Softwr

Business Intelligence · head to head

Cube vs Sigma Computing

Cube logo

Cube

Business Intelligence

AI-native analytics platform with semantic layer and governed access

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Only Cube has a free tier, so it costs nothing to try first.
  • Each has a real cost: Cube per-developer licensing can be expensive for large analytics teams; 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.
  • They diverge on capability: Cube covers Analytics Chat, Sigma Computing covers Spreadsheet interface over SQL.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Cube and Sigma Computing differ
AttributeCubeSigma Computing
Starting priceFreeOn request
Pricing modelPer-developer seats with volume pricingquote
Free tierYesNo
PlatformsCloud, Self-hostedWeb
CategoryBusiness IntelligenceSpreadsheets

Identical on both: 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 Cube

  • Analytics Chat
  • Dashboards
  • Embedded Analytics
  • AI Integrations
  • Core Data APIs
  • Caching and pre-aggregations

Only in Sigma Computing

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

Both cover

  • Workbooks

What people use each for

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

Cube

  • Building governed semantic data models for analyticsnot Sigma Computing
  • Embedding analytics into customer-facing productsnot Sigma Computing
  • Enabling natural language data queries for teamsnot Sigma Computing
  • Creating conversational dashboards with AI assistancenot Sigma Computing

Sigma Computing

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

Where each one falls short

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

Cube

  • Per-developer licensing can be expensive for large analytics teams
  • Additional cost for Explorer and Viewer roles beyond developers
  • Semantic layer approach requires upfront modeling investment
  • Smaller connector ecosystem than dedicated BI platforms
  • May be overengineered for simple reporting needs

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.

Pricing, plan by plan

Cube

Free
  • FreeFree
    • For hobbyists and personal projects
    • Basic data source connection
    • Semantic modeling
  • Starter$40/developer/month
    • Extended agent limits
    • Premium LLMs
    • Unlimited workbooks
  • Premium$80/developer/month
    • All Starter features
    • Embedded dashboards
    • Embedded analytics chat
  • Enterprise$null/custom
    • 99.990% uptime SLA
    • Dedicated single-tenant installation
    • Bring Your Own Cloud

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

Which should you pick?

Choose Cube if

  • You need analytics chat.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want dashboards.
Cube review

Choose Sigma Computing if

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

Questions people ask

Is Cube or Sigma Computing better?
Neither clearly leads. Cube starts at Free and Sigma Computing at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Cube or Sigma Computing?
Cube has a free tier; the other does not. Paid plans start at Free for Cube and On request for Sigma Computing.
Does Cube or Sigma Computing run on more platforms?
Cube runs on Cloud, Self-hosted. Sigma Computing runs on Web.
Can I use Cube for free?
Yes. Cube has a free tier, so you can try it without paying. Sigma Computing starts at On request.
What is Cube best used for?
Cube is most often used for building governed semantic data models for analytics, embedding analytics into customer-facing products, enabling natural language data queries for teams, creating conversational dashboards with ai assistance. Of those, building governed semantic data models for analytics and embedding analytics into customer-facing products are not what Sigma Computing is typically brought in for.
What can Cube do that Sigma Computing cannot?
Cube covers Analytics Chat, Dashboards, Embedded Analytics, AI Integrations. Sigma Computing covers Spreadsheet interface over SQL, No extract layer, Input tables and write-back, Embedded analytics. Both handle Workbooks.

Answered from the vendors’ own pages

Cube: What is the cost per developer on Cube?

Starter plan costs $40/developer/month. Premium adds embedded analytics at $80/developer/month. Explorer and Viewer roles cost $40 and $20/month respectively.

Source
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.

Cube: What uptime SLAs does Cube offer?

Premium plan offers 99.950% uptime SLA. Enterprise plan provides 99.990% uptime SLA with dedicated support.

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.

Cube: Can I use Cube for free?

Yes, the Free plan includes basic data source connection, semantic modeling, workbooks, and dashboards for hobbyists and personal projects.

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.

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.

Share

Related pages

More on Cube

More on Sigma Computing

Keep looking

Other head to heads