Databases · head to head
DuckDB vs JMP

DuckDB
Databases
MIT-licensed analytical SQL database that runs inside your process, with no server, no dependencies and one writer at a time.
- From
- Free
- Rated
- -

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: DuckDB a database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.; 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: DuckDB covers In-process execution, 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 DuckDB and JMP actually diverge.
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 DuckDB
- In-process execution
- Vectorised columnar engine
- Direct file querying
- Zero dependencies
- Larger-than-memory queries
- MIT licence
- Postgres-flavoured SQL
- Extension ecosystem
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.
DuckDB
- Transformation steps in a data pipeline that would otherwise need Spark, replaced by SQL over Parquet in a single processnot JMP
- Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot JMP
- Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot JMP
- Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot 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 DuckDB
- Process capability and control chart work in a manufacturing quality function that has to produce evidence for a customer or regulatornot DuckDB
- Exploratory analysis by an engineer or scientist who will not write code but needs more than a spreadsheetnot DuckDB
- Semiconductor, chemical and pharmaceutical development groups where JMP is already the shared language for reporting resultsnot DuckDB
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
DuckDB
- A database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
- There is no network protocol, authentication or user management, so exposing it to remote clients means writing and securing your own service around it.
- It is built for scans and aggregations, not for many small transactions, so a workload of high-frequency single-row inserts and updates performs badly compared with SQLite or Postgres.
- Storage files are backwards compatible but not forwards compatible, so a file written by a newer version cannot be read by an older one and every consumer of a shared file must be upgraded together.
- Query memory settings matter: some operations still need to hold significant state, so an under-configured memory limit turns a large join or a high-cardinality aggregation into a spill-heavy query or an out-of-memory failure rather than a slow success.
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
DuckDB
FreeNo published plan breakdown. See the DuckDB review.
JMP
Free- TrialFree
- 30-day trial
- Full features
- JMP$1785/year
- Core JMP
- Standard features
Which should you pick?
Choose DuckDB if
- You need in-process execution.
- You want to start without paying.
- You work on Linux, macOS, Windows, WebAssembly.
- You also want vectorised columnar engine.
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 DuckDB or JMP better?
- Neither clearly leads. DuckDB 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, DuckDB or JMP?
- DuckDB starts at Free and JMP at Free.
- Does DuckDB or JMP run on more platforms?
- DuckDB runs on Linux, macOS, Windows, WebAssembly. JMP runs on Mac, Windows.
- Can I use DuckDB for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is DuckDB best used for?
- DuckDB is most often used for transformation steps in a data pipeline that would otherwise need spark, replaced by sql over parquet in a single process, analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptable, local exploration of files that are too large for a pandas dataframe but far too small to justify a warehouse, continuous integration and testing of analytical sql, where a real engine can run in the test process without provisioning anything. Of those, transformation steps in a data pipeline that would otherwise need spark, replaced by sql over parquet in a single process and analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptable are not what JMP is typically brought in for.
- What can DuckDB do that JMP cannot?
- DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies. JMP covers Custom design of experiments, Linked interactive graphics, Analysis platforms, Quality and process tools.
Answered from the vendors’ own pages
DuckDB: Can multiple applications share one DuckDB database?
Not for writing. One process holds the database read-write; others may attach read-only and will not see later writes. Shared multi-writer access needs a different database or a table format with a catalogue.
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.
DuckDB: Is it a replacement for a data warehouse?
For single-node analytical workloads up to a few hundred gigabytes it very often is. It is not a replacement when many concurrent users need a shared, governed, always-on service.
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.
DuckDB: Do I have to load data into it?
No. It queries Parquet, CSV, JSON and Arrow in place, including on object storage. Its own storage format is optional and mainly useful when you want indexes, constraints and faster repeated access.
JMP: Does it run on Linux?
No. Windows and macOS only, as an installed application.
DuckDB: What is MotherDuck's relationship to it?
MotherDuck is a separate company offering a managed and hybrid service built on the DuckDB engine. DuckDB itself remains MIT-licensed and independent of it, with the IP held by the DuckDB Foundation.
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.
DuckDB: Is it suitable for OLTP?
No. It is designed for analytical scans. For transactional workloads with frequent small writes, SQLite or Postgres is the right tool.
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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