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Machine Learning · head to head

BigQuery ML vs JMP

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

From
Free
Rated
-
Full reviewWebsite
JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

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; JMP it is a desktop application holding the working table in memory, so a data set that outgrows the workstation has no in-place upgrade path, only a move to a different tool and a different skill set.
  • They diverge on capability: BigQuery ML covers SQL-based ML, JMP covers Custom design of experiments.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where BigQuery ML and JMP differ
AttributeBigQuery MLJMP
Pricing modelusage-basedsubscription
PlatformsWebMac, Windows
Founded20081976

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

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 JMP

  • Custom design of experiments
  • Linked interactive graphics
  • Analysis platforms
  • Quality and process tools
  • Graph Builder
  • JSL scripting
  • Scoring code export
  • Predictive modelling in JMP Pro

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

JMP

  • Planning a physical experiment where each run is expensive, and the question is which twelve runs to perform rather than how to model data you already havenot BigQuery ML
  • Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot BigQuery ML
  • Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot BigQuery ML
  • Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot 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

JMP

  • It is a desktop application holding the working table in memory, so a data set that outgrows the workstation has no in-place upgrade path, only a move to a different tool and a different skill set.
  • There is no Linux build and no server edition for running analyses, so JMP cannot sit in a scheduled pipeline the way an R or Python script can, and recurring reports depend on a named person running them on a laptop.
  • The predictive modelling capability most buyers mean when they call this machine learning software is in JMP Pro, a separate and more expensive licence, so the base product's price is not the price of the thing being evaluated.
  • JSL is proprietary to JMP, so the scripts, add-ins and automation a group accumulates over a decade do not port anywhere and become sunk cost the moment anyone questions the renewal.
  • Deployment ends at exported scoring code with no registry, monitoring or retraining, so a model that runs in production is maintained by another team in another language and steadily diverges from the version the analyst still has open.

Pricing, plan by plan

BigQuery ML

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

JMP

Free
  • TrialFree
    • 30-day trial
    • Full features
  • JMP$1785/year
    • Core JMP
    • Standard features

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 JMP if

  • You need custom design of experiments.
  • You want to start without paying.
  • You work on Mac, Windows.
  • You also want linked interactive graphics.
JMP review

Questions people ask

Is BigQuery ML or JMP better?
Neither clearly leads. BigQuery ML starts at Free and JMP at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery ML or JMP?
BigQuery ML starts at Free and JMP at Free.
Does BigQuery ML or JMP run on more platforms?
BigQuery ML runs on Web. JMP runs on Mac, Windows.
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 JMP is typically brought in for.
What can BigQuery ML do that JMP cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools.

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
JMP: Is JMP the same thing as SAS?

No. JMP is a separate desktop product from a SAS subsidiary, with its own interface, its own scripting language and its own licence. Knowing SAS does not transfer to it beyond the statistics.

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
JMP: Do I need JMP Pro?

If you want cross validation, penalised regression, boosted trees or neural networks, yes. The base edition covers classical statistics, graphics and design of experiments well and stops short of predictive modelling.

JMP: Does it run on Linux?

No. Windows and macOS only, as an installed application.

JMP: Can I put a JMP model into production?

Only by exporting the scoring formula as SQL, C, Python or similar and running it in another system. JMP itself does not serve, monitor or retrain models.

JMP: Who actually uses it?

Process and quality engineers, and scientists in R&D, particularly in semiconductor, chemicals, pharmaceutical and medical device work. It is not usually chosen by data engineering or platform teams.

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