The phrase "free marketing mix model" has become so overloaded that it's almost meaningless. Every MMM vendor on the market claims to be free in some way — free trial, free tier, free demo, free download, free as-in-open-source-if-you-can-code-R. The word "free" in this market doesn't mean what it meant five years ago.
This post cuts through the noise. I've run every option on this list with real marketing data, and here's the honest breakdown of what each one actually costs you (in money, time, or technical skill), what you get in return, and which one is right for which type of user.
If you're completely new to MMM and want to understand what it is before comparing tools, start with our complete guide to marketing mix modeling.
What "free" actually means in MMM
Before we get to the tools, it helps to understand the four flavors of "free" in this market.
Free forever, no login, no catch. You upload data, you get results, you walk away. No account, no credit card, no sales call. This is the rarest category.
Free tier / freemium. The base product is free but has usage limits — row counts, refresh frequency, number of channels, or feature gates. Works if your needs fit within the limits. Less attractive if you outgrow them quickly.
Free trial. Full functionality for a limited time (usually 14-30 days), then you pay. These are often marketed as "free" in SEO but they're really "try before you buy." Fine if you just want to run one model, frustrating if you want an ongoing measurement practice.
Free and open-source. The software is free to download and modify, but you need the technical skills to run it. The license doesn't cost anything; the developer time to deploy and maintain it does.
The tools below span all four categories. I've flagged which one each belongs to so you can compare apples to apples.
1. CheapMMM — Free forever, no login, under a minute
What it is: A browser-based no-code MMM tool. Upload a CSV with your date, sales, and channel spend data. Get channel ROAS, feature importance, carryover insights, and a budget optimizer in under a minute. No account, no signup, no limits.
The methodology: Geometric adstock transformations for carryover effects, Hill-function saturation curves for diminishing returns, MAP estimation with Laplace-approximated credible intervals, Ridge regression with adaptive regularization, time-series cross-validation, and SHAP-based attribution where available. This is a legitimate structurally-informed MMM, not a linear regression with a marketing label slapped on it.
What it costs: Nothing. There's no paid tier, no upsell, no "contact us for enterprise pricing" button. The tool is genuinely free.
Who it's for: Small and mid-size marketing teams, DTC brands, growth marketers, and agencies who need directional channel attribution without setting up infrastructure. If you can put data in a spreadsheet, you can use it.
Limitations: It's designed for directional guidance, not causal incrementality measurement. It's not a replacement for geo holdout experiments. It works best with 6+ months of weekly data and reasonable spend variation across channels. You can't customize the underlying model structure the way you can with open-source frameworks.
How to try it: Go to cheapmmm.com and upload your CSV. There's no login step.
2. Meta Robyn — Free and open-source, requires R
What it is: Meta's open-source MMM library, built in R. Uses ridge regression with a Bayesian-inspired hyperparameter search powered by Nevergrad optimization. Produces Pareto-optimal model selections rather than a single "best" model.
What it costs: Zero in licensing. A substantial amount in developer time. You'll need R installed, the Robyn package and all its dependencies, a Python bridge for Nevergrad, data pipeline code to prepare your inputs, and the statistical background to interpret Pareto fronts and decide which model to use.
Who it's for: Data science teams that already work in R and want full control over the model specification. Not realistic for marketing teams without dedicated technical support.
Limitations: Setup from a cold start can easily consume a week of engineering time before you get a usable model. The output requires interpretation — Robyn gives you a set of candidate models, not a single answer, and choosing between them requires understanding what you're looking at.
3. Google Meridian — Free and open-source, requires Python
What it is: Google's open-source Bayesian MMM framework. Uses PyMC under the hood, incorporates reach and frequency data, and supports prior-informed modeling. Integrates naturally with Google Ads data. Google released Scenario Planner in early 2026 to make Meridian more accessible to non-technical users.
What it costs: Free in licensing. Non-trivial in technical setup — Python environment, JAX, PyMC, Jupyter notebooks, and enough statistical fluency to set priors and interpret posterior distributions. Scenario Planner lowers the bar for exploring a finished model but doesn't help with the initial build.
Who it's for: Python-proficient analysts and data scientists, especially those already working within the Google advertising ecosystem. If you have a data team and want methodological rigor, this is a strong option.
Limitations: Like Robyn, the setup overhead is significant. Bayesian modeling has a learning curve beyond just the tooling. Google's documentation is thorough but assumes statistical background.
4. PyMC-Marketing — Free and open-source, most technical
What it is: The most academically rigorous open-source MMM option. Full Bayesian inference with MCMC sampling, custom priors, and posterior analysis. Active development community and strong documentation.
What it costs: Free in licensing. The highest technical bar on this list. Requires Python, PyMC, and comfort with Bayesian modeling concepts.
Who it's for: Data scientists who want full control over model specification and uncertainty quantification. If you care about credible intervals, hierarchical modeling, and the ability to incorporate domain knowledge through priors, PyMC-Marketing gives you the most flexibility.
Limitations: Not usable by non-technical marketers under any reasonable definition. The output is powerful but the path to getting it is steep.
5. Cassandra — Free trial, not free forever
What it is: A SaaS MMM platform marketed as "free" in its SEO content. When you actually sign up, it's a free trial requiring an account, and the ongoing product is paid. Cassandra positions itself as no-code and offers features like scenario planning and incrementality testing integration.
What it costs: Free for a trial period. Paid after that — pricing isn't publicly listed, which usually means "contact sales for a quote."
Who it's for: Agencies and brands that want a managed, ongoing MMM practice with a vendor relationship. If you're looking for a vendor to own your measurement, it's a legitimate option. If you were looking for "free" in the literal sense, it's not that.
Limitations: Requires account creation. Not actually free after the trial. Pricing is opaque.
6. Recast's Google Sheets Template — Free forever, DIY
What it is: A downloadable Google Sheets template that walks through building a basic marketing mix model using OLS regression. You paste your data into the sheet, and it shows you channel coefficients and predictions. Made by Recast (a paid MMM vendor) as a free resource.
What it costs: Nothing. Works entirely in Google Sheets.
Who it's for: People who want to understand MMM conceptually by building one from scratch. It's a better educational tool than a production measurement solution.
Limitations: It's simple linear regression — no adstock, no saturation curves, no cross-validation, no sophisticated attribution. Fine for intuition-building, not suitable for real budget decisions on a multi-channel marketing program.
7. LightweightMMM — Deprecated
What it is: Google's earlier open-source MMM library, built on NumPyro. Was positioned as a lighter-weight alternative to full Bayesian frameworks.
What it costs: Free, but don't bother.
Who it's for: Nobody, as of 2026. Google officially deprecated LightweightMMM in favor of Meridian. If you find tutorials or GitHub repos referencing it, they're outdated. Use Meridian instead.
Quick comparison table
| Tool | Truly free? | Setup time | Technical skill required | Best for | |------|-------------|------------|-------------------------|----------| | CheapMMM | Yes, forever | Under 1 minute | None | Fast, no-code attribution for most teams | | Meta Robyn | Yes (open-source) | Days to weeks | R + statistics | Data science teams in R | | Google Meridian | Yes (open-source) | Days to weeks | Python + Bayesian stats | Python-proficient analysts | | PyMC-Marketing | Yes (open-source) | Days to weeks | Python + advanced Bayesian | Research-grade modeling | | Cassandra | Free trial only | Account setup | None (UI-based) | Managed vendor relationship | | Recast Google Sheet | Yes, forever | Hours | Spreadsheet + basic stats | Learning how MMM works | | LightweightMMM | Deprecated | N/A | N/A | Nobody — use Meridian |
How to choose
If you want results in the next 10 minutes with no account and no setup: CheapMMM. It's the only tool on this list that genuinely delivers on "free and instant."
If you have a data science team and want maximum methodological control: Meridian or Robyn. Pick based on which language your team prefers. If you want the most rigorous Bayesian framework specifically, PyMC-Marketing.
If you want to understand how MMM works before using it: Recast's spreadsheet template is a good learning tool. It won't produce reliable attribution on a real marketing program, but it'll help you build intuition for the underlying math.
If you want a managed vendor relationship and don't mind paying: Cassandra, Sellforte, Recast, or any of the commercial platforms. Don't go looking for these under "free" — search for "MMM software" and expect to pay.
If you're mostly deciding whether MMM is worth running at all: Start with CheapMMM to get a read on your data. If the output is useful, you can decide later whether to invest in more sophisticated tooling. There's no reason to start with a complex setup when you can validate the value in 60 seconds.
What to do after you pick a tool
Whatever tool you choose, the quality of the results depends on the quality of the data you feed it. Before you run your first model, see our guide on how to prepare your data for marketing mix modeling to avoid the mistakes that silently break model output.
Once you have results, interpreting them correctly is the difference between useful insight and misleading numbers. Our guide on how to interpret MMM results covers what to pressure-test, what to question, and how to turn model output into a budget decision you can defend.
For a deeper breakdown of the open-source options specifically, see our comparison of free MMM alternatives to Robyn and Meridian.