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Machine Learning · head to head

JMP vs Semantic Kernel

JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

From
Free
Rated
-
Full reviewWebsite
Semantic Kernel logo

Semantic Kernel

Machine Learning

Model-agnostic SDK for AI orchestration

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.; Semantic Kernel steep learning curve for advanced features
  • They diverge on capability: JMP covers Custom design of experiments, Semantic Kernel covers Multi-model support.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which JMP and Semantic Kernel actually diverge.

Attributes where JMP and Semantic Kernel differ
AttributeJMPSemantic Kernel
Pricing modelsubscriptionOpen source, no pricing
PlatformsMac, WindowsPython, .NET, Java
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 Semantic Kernel

  • Multi-model support
  • Agent framework
  • Multi-agent systems
  • Plugin ecosystem
  • Vector database integration
  • Multimodal support
  • Local model support
  • Enterprise observability

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

Semantic Kernel

  • Building enterprise AI applications with LLM integrationnot JMP
  • Creating multi-agent systems for complex workflowsnot JMP
  • Developing AI-powered chatbots and assistantsnot JMP
  • Implementing RAG systems with vector databasesnot 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.

Semantic Kernel

  • Steep learning curve for advanced features
  • Documentation focuses on Azure cloud services
  • Configuration complexity for multi-model scenarios
  • Requires understanding of AI/LLM concepts

Pricing, plan by plan

JMP

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

Semantic Kernel

Free
  • Open SourceFree
    • MIT license
    • Full framework access
    • All language SDKs

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 Semantic Kernel if

  • You need multi-model support.
  • You want to start without paying.
  • You work on Python, .NET, Java.
  • You also want agent framework.
Semantic Kernel review

Questions people ask

Is JMP or Semantic Kernel better?
Neither clearly leads. JMP starts at Free and Semantic Kernel at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, JMP or Semantic Kernel?
JMP starts at Free and Semantic Kernel at Free.
Does JMP or Semantic Kernel run on more platforms?
JMP runs on Mac, Windows. Semantic Kernel runs on Python, .NET, Java.
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 Semantic Kernel is typically brought in for.
What can JMP do that Semantic Kernel cannot?
JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools. Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem.

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.

Semantic Kernel: What LLM providers does Semantic Kernel support?

Semantic Kernel supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and other providers through extensible model implementations.

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.

Semantic Kernel: Can I run Semantic Kernel locally?

Yes. Semantic Kernel supports local models through Ollama, LMStudio, and ONNX for complete data control and offline operation.

Source
JMP: Does it run on Linux?

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

Semantic Kernel: Is Semantic Kernel free?

Yes. Semantic Kernel is MIT-licensed open source and completely free. You only pay for external LLM APIs you use.

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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