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Databases · head to head

Apache Pinot vs JMP

Apache Pinot logo

Apache Pinot

Databases

Real-time distributed OLAP datastore for analytics

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: Apache Pinot self-hosted and distributed, so running it means operating a cluster rather than consuming a service; 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: Apache Pinot covers Real-time Analytics, 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 Apache Pinot and JMP actually diverge.

Attributes where Apache Pinot and JMP differ
AttributeApache PinotJMP
Pricing modelopen-sourcesubscription
PlatformsLinux, Docker, KubernetesMac, Windows
CategoryDatabasesMachine Learning
Founded19991976

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 Apache Pinot

  • Real-time Analytics
  • Column-oriented
  • Distributed Processing
  • SQL Support
  • Pluggable Indexing
  • Star-tree Index
  • Upsert Support
  • Kafka

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.

Apache Pinot

  • Sub-second analytics queries on freshly ingested datanot JMP
  • User-facing dashboards inside a productnot JMP
  • Real-time metrics at high ingest ratesnot JMP
  • Petabyte-scale analytics as run at LinkedIn and Ubernot 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 Apache Pinot
  • Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot Apache Pinot
  • Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot Apache Pinot
  • Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot Apache Pinot

Where each one falls short

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

Apache Pinot

  • Self-hosted and distributed, so running it means operating a cluster rather than consuming a service
  • Managed hosting comes from third parties such as StarTree rather than from the project
  • Built for user-facing real-time OLAP, so it is not a general purpose database

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

Apache Pinot

Free
  • Open SourceFree
    • Real-time analytics
    • SQL queries
    • Horizontal scaling

JMP

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

Which should you pick?

Choose Apache Pinot if

  • You need real-time analytics.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want column-oriented.
Apache Pinot 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 Apache Pinot or JMP better?
Neither clearly leads. Apache Pinot 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, Apache Pinot or JMP?
Apache Pinot starts at Free and JMP at Free.
Does Apache Pinot or JMP run on more platforms?
Apache Pinot runs on Linux, Docker, Kubernetes. JMP runs on Mac, Windows.
Can I use Apache Pinot for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Pinot best used for?
Apache Pinot is most often used for sub-second analytics queries on freshly ingested data, user-facing dashboards inside a product, real-time metrics at high ingest rates, petabyte-scale analytics as run at linkedin and uber. Of those, sub-second analytics queries on freshly ingested data and user-facing dashboards inside a product are not what JMP is typically brought in for.
What can Apache Pinot do that JMP cannot?
Apache Pinot covers Real-time Analytics, Column-oriented, Distributed Processing, SQL Support. JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools.

Answered from the vendors’ own pages

Apache Pinot: How much does Apache Pinot cost?

Apache Pinot is free and open-source. It is provided under the Apache License, which allows free use, modification, and distribution.

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.

Apache Pinot: Is Apache Pinot free for commercial use?

Yes. Apache Pinot is licensed under the Apache License, which explicitly permits commercial use at no cost.

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.

Apache Pinot: Can I run Apache Pinot locally or with Docker?

Yes. Apache Pinot offers a Docker quickstart and free downloads of the latest version (1.5.1 at the time of the page). You are responsible for hosting and infrastructure.

Source
JMP: Does it run on Linux?

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

Apache Pinot: Are there restrictions on how I can use Apache Pinot?

The Apache License permits unrestricted use, but requires retention of license notices and statements. No usage limits or feature restrictions are enforced.

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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