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Spreadsheets · head to head

Sigma Computing vs Sisense

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
Sisense logo

Sisense

Business Intelligence

Infuse analytics everywhere

From
$10000/year
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.; Sisense pricing lacks transparency with opaque scaling costs and hidden fees for onboarding and training
  • They diverge on capability: Sigma Computing covers Spreadsheet interface over SQL, Sisense covers Embedded Analytics.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Sigma Computing and Sisense differ
AttributeSigma ComputingSisense
Starting priceOn request$10000/year
Pricing modelquoteUnknown
PlatformsWebWeb, Cloud, On-premises
CategorySpreadsheetsBusiness Intelligence
FoundedUnknown2004

Identical on both: free tier (No), 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
  • Embedded analytics
  • Version control and dbt awareness

Only in Sisense

  • Embedded Analytics
  • AI/ML Integration
  • In-chip Technology
  • White-labeling
  • REST API
  • Snowflake
  • AWS
  • Azure

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 Sisense
  • An organisation that has consolidated on Snowflake or Databricks and wants one governed access layer rather than several desktop toolsnot Sisense
  • Planning and scenario work where users need to type assumptions back into governed tables rather than into a local filenot Sisense
  • Embedding customer-facing analytics into a product where each tenant must only see their own rowsnot Sisense

Sisense

  • Self-service analyticsnot Sigma Computing
  • Data explorationnot Sigma Computing
  • Ad-hoc reportingnot Sigma Computing
  • Collaborative analysisnot Sigma Computing
  • Embedded analyticsnot 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.

Sisense

  • Pricing lacks transparency with opaque scaling costs and hidden fees for onboarding and training
  • Limited connector ecosystem compared to competitors; missing native connectors to many data sources
  • Dashboard customization options are limited; widgets cannot span multiple rows, restricting layout possibilities
  • Performance issues reported with large datasets and stability problems with data cubes

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

Sisense

$10000/year
  • Small Team$10000/year minimum
    • Basic analytics dashboards
    • Limited data sources
  • Mid-Market$undefined/custom
    • Advanced analytics
    • Multiple data sources
    • Custom integrations
  • Enterprise$60000/year+
    • Advanced AI analytics
    • Premium support
    • Custom development

Which should you pick?

Choose Sigma Computing if

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

Choose Sisense if

  • You need embedded analytics.
  • You work on Web, Cloud, On-premises.
  • You also want ai/ml integration.
Sisense review

Questions people ask

Is Sigma Computing or Sisense better?
Neither clearly leads. Sigma Computing starts at On request and Sisense at $10000/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Sigma Computing or Sisense?
Sigma Computing starts at On request and Sisense at $10000/year.
Does Sigma Computing or Sisense run on more platforms?
Sigma Computing runs on Web. Sisense runs on Web, Cloud, On-premises.
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 Sisense is typically brought in for.
What can Sigma Computing do that Sisense cannot?
Sigma Computing covers Spreadsheet interface over SQL, No extract layer, Input tables and write-back, Workbooks. Sisense covers Embedded Analytics, AI/ML Integration, In-chip Technology, White-labeling.

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.

Sisense: What is Sisense primarily used for?

Sisense is an embedded analytics platform that combines data ingestion, modeling, and dashboarding, allowing organizations to embed analytics and insights directly into their applications and workflows.

Source
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.

Sisense: Does Sisense have a transparent pricing model?

Sisense pricing is not publicly listed and requires contacting sales. Typical costs start at $10,000 per year for small teams but can scale to $60,000+ annually depending on users, data volume, number of data sources, and complexity. AI capabilities typically add 20-30% to base costs.

Source
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.

Sisense: What data sources can Sisense connect to?

Sisense provides pre-built connectors for popular applications including Salesforce, Google Analytics, Zendesk, and others. It also supports custom connections through APIs and SDKs for specialized data sources.

Source
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.

Sisense: Is Sisense easy to use for non-technical users?

Sisense requires significant technical expertise to set up, particularly for creating Elasticubes (database caches) which often need SQL code. While it promotes codeless reporting, typical implementations require a technical resource.

Source
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