depends on workbook discipline and calculated columns, and as more teams build their own workbooks, definitions can diverge. On our BI Bench test of AI analyst agents, Sigma was among the fastest at 42.6 seconds per answer but met 35.15% of grading criteria. Omni was not part of that benchmark.

Omni vs Sigma, criterion by criterion

CriterionOmniSigma
Where a metric is definedOnce, in a semantic model the data team ownsIn workbooks and calculated columns, guided by team standards
First experience for a business userExplores a curated model through workbooks and AI chatOpens a grid on live warehouse data and starts working
Drift risk as usage growsLower, because workbooks inherit shared definitionsHigher, workbook sprawl needs active governance
Time before broad rolloutLonger, the model has to exist firstShorter for spreadsheet-comfortable users
AI roleChat and formula help grounded in the modelAvailable, secondary to spreadsheet interaction
Embedded analyticsExpanding through the October 2025 Explo acquisitionOffered for SaaS teams alongside internal BI

Lean toward Omni if

Finance and leadership need one version of each metric across every team.

You have, or plan to hire, someone to own a semantic model.

You want AI answers drawn from the same definitions as dashboards.

Lean toward Sigma if

Your users already think in spreadsheets and want to start working immediately.

Exploration across very large warehouse tables matters more than central definitions.

You can enforce workbook standards as adoption spreads.

For teams that want answers rather than a grid or a model

Omni and Sigma both still expect someone to build: a model in one case, a workbook in the other. Basedash removes that step for the many people who just want the number. They type the question, Basedash shows the SQL it generated next to the chart, and the definitions worth reusing are saved in a built-in semantic layer as plain SQL. On the same BI Bench test where Sigma scored 35.15%, Basedash reached 94.8%, and answered faster too, at 32.6 seconds on average.

Sigma remains the better home for analysts who genuinely want to manipulate rows in a spreadsheet. Omni remains the better home for a data team that wants a LookML-style model as its foundation.

Also compare each against Basedash

FAQ

Our finance team loves spreadsheets. Is Omni or Sigma easier for them?

Sigma is usually the faster win, because its whole interface is a spreadsheet running on the warehouse, and finance staff recognize it immediately. Omni also offers Excel-style formulas in its workbooks, so the gap is smaller than it looks, but finance users in Omni are working on top of a model the data team built. That is an advantage when month-end numbers must reconcile across teams and a drawback if the model does not yet include the tables finance needs. Pilot with a real close process, not a demo dataset.

How do we stop metrics drifting in Sigma, and does Omni avoid the problem?

In Sigma, consistency comes from process: shared source workbooks, naming standards, calculated columns maintained by the data team, and warehouse-level access controls. It works, but it needs ongoing attention as more teams build. Omni addresses drift structurally by defining metrics once in its semantic model, so new workbooks inherit them. The tradeoff is that Omni's model has to be designed and kept current, which is real analytics-engineering work. Pick the tool whose kind of upkeep your team is better staffed for.

Do Omni and Sigma both work with our existing warehouse?

Both are warehouse-native, meaning they query data where it already lives rather than copying it into their own store. Sigma is commonly deployed on Snowflake, BigQuery, and Redshift. Omni is likewise built to sit on a cloud warehouse, and teams with a dbt project often find its modeling approach a comfortable extension of that work. Either way, both tools assume the warehouse is already in place and reasonably clean, so budget for that work if it is not.

When should we consider Basedash instead?

Consider Basedash when most of your users neither want to build a workbook nor wait for a semantic model. Users type what they want to know, the chart comes back with its SQL attached, and the data team curates reusable definitions in a lighter built-in layer. If your SaaS data is not in a warehouse yet, Basedash's 750+ connectors and managed warehouse cover that step too. On BI Bench it was both more accurate and faster than Sigma, which is the only one of the two that was tested.

We can help you migrate your data and dashboards from any other tool.

Start freeBook a demo