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BigQuery ML vs Google Vertex AI

BigQuery ML logo

BigQuery ML

Software

Machine learning in BigQuery using SQL

From
Free
Rated
-
Full reviewWebsite
Google Vertex AI logo

Google Vertex AI

Software

Unified ML platform to build, deploy, and scale AI models

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; Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
  • They diverge on capability: BigQuery ML covers SQL-based ML, Google Vertex AI covers AutoML.

Where they differ

Only the attributes on which BigQuery ML and Google Vertex AI actually diverge.

Attributes where BigQuery ML and Google Vertex AI differ
AttributeBigQuery MLGoogle Vertex AI
Starting priceFreeOn request
Pricing modelusage-basedUnknown
Free tierYesNo
PlatformsWebCloud, Web

Identical on both: user rating (Not yet rated), category (Unknown), founded (2008).

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
  • Vertex AI

Only in Google Vertex AI

  • AutoML
  • Custom training
  • Feature Store
  • Model monitoring
  • Prediction serving
  • PyTorch
  • Dataflow

Both cover

  • BigQuery
  • TensorFlow
  • Cloud Storage
  • Web support

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

Google Vertex AI

  • Machine learningnot BigQuery ML
  • Data analysisnot BigQuery ML
  • Model trainingnot BigQuery ML
  • Predictive analyticsnot 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

Google Vertex AI

  • Vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
  • Requires familiarity with Google Cloud Platform infrastructure and concepts
  • Cost can escalate quickly with large training and inference workloads

Pricing, plan by plan

BigQuery ML

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

Google Vertex AI

On request

No published plan breakdown. See the Google Vertex AI 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 Google Vertex AI if

  • You need automl.
  • You work on Cloud, Web.
  • You also want custom training.
Google Vertex AI review

Questions people ask

Is BigQuery ML or Google Vertex AI better?
Neither clearly leads. BigQuery ML starts at Free and Google Vertex AI at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery ML or Google Vertex AI?
BigQuery ML has a free tier; the other does not. Paid plans start at Free for BigQuery ML and On request for Google Vertex AI.
Does BigQuery ML or Google Vertex AI run on more platforms?
BigQuery ML runs on Web. Google Vertex AI runs on Cloud, Web.
Can I use BigQuery ML for free?
Yes. BigQuery ML has a free tier, so you can try it without paying. Google Vertex AI 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 Google Vertex AI is typically brought in for.
What can BigQuery ML do that Google Vertex AI cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring. Both handle BigQuery, TensorFlow, Cloud Storage, Web support.

Answered from the vendors’ own pages

Google Vertex AI: What is the pricing model for Google Vertex AI?

Vertex AI uses a pay-as-you-go model with no upfront costs or lock-in fees. Costs vary by service: training is billed by compute resources and time (30-second increments), online predictions by machine type per hour, and batch predictions by compute time or per-record for specific AutoML types.

Source
Google Vertex AI: What types of data can Vertex AI handle?

Vertex AI supports image, video, text, and tabular data types with tools for uploading, storing, and managing large datasets.

Source
Google Vertex AI: Does Vertex AI support custom model training?

Yes. Vertex AI supports both AutoML for automated machine learning and custom training code in Python, R, and other languages.

Source
Google Vertex AI: What deployment options are available in Vertex AI?

Vertex AI supports online predictions for real-time use cases and batch predictions for large-scale processing.

Source
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