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Machine Learning · head to head

BigQuery ML vs Databox

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

From
Free
Rated
-
Full reviewWebsite
Databox logo

Databox

Machine Learning

KPI dashboards that pull from the tools you already run on

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; Databox data sources are the billing unit, so cost tracks the number of tools you connect rather than the value you get from them
  • They diverge on capability: BigQuery ML covers SQL-based ML, Databox covers Pre-built dashboards.
  • Prices and features above were last checked on 25 September 2026.

Where they differ

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

Attributes where BigQuery ML and Databox differ
AttributeBigQuery MLDatabox
Pricing modelusage-basedsubscription
PlatformsWebWeb, Mobile, Tv
CategoryMachine LearningUnknown
Founded20082012

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

  • Pre-built dashboards
  • Dashboard designer
  • Metric library
  • Goal tracking
  • Scorecards
  • Alerts
  • Scheduled reports
  • Client reporting

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

Databox

  • Watching marketing, sales and finance KPIs from separate SaaS tools on one shared dashboardnot BigQuery ML
  • Agencies reporting campaign performance to many clients without rebuilding a report per accountnot BigQuery ML
  • Putting a live KPI board on an office TV or a recurring email to a leadership teamnot BigQuery ML
  • Replacing a manually maintained spreadsheet that someone updates from tool exports each weeknot BigQuery ML
  • Tracking goals and getting alerted when a metric moves past a thresholdnot 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

Databox

  • Data sources are the billing unit, so cost tracks the number of tools you connect rather than the value you get from them
  • The $71 Analyst plan is a single seat: adding even a second person means the $199 Core plan, a 2.8x jump
  • AI credits are metered monthly, from 50 on Free to 1,000 on Scale, so heavier AI use pushes you up a tier
  • Every published price assumes annual billing; monthly billing forfeits the advertised 20% saving
  • Free and Analyst plans sync daily and hourly respectively, so neither suits anything close to real-time monitoring
  • It is a dashboarding layer, not a warehouse: there is no transformation or modelling step for messy source data

Pricing, plan by plan

BigQuery ML

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

Databox

Free
  • FreeFree
    • 3 data sources
    • 1 user
    • 50 AI credits per month
  • Analyst$71/month
    • 5 data sources
    • 1 user
    • 150 AI credits per month
  • Team - Core$199/month
    • 10 data sources
    • 3 users
    • 500 AI credits per month
  • Team - Scale$319/month
    • 30 data sources
    • 10 users
    • 1,000 AI credits per month

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 Databox if

  • You need pre-built dashboards.
  • You want to start without paying.
  • You work on Web, Mobile, Tv.
  • You also want dashboard designer.
Databox review

Questions people ask

Is BigQuery ML or Databox better?
Neither clearly leads. BigQuery ML starts at Free and Databox at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery ML or Databox?
BigQuery ML starts at Free and Databox at Free.
Does BigQuery ML or Databox run on more platforms?
BigQuery ML runs on Web. Databox runs on Web, Mobile, Tv.
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 Databox is typically brought in for.
What can BigQuery ML do that Databox cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Databox covers Pre-built dashboards, Dashboard designer, Metric library, Goal tracking.

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
Databox: How much does Databox cost?

Databox has a free forever plan with 3 data sources and 1 user. Paid plans are Analyst at $71 per month, Team Core at $199 per month and Team Scale at $319 per month, with an Agency plan from $79 per month plus $20 client packs and a Custom tier on request. All prices are billed annually; monthly billing forfeits the 20% annual saving.

Source
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
Databox: Does Databox have a free plan or a free trial?

Both. The Free plan is free forever and covers 3 data sources, 1 user, 50 AI credits a month and daily syncing. Paid plans also offer a 14-day free trial with no credit card required.

Source
Databox: How many users does each Databox plan include?

Free and Analyst are single-user. Team Core includes 3 users and Team Scale includes 10. The Agency and Custom plans include unlimited users.

Source
Databox: How many tools does Databox integrate with?

Databox connects to more than 130 tools, spreadsheets, databases and APIs, including HubSpot, Salesforce, Google Analytics, Google and Facebook Ads, Shopify, Stripe, QuickBooks, Google Sheets and custom API sources.

Source
Databox: How often does Databox refresh its data?

The Free plan syncs daily. Analyst, Team Core, Team Scale, Agency and Custom plans all sync hourly.

Source
Databox: Who makes Databox?

Databox, Inc., founded in 2012 by Davorin Gabrovec, who is now President and Chief Product Officer. Pete Caputa became CEO in 2017 after a decade at HubSpot. The company is headquartered in Boston, Massachusetts, with its product and engineering team based in Ptuj, Slovenia.

Source
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