Softwr

Machine Learning · head to head

Databricks vs JMP

Databricks logo

Databricks

Machine Learning

Unified analytics platform for data engineering and data science

From
Free
Rated
-
JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

From
Free
Rated
-

The short version

  • Each has a real cost: Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges; 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: Databricks covers Delta Lake, 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 Databricks and JMP actually diverge.

Attributes where Databricks and JMP differ
AttributeDatabricksJMP
Pricing modelusage-basedsubscription
PlatformsWeb, Aws, Azure, GcpMac, Windows
Founded20131976

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 Databricks

  • Delta Lake
  • Apache Spark
  • MLflow
  • Unity Catalog
  • Photon Engine
  • Collaborative Notebooks
  • Auto-scaling
  • AWS

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.

Databricks

  • Running Spark data engineering pipelines on managed clustersnot JMP
  • Building a lakehouse over data in cloud object storagenot JMP
  • Training and serving machine learning models alongside the datanot 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 Databricks
  • Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot Databricks
  • Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot Databricks
  • Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot Databricks

Where each one falls short

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

Databricks

  • Cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
  • The free trial lasts 14 days
  • Discounts require a Committed Use Contract, with larger commitments needed for larger discounts
  • Azure Databricks pricing is set by Microsoft rather than by Databricks
  • Security and compliance capabilities are sold as separate platform add ons rather than included in the base rate

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

Databricks

Free
  • Community EditionFree
    • Limited cluster
    • Notebook environment
    • Community support
  • Standard$0.07/DBU
    • Jobs compute
    • SQL compute
    • Standard support

JMP

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

Which should you pick?

Choose Databricks if

  • You need delta lake.
  • You want to start without paying.
  • You work on Web, Aws, Azure, Gcp.
  • You also want apache spark.

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.

Questions people ask

Is Databricks or JMP better?
Neither clearly leads. Databricks 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, Databricks or JMP?
Databricks starts at Free and JMP at Free.
Does Databricks or JMP run on more platforms?
Databricks runs on Web, Aws, Azure, Gcp. JMP runs on Mac, Windows.
Can I use Databricks for free?
Both have a free tier, so you can try either at no cost before committing.
What is Databricks best used for?
Databricks is most often used for running spark data engineering pipelines on managed clusters, building a lakehouse over data in cloud object storage, training and serving machine learning models alongside the data. Of those, running spark data engineering pipelines on managed clusters and building a lakehouse over data in cloud object storage are not what JMP is typically brought in for.
What can Databricks do that JMP cannot?
Databricks covers Delta Lake, Apache Spark, MLflow, Unity Catalog. JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools.

Answered from the vendors’ own pages

Databricks: How is Databricks priced?

Databricks bills pay as you go with no up front cost, charging per second for the products used. Consumption is measured in Databricks Units, a normalised unit of processing power on the platform.

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.

Databricks: Does Databricks publish a per DBU price?

Not on its main pricing page. Rates vary by product and instance type, and Databricks directs buyers to individual product pricing pages and a calculator rather than listing a single figure.

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.

Databricks: Does the Databricks price include cloud costs?

No. Databricks states that if you configure it to work with your own cloud account, your cloud provider still charges you separately for the underlying resources.

Source
JMP: Does it run on Linux?

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

Databricks: Can I get a discount on Databricks?

Databricks offers Committed Use Contracts, where larger usage commitments earn greater benefits, including options to use commitments flexibly across multiple clouds.

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

Other head to heads