Machine Learning & Data Science · head to head
JMP vs Weaviate
JMP
Machine Learning & Data Science
Statistical discovery software from SAS
- From
- Free
- Rated
- -
The short version
- Each has a real cost: JMP the Internet Archive's capture of JMP's homepage on 13 January 2020 named five distinct editions, JMP, JMP Live, JMP Pro, JMP Clinical, and JMP Genomics, each targeting a different analysis use case, with no price figure published for any.; Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection
- They diverge on capability: JMP covers Interactive statistics, Weaviate covers Vector and keyword search.
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 & Data Science).
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
- Interactive statistics
- Dynamic visualization
- Design of experiments
- Predictive modeling
- Quality control
- SAS
- Python
- R
Only in Weaviate
- Vector and keyword search
- Built-in vectorizers
- GraphQL API
- Multi-tenancy
- Hybrid search
- OpenAI
- Hugging Face
- Cohere
Both cover
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
JMP
- Machine learningnot Weaviate
- Data analysisnot Weaviate
- Model trainingnot Weaviate
- Predictive analyticsnot 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
- The Internet Archive's capture of JMP's homepage on 13 January 2020 named five distinct editions, JMP, JMP Live, JMP Pro, JMP Clinical, and JMP Genomics, each targeting a different analysis use case, with no price figure published for any.
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 interactive statistics.
- You want to start without paying.
- You work on Mac, Windows.
- You also want dynamic visualization.
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 machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Weaviate is typically brought in for.
- What can JMP do that Weaviate cannot?
- JMP covers Interactive statistics, Dynamic visualization, Design of experiments, Predictive modeling. Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy. Both handle Mac support, Windows support.