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Machine Learning · head to head

JMP vs TensorBoard

JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

From
Free
Rated
-
Full reviewWebsite
T

TensorBoard

Machine Learning

Local visualisation for training runs, reading event files written to a directory

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.; TensorBoard there is no authentication of any kind, so putting it on a shared host exposes every run, every metric and every logged sample image to anyone who can reach the port, and adding access control means building and maintaining a reverse proxy.
  • They diverge on capability: JMP covers Custom design of experiments, TensorBoard covers Scalar dashboards.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which JMP and TensorBoard actually diverge.

Attributes where JMP and TensorBoard differ
AttributeJMPTensorBoard
Pricing modelsubscriptionopen-source
PlatformsMac, WindowsWeb
Founded1976Unknown

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 TensorBoard

  • Scalar dashboards
  • Run comparison
  • Graph visualisation
  • Histograms and distributions
  • Embedding projector
  • Image, audio and text panels
  • Hyperparameter view
  • Profiler

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

TensorBoard

  • Watching a training run in progress on a workstation or a remote box, to decide whether to stop it earlynot JMP
  • Diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curvenot JMP
  • Profiling a slow training loop to find out whether the bottleneck is the data pipeline or the acceleratornot JMP
  • Working in an environment with no outbound network access, where a hosted tracking service is not an optionnot 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.

TensorBoard

  • There is no authentication of any kind, so putting it on a shared host exposes every run, every metric and every logged sample image to anyone who can reach the port, and adding access control means building and maintaining a reverse proxy.
  • Event files grow without limit and the interface loads runs into memory, so a directory holding hundreds of runs or a script logging scalars every step becomes slow to start and unpleasant to navigate well before the disk fills.
  • It shows only what the training loop chose to write, so nothing connects a curve back to the code commit, the data set version or the environment unless the engineer logged those explicitly, which means the reproducibility problem is left entirely to you.
  • TensorBoard.dev, the hosted service for sharing a run by link, was shut down at the end of 2023, so results now travel between colleagues as screenshots or through a deployment somebody on the team has to operate.
  • Comparing runs is done by ticking boxes in a run list, which is fine for ten runs and useless for a thousand, and that threshold is precisely where teams start paying for MLflow, Weights and Biases or Neptune instead.

Pricing, plan by plan

JMP

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

TensorBoard

Free

No published plan breakdown. See the TensorBoard 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 TensorBoard if

  • You need scalar dashboards.
  • You want to start without paying.
  • You also want run comparison.
TensorBoard review

Questions people ask

Is JMP or TensorBoard better?
Neither clearly leads. JMP starts at Free and TensorBoard at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, JMP or TensorBoard?
JMP starts at Free and TensorBoard at Free.
Does JMP or TensorBoard run on more platforms?
JMP runs on Mac, Windows. TensorBoard runs on Web.
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 TensorBoard is typically brought in for.
What can JMP do that TensorBoard cannot?
JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools. TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions.

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.

TensorBoard: Does it work with PyTorch?

Yes. PyTorch includes a SummaryWriter that emits the same event file format, and Lightning wires it up by default. Nothing about the tool requires TensorFlow at run time.

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.

TensorBoard: Do I have to install TensorFlow to use it?

No. The tensorboard package installs on its own. Some plugins expect TensorFlow to be present, but the scalar, histogram and image dashboards do not.

JMP: Does it run on Linux?

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

TensorBoard: Is it an experiment tracker?

No, and treating it as one is the common mistake. It visualises whatever a run wrote to disk. It does not store hyperparameters, code versions, artefacts or results in a way that survives someone deleting the log directory.

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.

TensorBoard: How do I share a dashboard with a colleague?

Host it yourself behind your own authentication, or send screenshots. The hosted sharing service was retired at the end of 2023.

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.

TensorBoard: What does it cost?

Nothing. It is Apache 2.0 licensed and runs on your own machine.

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