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Machine Learning · head to head

JMP vs Tableau

JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

From
Free
Rated
-
Full reviewWebsite
Tableau logo

Tableau

Spreadsheets

Visual analytics platform for business intelligence

From
$70/month
Rated
-
Full reviewWebsite

The short version

  • Only JMP has a free tier, so it costs nothing to try first.
  • Each has a real cost: 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.; Tableau pricing not displayed on product homepage; vendors require visit to pricing calculator or sales contact
  • They diverge on capability: JMP covers Custom design of experiments, Tableau covers Interactive Dashboards.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which JMP and Tableau actually diverge.

Attributes where JMP and Tableau differ
AttributeJMPTableau
Starting priceFree$70/month
Free tierYesNo
PlatformsMac, WindowsWeb, Desktop, Mobile
CategoryMachine LearningSpreadsheets
Founded19761999

Identical on both: pricing model (subscription), 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 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

Only in Tableau

  • Interactive Dashboards
  • Data Blending
  • Real-time Analytics
  • Advanced Visualizations
  • Mobile Support
  • Salesforce
  • SAP
  • Oracle

What people use each for

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

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 Tableau
  • Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot Tableau
  • Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot Tableau
  • Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot Tableau

Tableau

  • Business analytics dashboards for sales, finance, and HR departmentsnot JMP
  • Real-time operational KPI monitoring for manufacturing and retailnot JMP
  • Interactive data visualization for market analysis and competitive intelligencenot JMP
  • Self-service analytics for business users without SQL knowledgenot JMP
  • Enterprise data governance and governed analytics deploymentnot JMP

Where each one falls short

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

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.

Tableau

  • Pricing not displayed on product homepage; vendors require visit to pricing calculator or sales contact

Pricing, plan by plan

JMP

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

Tableau

$70/month
  • Creator$70/month
    • Full authoring capabilities
    • Prep Builder
    • Data Management
  • Explorer$42/month
    • Web editing
    • Self-service analytics
  • Viewer$15/month
    • View and interact with dashboards

Which should you pick?

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

Choose Tableau if

  • You need interactive dashboards.
  • You work on Web, Desktop, Mobile.
  • You also want data blending.
Tableau review

Questions people ask

Is JMP or Tableau better?
Neither clearly leads. JMP starts at Free and Tableau at $70/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, JMP or Tableau?
JMP has a free tier; the other does not. Paid plans start at Free for JMP and $70/month for Tableau.
Does JMP or Tableau run on more platforms?
JMP runs on Mac, Windows. Tableau runs on Web, Desktop, Mobile.
Can I use JMP for free?
Yes. JMP has a free tier, so you can try it without paying. Tableau starts at $70/month.
What is JMP best used for?
JMP is most often used for 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 have, process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulator, exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheet, semiconductor, chemical and pharmaceutical development groups where jmp is already the shared language for reporting results. Of those, 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 have and process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulator are not what Tableau is typically brought in for.
What can JMP do that Tableau cannot?
JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools. Tableau covers Interactive Dashboards, Data Blending, Real-time Analytics, Advanced Visualizations.

Answered from the vendors’ own pages

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.

Tableau: How much does Tableau Cloud cost?

Tableau does not list Cloud pricing on its homepage. Customers can use the Tableau pricing calculator or contact sales for rate information.

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.

Tableau: What is the cost of Tableau Server on-premise?

Tableau Server pricing is not published on the product website. Licensing costs are available through the Tableau sales team or pricing calculator.

Source
JMP: Does it run on Linux?

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

Tableau: Does Tableau offer a free or trial version?

Tableau Desktop is available for free download. Tableau Cloud and Server require licensing purchases; pricing is accessed via the calculator or sales contact.

Source
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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