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Machine Learning · head to head

BigQuery ML vs Fabi

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

From
Free
Rated
-
Full reviewWebsite
Fabi logo

Fabi

Business Intelligence

AI notebooks combining SQL, Python and no-code for small data teams

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Fabi fabi is an early-stage company with a small team, so the durability risk is real and there is no obvious migration path for Smartbooks if it stops trading.
  • They diverge on capability: BigQuery ML covers SQL-based ML, Fabi covers Smartbooks.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery ML and Fabi actually diverge.

Attributes where BigQuery ML and Fabi differ
AttributeBigQuery MLFabi
Pricing modelusage-basedPer user per month
CategoryMachine LearningBusiness Intelligence
Founded2008Unknown

Identical on both: starting price (Free), free tier (Yes), 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 Fabi

  • Smartbooks
  • Smart Reports
  • AI analysis
  • Scheduled runs
  • Database connectors
  • Viewer seats

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 Fabi
  • Linear and logistic regression on warehouse datanot Fabi
  • K-means clustering and matrix factorisation for recommendationsnot Fabi
  • Time series forecasting with ARIMA_PLUSnot Fabi
  • Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Fabi

Fabi

  • A single analyst at a startup fielding ad hoc questions faster than dashboards can be built for themnot BigQuery ML
  • An operations team that needs Python for a one-off analysis but has to hand the result to non-technical colleaguesnot BigQuery ML
  • Replacing a set of scheduled Jupyter notebooks that nobody outside the data team can read or rerunnot BigQuery ML
  • Exploratory work against a warehouse where building a semantic model first would cost more than the answer is worthnot 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

Fabi

  • Fabi is an early-stage company with a small team, so the durability risk is real and there is no obvious migration path for Smartbooks if it stops trading.
  • There is no meaningful governance layer: no data catalogue, no certified metric definitions and limited lineage, so it does not scale to an organisation that needs a single agreed number.
  • The free and Builder tiers cap AI requests, and the daily cap on Starter is reached quickly during genuine exploratory work.
  • Connector counts are limited by tier, so the Builder plan at 39 dollars connects to exactly one data source, which is rarely enough in practice.
  • It overlaps heavily with what Snowflake, Databricks and Hex now ship natively, so a company already paying for one of those is buying a fourth notebook interface.

Pricing, plan by plan

BigQuery ML

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

Fabi

Free
  • StarterFree
    • One builder seat
    • Ten dashboard viewers
    • Five Smartbooks
  • Builder$39/month
    • Twenty-five Smart Report viewers
    • Ten Smartbooks with workflows
    • One data connector
  • Team$199/month
    • Four builder seats, extra seats at 39 USD
    • Fifty viewers
    • Premium connector
  • Enterprise$undefined/year
    • Unlimited builder seats
    • Full connector access
    • Custom security review

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 Fabi if

  • You need smartbooks.
  • You want to start without paying.
  • You also want smart reports.
Fabi review

Questions people ask

Is BigQuery ML or Fabi better?
Neither clearly leads. BigQuery ML starts at Free and Fabi at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery ML or Fabi?
BigQuery ML starts at Free and Fabi at Free.
Does BigQuery ML or Fabi 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?
Both have a free tier, so you can try either at no cost before committing.
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 Fabi is typically brought in for.
What can BigQuery ML do that Fabi cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Fabi covers Smartbooks, Smart Reports, AI analysis, Scheduled runs.

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
Fabi: Is there a free plan?

Yes, a Starter tier with one builder, ten viewers, five Smartbooks and ten AI requests a day.

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
Fabi: Do viewers cost money?

Viewers are bundled by tier rather than charged individually; builders are the priced seat.

Fabi: Can I run arbitrary Python?

Yes, Smartbooks include Python cells alongside SQL and no-code steps.

Fabi: How many data connectors does the entry paid plan include?

One. Additional and premium connectors come with the Team tier.

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