If you have priced a DOE licence lately, the sticker is a shock: a single seat of JMP or Minitab runs well over a thousand dollars a year, and MODDE is quote-only. For a graduate student, a small biotech, or anyone running a handful of designs a year, that is hard to justify, so this guide maps the credible free and open source DOE options against the commercial leaders. We compare JMP, Minitab, Design-Expert and MODDE against R and Python packages and free browser tools, on price, ease of use, and fit for cell culture and fermentation work. If you just want to build a run table right now without installing anything, you can open a free browser-based DOE generator and come back for the full comparison.
One clarification up front, because the words get muddled: an alternative to JMP that is truly open source (public, auditable code) is different from a web app that is merely free to use. Both save you the licence fee, but they are not interchangeable when a regulator asks how your software was qualified. We keep the two buckets separate throughout.
The cost of the big tools
The four DOE market leaders are all four-figure-per-year tools, and only two of them publish a price at all. JMP and Minitab have list figures you can find; Design-Expert and MODDE are sold by quote, which in practice means "expect a four-figure annual number, negotiated."
Here is the honest state of pricing as of 2026. Where a vendor does not publish a number, we say so rather than invent one.
| Software | Vendor | Model | Approx. price |
|---|---|---|---|
| JMP | SAS Institute | Annual subscription | ~$1,300–1,600 |
| JMP Pro | SAS Institute | Annual subscription | Quote-only (higher tier) |
| Minitab | Minitab LLC | Annual subscription | ~$1,800–1,900 |
| Design-Expert | Stat-Ease | Perpetual / subscription | Quote-only (historically four figures) |
| MODDE | Sartorius (Umetrics) | Commercial licence | Quote-only (enterprise) |
Academic and multi-seat discounts exist, and add-on modules (Minitab's Predictive Analytics, JMP Pro's modelling) are priced separately, so a real quote can drift well above the single-seat list. The point of the table is direction, not a purchase order: this is the cost that free and open source tools are competing against.
Figure 1. Approximate annual cost per user. Commercial tools (blue) sit in the four-figure band; open source and free browser tools (teal) are $0. Quote-only tools are shown at a representative four-figure level for scale.
Open source DOE: R & Python
The most capable free option is not a single app but the R DOE ecosystem, which generates and analyzes every design type the commercial tools offer. If you are willing to write a few lines of code, R is a genuine alternative to Minitab and JMP for the whole workflow, not just the design step.
The core R packages, all on CRAN and catalogued in the official Design of Experiments task view, are:
- FrF2 is the flagship: regular and non-regular two-level fractional factorials, Plackett-Burman screens via pb(), plus aliasing and effect-analysis tools. It is the direct free counterpart to JMP's and Minitab's factorial builders.
- DoE.base is the foundation layer: full factorials, general factorial designs, and orthogonal arrays. It underpins FrF2 and the menu-driven R Commander DoE plugin.
- rsm handles response surface methodology: central composite and Box-Behnken designs, first- and second-order model fitting, steepest ascent, and contour visualisation. It is the free stand-in for MODDE's optimisation designs.
- AlgDesign generates algorithmic optimal designs (D-, A-, and I-optimal) by Federov exchange, which matters when constraints make a textbook design impossible.
In Python, the picture is design-generation-first: pyDOE3 (the actively maintained successor to pyDOE/pyDOE2) builds full and fractional factorials, Plackett-Burman, Box-Behnken, central composite, and Latin hypercube designs. Stat-Ease's own dexpy adds factorial, response-surface, and definitive screening designs with model support, and a dedicated definitive-screening-design package returns a DSD as a pandas DataFrame. Analysis in Python leans on statsmodels for the ANOVA rather than a built-in DOE analyser.
Skip the install: build the design in your browser
The free DOE generator produces full factorial, fractional, Plackett-Burman, central composite, Box-Behnken, and definitive screening run tables, randomized and CSV-ready, with bioprocess presets. No R, no Python, no licence.
Free browser DOE tools
Free browser DOE tools let you generate a design with no installation, but most are proprietary freeware, not open source, so keep the distinction in mind. They are the fastest path from "I need a run table" to "runs on the bench," and they are ideal when the barrier is not budget but the friction of installing and learning R.
Free, code-free GUI options fall into two groups. Genuinely open source desktop GUIs include JASP and PSPP (general statistics with ANOVA and regression usable for DOE analysis) and R Commander with the RcmdrPlugin.DoE plugin, which wraps FrF2 and DoE.base in menus so you get open source engines without writing code. Proprietary-but-free web tools also exist for pure design generation; treat those as "free browser tools," not as design of experiments software open source, because you cannot audit or version-pin their code.
The pragmatic combination that most non-programmers land on: a browser generator for the design, then JASP or R Commander for the ANOVA and model reduction. That pairing reproduces the JMP or MODDE experience, design plus analysis, for zero licence cost. Our own design of experiments calculator sits in the design-generation slot with bioprocess presets built in, and pairs cleanly with any free analysis GUI. If you are still deciding which design to generate, the which DOE design should I use guide narrows it by goal and factor count first.
What you give up going free
Going free costs you polish, support, and paperwork, not statistical validity. The math in FrF2 and rsm is the same math JMP and Minitab implement; what a licence buys is the experience around it. Knowing exactly what you are trading keeps the decision honest.
The three things you most often miss with free tools are an integrated analysis GUI (you assemble your own from a generator plus JASP or R), vendor validation documentation for GxP audits, and design-space visualisation as slick as MODDE's. The first is a workflow habit, the second is addressed by version-pinning and documenting an open source package, and the third is cosmetic. None of them is the math.
Best pick by use case
The right free choice depends on whether you code, whether you are in a regulated environment, and how deep the analysis needs to go. There is no single winner, so match the tool to the job.
| Use case | Best free pick | Why |
|---|---|---|
| No-code quick screen | Browser DOE generator + JASP | Design in a browser, ANOVA in a free GUI, zero install. |
| Full RSM optimization | R (rsm + FrF2) | CCD/Box-Behnken, model fitting, steepest ascent, contours. |
| Python data pipeline | pyDOE3 + statsmodels | Design generation that scripts into an existing analysis stack. |
| GMP / regulated | Version-pinned R (FrF2, rsm) | Auditable, documentable, exactly reproducible analysis of record. |
| Teaching / students | R Commander + DoE plugin | Menu-driven open source engines; no licence for a whole class. |
| Media / buffer mixtures | Browser generator + rsm | Generate the blend design, fit the Scheffé model free. |
Worked example: a 5-factor screen, priced three ways
You want to screen 5 media components for their effect on titer with a Plackett-Burman 12-run design, then analyze the effects.
Commercial route. Open JMP or Minitab, build the 12-run Plackett-Burman, run the analysis. Cost: one seat, roughly $1,320–1,851/year. Time to first design: minutes, if you already own it.
Open source route. In R: library(FrF2); pb(12, nfactors=5) generates the design; fit with lm() and read the effects. Cost: $0. Time: minutes once R is installed, plus a one-time learning curve.
No-code route. In the free browser generator, choose Plackett-Burman, enter 5 factors, get a randomized 12-run table and CSV. Analyze the returned data in JASP. Cost: $0. Time: minutes, no install, no code.
The verdict. All three produce the same 12-run Plackett-Burman matrix and the same effect estimates, because it is the same design. The only differences are the GUI polish and the annual invoice. For a lab running a few screens a year, the free routes are not a compromise on the statistics, only on the interface.
Bioprocess-specific needs
Bioprocess DOE has a few needs that generic tools handle awkwardly, and this is where a purpose-built free tool can beat a general-purpose commercial one. Cell culture and fermentation bring hard-to-change factors, component-sum constraints in media, and time-course responses that a textbook design ignores.
Three bioprocess realities shape the choice. First, media and buffer optimisation is often a mixture design problem, where components sum to a constant and an ordinary factorial is invalid, so you need a tool that offers mixture or constrained designs. Second, factors like temperature or pH are hard to change run-to-run, which pushes you toward split-plot or blocked designs. Third, bioprocess factors have sensible default ranges (temperature 30–37 °C, pH 6.8–7.2, DO 20–60%) that generic tools make you enter from scratch every time. A DOE generator with bioprocess presets removes that friction, which is exactly why the free browser generator ships CHO fed-batch, E. coli expression, Pichia, chromatography, and media presets. For the biology-specific caveats, our guide on DOE for cell culture and fermentation covers what breaks and how to design around it.
The bottom line: for a full regulated design-space campaign with vendor-backed validation, a commercial package still earns its price. For screening, optimisation, teaching, and most day-to-day bioprocess development, an open source DOE stack or a free browser generator delivers the same designs and the same statistics at zero cost. Start free, and buy a licence only when a specific requirement, validation paperwork or a proprietary visualisation, actually forces it. When you are ready to build a design, the free DOE generator is one click away.
Frequently Asked Questions
Is there an open source alternative to JMP for design of experiments?
Yes. The most complete open source alternative to JMP for DOE is R with the FrF2, DoE.base, rsm, and AlgDesign packages, which together build full and fractional factorials, Plackett-Burman screens, central composite and Box-Behnken response-surface designs, and optimal designs, then fit and analyze the models. In Python, pyDOE3 and dexpy generate the same design types. For a no-code option, a free browser-based DOE generator builds the run table without installing anything. None cost a licence fee.
How much does DOE software like JMP and Minitab cost?
JMP lists at roughly 1,300 to 1,600 US dollars per named user per year, and Minitab at roughly 1,800 to 1,900 US dollars per user per year. Design-Expert (Stat-Ease) and MODDE (Sartorius) are quote-only with no published price; single-user Design-Expert licences have historically run in the four-figure range, and MODDE is enterprise-priced through Sartorius sales. JMP Pro is a higher tier above JMP with no public price. Open source DOE tools in R and Python cost nothing.
What is the difference between open source DOE and free browser DOE tools?
Open source DOE means the source code is public and freely modifiable, as with the R packages FrF2, DoE.base, and rsm, or the Python packages pyDOE3 and dexpy. Free browser DOE tools are web apps you use at no cost but whose code is usually proprietary freeware. Both avoid a licence fee, but only open source packages can be audited, extended, and version-pinned for a validated workflow, which matters in a GMP environment.
Can I use free DOE tools in a regulated GMP bioprocess?
You can, but the free tool must be qualified like any other software used in a GxP decision. Open source R packages such as FrF2 and rsm are used in regulated development because you can pin a version, document it, and reproduce the analysis exactly. The design generation step is low risk because the run table is verifiable, but the statistical analysis that feeds a design space or a specification should be reproduced or checked against a validated method. Many teams generate designs free and reserve a validated package for the final analysis of record.
What is the best free alternative to MODDE for bioprocess DOE?
For bioprocess work the closest free alternative to MODDE is R with rsm plus FrF2, because it covers the screening-to-response-surface workflow MODDE is built around, including central composite and Box-Behnken designs and mixture-adjacent modelling. If you want MODDE's point-and-click feel without coding, pair a free browser DOE generator for the design with JASP or R Commander for the ANOVA and model fitting. You give up MODDE's polished design-space visualisation and vendor support, not the underlying statistics.
Related Tools
- DOE Experiment Generator — A free, no-install alternative to JMP and Minitab for building factorial, fractional, Plackett-Burman, CCD, Box-Behnken, and definitive screening run tables with bioprocess presets.
- Media & Feed Estimator — Cost the runs in your DOE before you book bench time.
- Growth Curve Fitter — Fit the responses your designed experiment produces and read model quality free.
References
- Grömping, U. (2014). R Package FrF2 for Creating and Analyzing Fractional Factorial 2-Level Designs. Journal of Statistical Software, 56(1), 1–56. DOI: 10.18637/jss.v056.i01
- Jones, B. & Nachtsheim, C.J. (2011). A Class of Three-Level Designs for Definitive Screening in the Presence of Second-Order Effects. Journal of Quality Technology, 43(1), 1–15. DOI: 10.1080/00224065.2011.11917841
- Mandenius, C.-F. & Brundin, A. (2008). Bioprocess optimization using design-of-experiments methodology. Biotechnology Progress, 24(6), 1191–1203. DOI: 10.1002/btpr.67
- NIST/SEMATECH (2012). e-Handbook of Statistical Methods, Chapter 5: Process Improvement. itl.nist.gov