Machine Learning · head to head
JMP vs Weaviate
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: 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.; Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection
- They diverge on capability: JMP covers Custom design of experiments, Weaviate covers Vector and keyword search.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which JMP and Weaviate actually diverge.
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 Weaviate
- Vector and keyword search
- Built-in vectorizers
- GraphQL API
- Multi-tenancy
- Hybrid search
- OpenAI
- Hugging Face
- Cohere
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 Weaviate
- Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot Weaviate
- Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot Weaviate
- Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot Weaviate
Weaviate
- Running a vector database for semantic and hybrid searchnot JMP
- Generating and storing embeddings alongside the objects they describenot 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.
Weaviate
- The free tier caps at 100,000 objects, 1 GB of memory and a single collection
- Billing is per million vector dimensions rather than per record, so wider embeddings cost proportionally more for the same object count
- Premium is a prepaid contract starting at $400 a month rather than pay as you go
- Storage rates do not fall consistently with tier, and Premium Dedicated is $0.1505 per GiB against $0.12 on the cheaper Flex plan
- The Query Agent is metered separately, free to 1,000 requests a month and $30 a month plus overage beyond
Pricing, plan by plan
JMP
Free- TrialFree
- 30-day trial
- Full features
- JMP$1785/year
- Core JMP
- Standard features
Weaviate
Free- Open SourceFree
- Full features
- Self-hosted
- ServerlessFree
- Managed service
- Auto-scaling
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.
Choose Weaviate if
- You need vector and keyword search.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want built-in vectorizers.
Questions people ask
- Is JMP or Weaviate better?
- Neither clearly leads. JMP starts at Free and Weaviate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, JMP or Weaviate?
- JMP starts at Free and Weaviate at Free.
- Does JMP or Weaviate run on more platforms?
- JMP runs on Mac, Windows. Weaviate 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 Weaviate is typically brought in for.
- What can JMP do that Weaviate cannot?
- JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools. Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy.
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.
Weaviate: What pricing options does Weaviate offer?
Weaviate provides a free tier with usage-based pricing, plus enterprise options. Visit the pricing page for detailed information on plans.
SourceJMP: 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.
Weaviate: Does Weaviate offer customer support?
Yes, support is included with Weaviate's cloud offerings. Enterprise customers receive first-class support from their global team of experts.
SourceJMP: Does it run on Linux?
No. Windows and macOS only, as an installed application.
Weaviate: Can I deploy Weaviate on my own infrastructure?
Yes. Weaviate is open source and deployment-agnostic. You can run it in your own cloud environment or use their managed cloud service.
SourceJMP: 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.
Weaviate: What data security features does Weaviate provide?
Weaviate includes security & governance, RBAC, SOC 2 and HIPAA compliance, along with multi-tenancy and high availability for enterprise requirements.
SourceJMP: 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.
Weaviate: How do I get started with Weaviate?
Sign up for their cloud tier, create your first dataset, connect an LLM, and build your AI app. Documentation and quickstart guides are available for Python, Go, TypeScript, and JavaScript.
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