Softwr

Machine Learning · head to head

JMP vs PyTorch

JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

From
Free
Rated
-
Full reviewWebsite
PyTorch logo

PyTorch

Machine Learning

Deep learning framework with dynamic computation graphs

From
Free
Rated
-
Full reviewWebsite

The short version

  • 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.; PyTorch dynamic computation graph can be less efficient for production inference than static graphs
  • They diverge on capability: JMP covers Custom design of experiments, PyTorch covers Dynamic computation graphs.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which JMP and PyTorch actually diverge.

Attributes where JMP and PyTorch differ
AttributeJMPPyTorch
Pricing modelsubscriptionUnknown
PlatformsMac, WindowsLinux, Windows, macOS
Founded19762016

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 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 PyTorch

  • Dynamic computation graphs
  • Automatic differentiation
  • GPU acceleration
  • Distributed training
  • TorchScript
  • TorchVision
  • TorchText
  • TorchAudio

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

PyTorch

  • Machine learningnot JMP
  • Data analysisnot JMP
  • Model trainingnot JMP
  • Predictive analyticsnot 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.

PyTorch

  • Dynamic computation graph can be less efficient for production inference than static graphs
  • Requires more manual code for distributed training compared to some alternatives
  • Documentation focused heavily on research use cases rather than production deployment

Pricing, plan by plan

JMP

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

PyTorch

Free

No published plan breakdown. See the PyTorch review.

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 PyTorch if

  • You need dynamic computation graphs.
  • You want to start without paying.
  • You work on Linux, Windows, macOS.
  • You also want automatic differentiation.
PyTorch review

Questions people ask

Is JMP or PyTorch better?
Neither clearly leads. JMP starts at Free and PyTorch at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, JMP or PyTorch?
JMP starts at Free and PyTorch at Free.
Does JMP or PyTorch run on more platforms?
JMP runs on Mac, Windows. PyTorch runs on Linux, Windows, macOS.
Can I use JMP for free?
Both have a free tier, so you can try either at no cost before committing.
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 PyTorch is typically brought in for.
What can JMP do that PyTorch cannot?
JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools. PyTorch covers Dynamic computation graphs, Automatic differentiation, GPU acceleration, Distributed training.

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.

PyTorch: Is PyTorch free and open source?

Yes. PyTorch is an open source machine learning framework that is completely free to use. It was originally created and open-sourced by Facebook (now Meta) in 2016.

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.

PyTorch: What platforms does PyTorch support?

PyTorch supports Linux, Windows, and macOS. It provides strong GPU acceleration through CUDA and other backends for high-performance computing.

Source
JMP: Does it run on Linux?

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

PyTorch: Can I use PyTorch for production deployments?

Yes. PyTorch provides graph-based execution, distributed training, mobile deployment, and quantization features to support production deployments.

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.

Share

Related pages

More on JMP

More on PyTorch

Keep looking

Other head to heads