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Machine Learning · head to head

JMP vs Milvus

JMP logo

JMP

Machine Learning

Desktop statistical and design of experiments software from a SAS subsidiary

From
Free
Rated
-
Full reviewWebsite
Milvus logo

Milvus

Machine Learning

Open-source vector database for scalable similarity search

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.; Milvus vector dimensions are capped at 32,768
  • They diverge on capability: JMP covers Custom design of experiments, Milvus covers Billion-scale vectors.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which JMP and Milvus actually diverge.

Attributes where JMP and Milvus differ
AttributeJMPMilvus
Pricing modelsubscriptionfreemium
PlatformsMac, WindowsLinux, Mac, Windows, Web
Founded19762017

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 Milvus

  • Billion-scale vectors
  • Multiple index types
  • GPU acceleration
  • Hybrid search
  • Data partitioning
  • PyTorch
  • TensorFlow
  • Hugging Face

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

Milvus

  • Self hosting a vector database for semantic searchnot JMP
  • Storing and querying embeddings for retrieval augmented generationnot JMP
  • Similarity search over images, audio or text at scalenot 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.

Milvus

  • Vector dimensions are capped at 32,768
  • A collection is limited to 64 fields, 1,024 partitions and 16 shards
  • Only 1 index is allowed per field
  • Search returns at most 16,384 vectors as top-k, and nq is capped at 16,384
  • Input and output per RPC is capped at 64 MB for insert, search and query
  • VARCHAR values are limited to 65,535 characters
  • Data loaded into query nodes cannot exceed 90% of available memory
  • An instance supports at most 65,536 collections

Pricing, plan by plan

JMP

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

Milvus

Free
  • Open SourceFree
    • Full features
    • Self-hosted
    • Community support
  • Zilliz CloudFree
    • Managed service
    • Free tier available

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 Milvus if

  • You need billion-scale vectors.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want multiple index types.
Milvus review

Questions people ask

Is JMP or Milvus better?
Neither clearly leads. JMP starts at Free and Milvus at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, JMP or Milvus?
JMP starts at Free and Milvus at Free.
Does JMP or Milvus run on more platforms?
JMP runs on Mac, Windows. Milvus runs on Linux, Mac, Windows, 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 Milvus is typically brought in for.
What can JMP do that Milvus cannot?
JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools. Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search.

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.

Milvus: How much does Milvus cost?

Milvus is open-source and free to use and modify. The self-hosted version has no licensing cost. Zilliz Cloud (the managed SaaS version) does not publish pricing on the website.

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.

Milvus: Is there a free or open-source version of Milvus?

Yes, Milvus is fully open-source and available for free. Milvus Lite is a lightweight option for learning and prototyping that can be installed via pip.

Source
JMP: Does it run on Linux?

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

Milvus: Does Milvus offer a managed cloud service?

Yes, Zilliz Cloud is a fully managed Milvus cloud offering with serverless and dedicated cluster options. Pricing must be requested from the company as it is not listed on the public website.

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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