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Spreadsheets · head to head

Sigma Computing vs Zenlytic

Sigma Computing logo

Sigma Computing

Spreadsheets

Spreadsheet-style analytics that queries a cloud data warehouse directly with no extract layer

From
On request
Rated
-
Full reviewWebsite
Zenlytic logo

Zenlytic

Business Intelligence

Conversational analytics on a governed semantic layer that shows you the SQL it ran

From
On request
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Sigma Computing every user interaction is a live warehouse query, so compute costs rise with adoption and land on the warehouse invoice rather than the BI line item, which routinely surprises the team that approved the purchase.; Zenlytic answer quality is bounded by the semantic model, so a team without a maintained dbt project and metric definitions must complete a modelling project before the AI is useful at all.
  • They diverge on capability: Sigma Computing covers Spreadsheet interface over SQL, Zenlytic covers Zoe conversational agent.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Sigma Computing and Zenlytic actually diverge.

Attributes where Sigma Computing and Zenlytic differ
AttributeSigma ComputingZenlytic
CategorySpreadsheetsBusiness Intelligence

Identical on both: starting price (On request), pricing model (quote), free tier (No), platforms (Web), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Sigma Computing

  • Spreadsheet interface over SQL
  • No extract layer
  • Input tables and write-back
  • Workbooks
  • Version control and dbt awareness

Only in Zenlytic

  • Zoe conversational agent
  • YAML semantic layer
  • LookML import
  • Row and column permissions
  • Dashboards
  • dbt integration
  • Query transparency

Both cover

  • Embedded analytics

What people use each for

The jobs each tool is most often brought in to do.

Sigma Computing

  • A finance or operations team that lives in spreadsheets and needs warehouse-scale data without exporting CSVsnot Zenlytic
  • An organisation that has consolidated on Snowflake or Databricks and wants one governed access layer rather than several desktop toolsnot Zenlytic
  • Planning and scenario work where users need to type assumptions back into governed tables rather than into a local filenot Zenlytic
  • Embedding customer-facing analytics into a product where each tenant must only see their own rowsnot Zenlytic

Zenlytic

  • A data team of three fielding sixty ad hoc requests a week from operators who could answer them in chat insteadnot Sigma Computing
  • An ecommerce company where merchandisers need cohort and margin questions answered without waiting for an analystnot Sigma Computing
  • A Looker customer looking for an exit that can import existing LookML rather than remodel from scratchnot Sigma Computing
  • A finance team that needs every AI-generated number to show its SQL before it goes in a board packnot Sigma Computing

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Sigma Computing

  • Every user interaction is a live warehouse query, so compute costs rise with adoption and land on the warehouse invoice rather than the BI line item, which routinely surprises the team that approved the purchase.
  • It cannot work without a supported cloud data warehouse, so an organisation running analytics on an on-premises database or a plain application database has nothing to connect to.
  • Pricing is not published and seats are differentiated by capability, so budgeting requires a sales cycle and a headcount forecast before you can compare it with a tool you could simply buy.
  • Performance is only as good as the warehouse behind it, which means slow or badly modelled tables surface as a slow interface and the fix is a data engineering project, not a Sigma setting.
  • Statistical and scientific visualisation is limited compared with dedicated tools, so anything beyond standard business charting has to be done elsewhere and brought back in.

Zenlytic

  • Answer quality is bounded by the semantic model, so a team without a maintained dbt project and metric definitions must complete a modelling project before the AI is useful at all.
  • It is a small venture-funded company in a category that Snowflake, Databricks and Microsoft are all building into natively, which is a real procurement risk on a multi-year contract.
  • Pricing is not published and is negotiated per deal, so small buyers have no benchmark and no leverage.
  • The visualisation and dashboard layer is thinner than established BI tools, so teams wanting pixel control over reporting will find it limiting.
  • It assumes a cloud data warehouse; organisations with data spread across operational databases and spreadsheets have to consolidate first, which is the expensive part of the project.

Pricing, plan by plan

Sigma Computing

On request
  • Sigma Computing$undefined/year
    • Quoted per organisation
    • Seat types are differentiated by whether a user views, explores or authors
    • Warehouse compute is billed separately by your cloud data warehouse vendor

Zenlytic

On request
  • Zenlytic$undefined/year
    • Annual contract quoted by seats and deployment
    • Semantic layer, dashboards and Zoe included
    • Embedded analytics licensed separately

Which should you pick?

Choose Sigma Computing if

  • You need spreadsheet interface over sql.
  • You also want no extract layer.
Sigma Computing review

Choose Zenlytic if

  • You need zoe conversational agent.
  • You also want yaml semantic layer.
Zenlytic review

Questions people ask

Is Sigma Computing or Zenlytic better?
Neither clearly leads. Sigma Computing starts at On request and Zenlytic at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Sigma Computing or Zenlytic?
Sigma Computing starts at On request and Zenlytic at On request.
Does Sigma Computing or Zenlytic run on more platforms?
Both run on Web, so platform support will not decide this one for you.
What is Sigma Computing best used for?
Sigma Computing is most often used for a finance or operations team that lives in spreadsheets and needs warehouse-scale data without exporting csvs, an organisation that has consolidated on snowflake or databricks and wants one governed access layer rather than several desktop tools, planning and scenario work where users need to type assumptions back into governed tables rather than into a local file, embedding customer-facing analytics into a product where each tenant must only see their own rows. Of those, a finance or operations team that lives in spreadsheets and needs warehouse-scale data without exporting csvs and an organisation that has consolidated on snowflake or databricks and wants one governed access layer rather than several desktop tools are not what Zenlytic is typically brought in for.
What can Sigma Computing do that Zenlytic cannot?
Sigma Computing covers Spreadsheet interface over SQL, No extract layer, Input tables and write-back, Workbooks. Zenlytic covers Zoe conversational agent, YAML semantic layer, LookML import, Row and column permissions. Both handle Embedded analytics.

Answered from the vendors’ own pages

Sigma Computing: Does Sigma store a copy of my data?

No. Queries are executed on your warehouse and results are returned for display, which is why there is no extract refresh to manage.

Zenlytic: Does Zenlytic publish pricing?

No. Contracts are quoted annually based on seats and deployment scope.

Sigma Computing: What does it actually cost?

The vendor does not publish prices. Expect an annual contract priced by seat type, plus a warehouse compute increase you should model separately.

Zenlytic: Can it read my Looker model?

Yes, it imports LookML, which is the main reason Looker customers evaluate it.

Sigma Computing: Can business users break the warehouse?

They can run expensive queries. Warehouse sizing, query limits and materialised tables are the controls, and they need configuring before a wide rollout.

Zenlytic: Does it write arbitrary SQL against my warehouse?

No. Queries are constrained to the semantic layer, and the generated SQL is shown with every answer.

Sigma Computing: Is it a replacement for a modelling layer like dbt?

No. Sigma works best on top of modelled tables and is usually deployed alongside dbt rather than instead of it.

Zenlytic: Do I need dbt?

Not strictly, but the product is designed around a modelled warehouse and works poorly without one.

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