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Business Intelligence · head to head

Cube vs JMP

Cube logo

Cube

Business Intelligence

AI-native analytics platform with semantic layer and governed access

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: Cube per-developer licensing can be expensive for large analytics teams; 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: Cube covers Analytics Chat, 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 Cube and JMP actually diverge.

Attributes where Cube and JMP differ
AttributeCubeJMP
Pricing modelPer-developer seats with volume pricingsubscription
PlatformsCloud, Self-hostedMac, Windows
CategoryBusiness IntelligenceMachine Learning
FoundedUnknown1976

Identical on both: starting price (Free), free tier (Yes), 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 Cube

  • Analytics Chat
  • Workbooks
  • Dashboards
  • Embedded Analytics
  • AI Integrations
  • Core Data APIs
  • Caching and pre-aggregations

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.

Cube

  • Building governed semantic data models for analyticsnot JMP
  • Embedding analytics into customer-facing productsnot JMP
  • Enabling natural language data queries for teamsnot JMP
  • Creating conversational dashboards with AI assistancenot 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 Cube
  • Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot Cube
  • Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot Cube
  • Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot Cube

Where each one falls short

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

Cube

  • Per-developer licensing can be expensive for large analytics teams
  • Additional cost for Explorer and Viewer roles beyond developers
  • Semantic layer approach requires upfront modeling investment
  • Smaller connector ecosystem than dedicated BI platforms
  • May be overengineered for simple reporting needs

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

Cube

Free
  • FreeFree
    • For hobbyists and personal projects
    • Basic data source connection
    • Semantic modeling
  • Starter$40/developer/month
    • Extended agent limits
    • Premium LLMs
    • Unlimited workbooks
  • Premium$80/developer/month
    • All Starter features
    • Embedded dashboards
    • Embedded analytics chat
  • Enterprise$null/custom
    • 99.990% uptime SLA
    • Dedicated single-tenant installation
    • Bring Your Own Cloud

JMP

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

Which should you pick?

Choose Cube if

  • You need analytics chat.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want workbooks.
Cube 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 Cube or JMP better?
Neither clearly leads. Cube 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, Cube or JMP?
Cube starts at Free and JMP at Free.
Does Cube or JMP run on more platforms?
Cube runs on Cloud, Self-hosted. JMP runs on Mac, Windows.
Can I use Cube for free?
Both have a free tier, so you can try either at no cost before committing.
What is Cube best used for?
Cube is most often used for building governed semantic data models for analytics, embedding analytics into customer-facing products, enabling natural language data queries for teams, creating conversational dashboards with ai assistance. Of those, building governed semantic data models for analytics and embedding analytics into customer-facing products are not what JMP is typically brought in for.
What can Cube do that JMP cannot?
Cube covers Analytics Chat, Workbooks, Dashboards, Embedded Analytics. JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools.

Answered from the vendors’ own pages

Cube: What is the cost per developer on Cube?

Starter plan costs $40/developer/month. Premium adds embedded analytics at $80/developer/month. Explorer and Viewer roles cost $40 and $20/month respectively.

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.

Cube: What uptime SLAs does Cube offer?

Premium plan offers 99.950% uptime SLA. Enterprise plan provides 99.990% uptime SLA with dedicated support.

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.

Cube: Can I use Cube for free?

Yes, the Free plan includes basic data source connection, semantic modeling, workbooks, and dashboards for hobbyists and personal projects.

Source
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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