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BigQuery ML vs Sigma Computing

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

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 BigQuery ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; 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: BigQuery ML covers SQL-based ML, 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 BigQuery ML and Sigma Computing actually diverge.

Attributes where BigQuery ML and Sigma Computing differ
AttributeBigQuery MLSigma Computing
Starting priceFreeOn request
Pricing modelusage-basedquote
Free tierYesNo
CategoryMachine LearningSpreadsheets
Founded2008Unknown

Identical on both: 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 BigQuery ML

  • SQL-based ML
  • AutoML Tables
  • Model export
  • Prediction functions
  • Feature preprocessing
  • BigQuery
  • Vertex AI
  • TensorFlow

Only in Sigma Computing

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

What people use each for

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

BigQuery ML

  • Training models in SQL without exporting datanot Sigma Computing
  • Linear and logistic regression on warehouse datanot Sigma Computing
  • K-means clustering and matrix factorisation for recommendationsnot Sigma Computing
  • Time series forecasting with ARIMA_PLUSnot Sigma Computing
  • Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Sigma Computing

Sigma Computing

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

Where each one falls short

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

BigQuery ML

  • Not available in BigQuery's Standard edition, so the cheapest tier cannot use it
  • Billed through BigQuery compute and storage rather than as its own product, so training cost tracks data scanned
  • Remote models incur extra Agent Platform charges on top
  • Externally trained model types such as boosted trees and AutoML run through Agent Platform rather than inside BigQuery

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

BigQuery ML

Free
  • Free TierFree
    • 10GB storage
    • 1TB queries
  • On-Demand$5/TB
    • Pay per TB scanned
    • ML training costs

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 BigQuery ML if

  • You need sql-based ml.
  • You want to start without paying.
  • You also want automl tables.
BigQuery ML review

Choose Sigma Computing if

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

Questions people ask

Is BigQuery ML or Sigma Computing better?
Neither clearly leads. BigQuery ML 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, BigQuery ML or Sigma Computing?
BigQuery ML has a free tier; the other does not. Paid plans start at Free for BigQuery ML and On request for Sigma Computing.
Does BigQuery ML or Sigma Computing run on more platforms?
Both run on Web, so platform support will not decide this one for you.
Can I use BigQuery ML for free?
Yes. BigQuery ML has a free tier, so you can try it without paying. Sigma Computing starts at On request.
What is BigQuery ML best used for?
BigQuery ML is most often used for training models in sql without exporting data, linear and logistic regression on warehouse data, k-means clustering and matrix factorisation for recommendations, time series forecasting with arima_plus. Of those, training models in sql without exporting data and linear and logistic regression on warehouse data are not what Sigma Computing is typically brought in for.
What can BigQuery ML do that Sigma Computing cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Sigma Computing covers Spreadsheet interface over SQL, No extract layer, Input tables and write-back, Workbooks.

Answered from the vendors’ own pages

BigQuery ML: How much does Google Cloud BigQuery ML cost?

BigQuery ML pricing is not specified separately on Google Cloud's pricing page. It follows the same pay-as-you-go model as BigQuery, charging per terabyte of data scanned during analysis. Customers receive $300 in free credits and can use 20+ products free up to monthly limits.

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.

BigQuery ML: Does Google Cloud offer a free trial?

Yes, new customers get $300 in free credits and all customers can use 20+ Google Cloud products free up to their monthly usage limits.

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.

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.

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