Open source marketing mix modeling (MMM) gives you the model code at no license cost. It does not give you clean data, a statistician to specify priors, experiments to calibrate against, or a process that turns outputs into budget decisions. If your team has those, a free framework such as Meridian or Robyn is a legitimate starting point. If it does not, the real cost shows up in months of engineering and analyst time instead of a software invoice.
This guide explains what the main open source options are, what they require, where teams get stuck, and how to decide between building in-house and buying a platform.
TL;DR
- Open source MMM frameworks remove the license fee, but data quality and expertise remain the main obstacles.
- Meridian (Bayesian, Python) and Robyn (ridge-based, from Meta Marketing Science) are the two best-known projects. They differ in method, infrastructure needs and how they handle uncertainty.
- An observational model is only decision-grade when calibrated with incrementality experiments such as geo holdouts and lift tests.
- Choose open source if you have a modeling team and time. Choose a managed platform if you need trusted results on a planning calendar, for example before Q4 budget locks.
Table of Contents
- What open source MMM actually means
- The main open source MMM frameworks
- Where the cost really sits
- A realistic timeline for an in-house build
- Open source versus a managed platform
- Mistakes we see when teams go open source
- Where Cassandra fits
- Our perspective: models need proof, and proof needs experiments
- FAQ
- Sources
What open source MMM actually means
Marketing mix models are econometric models that measure the incremental impact of marketing and non-marketing activities on a KPI such as sales or revenue. They work from aggregated time series, not user-level tracking, which is why interest has grown as cookies and device identifiers have become less reliable.
An open source MMM is a library or package that implements that modeling approach. You get the code, usually the documentation, and a community. You supply everything else:
- Weekly or daily spend and exposure data for every channel
- Outcome data (orders, revenue, sign-ups)
- Control variables such as promotions, pricing, seasonality and competitor activity
- Compute to fit the model
- People who can judge whether the output is believable
That last item matters most. The code is the smallest part of an MMM program.
The main open source MMM frameworks
Meridian
Meridian is built by Google and was made available to everyone on January 29, 2025. Its GitHub repository lists an Apache License 2.0 and describes it as a framework that lets advertisers set up and run their own in-house models.
Practical points from the repository:
- It requires Python 3.11 to 3.13.
- It uses a Bayesian sampling approach (NUTS) that can be compute intensive, so the maintainers recommend at least one GPU. They note testing on a T4 GPU with 16 GB of RAM.
- It supports calibration with experiments and other prior information.
The Bayesian design is a real strength: you get credible intervals around channel contribution instead of a single point estimate. The trade-off is that you need someone who understands priors, convergence diagnostics and what a wide interval means for a budget decision.
Robyn
Robyn is an experimental, AI/ML-powered, open source MMM package from Meta Marketing Science. Its stated mission is to democratize modeling knowledge. In practice it uses a ridge-regression approach with automated hyperparameter search that produces many candidate models, from which an analyst selects one.
That selection step is where judgment enters. Several candidate models can fit history equally well while recommending very different budget moves. Choosing among them without experimental evidence is a risk we return to below.
Bayesian libraries
Other open source Python libraries build MMM on general-purpose probabilistic programming. One practitioner write-up describes PyMC-Marketing as one of the canonical open source Bayesian MMM libraries. These suit teams that want to customize adstock and saturation curves and are comfortable writing model code.
How they compare
| Criterion | Bayesian frameworks (Meridian, PyMC-based) | Ridge-based (Robyn) |
|---|---|---|
| Uncertainty | Posterior intervals on contribution | Typically a set of candidate models to choose from |
| Prior knowledge | Experiment results can enter as priors | Calibration is possible but model selection stays manual |
| Compute | GPU recommended for Meridian | Lighter, but runs many models |
| Skill needed | Bayesian modeling, diagnostics | Regularized regression, model selection |
| Support | Community and documentation | Community and documentation |
Neither route includes data pipelines, experiment design, or a dashboard your CFO can read.
Where the cost really sits
Industry coverage of open source MMM makes the same point: these tools lower the cost of modeling but not the barrier of expertise. Budget for four things.
1. Data preparation
Implementation is rarely limited by software. A Qonto analytics team describes a 'data archaeology' phase that often takes 3 to 6 weeks as siloed datasets from finance, CRM and ad platforms are consolidated. Every channel needs a consistent date grain, consistent spend definitions, and a documented history of promotions and pricing changes.
If you spend across many platforms, this stage repeats every refresh unless you automate it. This is where data connectors earn their place: see how Cassandra connects marketing data sources.
2. Model specification
You decide adstock (how long an ad's effect carries over), saturation (diminishing returns) and which controls to include. Each choice changes the answer. Two analysts can build defensible models with different channel rankings.
3. Validation
Practitioners warn against making R-squared the sole focus because it is easily inflated. They advise prioritizing out-of-sample validation and error metrics such as RMSE. Our guide to five metrics for assessing MMM accuracy walks through what to check before you trust a result.
A complementary check is a parameter recovery exercise: generate synthetic data where the true effects are known, run your chosen framework on it, and see whether it recovers them. An open source package called graphsimulacrum is built for this kind of MMM validation. A model that cannot recover known effects should not be trusted with real budget.
4. Calibration with experiments
Observational models are prone to bias, particularly when spend is driven by the same demand signals that drive sales (you raise spend in strong weeks). Seasoned practitioners argue that MMM must be calibrated with informative priors from incrementality experiments such as lift tests or geo holdouts, and describe that crossover as the state of the art.
The frameworks can accept this information. They do not run the experiments. You need a geo test design, adequate power, and a clean read. Our post on geo lift tests built for 80% power explains the design side.
A realistic timeline for an in-house build
The following is a planning estimate based on the stages above, not a benchmark. Your numbers will differ.
- Weeks 1 to 6: Gather and reconcile data. The Qonto example suggests the consolidation phase alone can take 3 to 6 weeks.
- Next phase: Set up the environment (Python version, GPU access if you use Meridian), specify priors, fit and diagnose.
- In parallel: Design and run at least one geo experiment on your largest or most uncertain channel so you have something to calibrate against.
- Then: Compare model output with experiment results, revise, and only then publish budget recommendations.
- Ongoing: Refresh data and refit on a regular cadence, and keep re-testing.
If your goal is to hit a Q4 plan, starting a first build in late October leaves very little room for the validation and calibration steps. That is the point where many teams consider a managed route.
Open source versus a managed platform
The decision is rarely ideological. It comes down to who owns the work.
| Question | Open source fits when | A platform fits when |
|---|---|---|
| Team | You have data scientists with Bayesian or econometric training | Your analysts are stretched across reporting and planning |
| Data | Engineering can maintain pipelines | Connectors and automated refreshes are needed |
| Speed | A multi-month build is acceptable | Decisions are due this quarter |
| Validation | You can design and read your own experiments | You want testing built into the measurement loop |
| Governance | You need full control of model internals | You need outputs a finance team can audit and reuse |
A third option is hybrid: use an open source model for exploration, and a platform for the always-on system of record. What matters is that some experimental evidence disciplines whichever model you use. For a wider view of how the methods fit together, read our piece on triangulating MTA, MMM and incrementality testing.
Mistakes we see when teams go open source
- Treating the model as truth. Experts describe a need for 'comfort with ambiguity' and using MMM as a probabilistic guide that is validated and updated continuously, not a definitive answer.
- Picking the best-fitting model instead of the best-calibrated one. Fit to history is cheap. Agreement with a holdout is not.
- Skipping a refresh plan. A model fitted once and left alone drifts from the market.
- Ignoring marginal return. Average ROI by channel says little about what the next dollar will do. Budget decisions need response curves, not just contribution tables.
- Running experiments that hurt live performance with no plan. Holdouts have a cost, so design them deliberately. See how to approach testing incrementality without hurting live performance.
Where Cassandra fits
If you have read this far and concluded that the modeling is the easy part, you are in our target audience. Cassandra is a marketing measurement platform that combines marketing mix modeling, incrementality testing and always-on incrementality in one system, with data integrations automated so your team does not repeat the data archaeology each quarter.
Published plans start at from €3,000 per month for Essentials, from €3,750 per month for Complete, and from €5,400 per month for Scale. Enterprise pricing is not published. Details are on the pricing page. If you want to see the approach before committing, our walkthrough on free marketing mix modeling software shows how to get a first model quickly.
Our perspective: models need proof, and proof needs experiments
We built Cassandra on a simple observation. AI agents can act quickly on a budget, but speed is not evidence. An agent that shifts spend toward the channel with the best platform-reported ROAS has not shown that the shift made money. Only a measurement system that separates correlation from incremental impact can show that.
That is why we think of open source MMM as a component, not a program. A free model answers one question: given this history, how would a set of assumptions attribute sales to channels? A working measurement program has to answer three more: are those assumptions right, what happens at the margin when you move budget, and what did we learn from the last decision?
In our view the loop has to close. Plan the budget, launch experiments, learn from them, feed the learning back into the model, and repeat, with a marketer guiding the system. When that loop runs continuously, MMM stops being a quarterly report and becomes a daily input to planning. For teams that want to build the loop themselves, open source tools are a fair place to begin. For teams that would rather spend their time on decisions, we built the platform so the loop is already in place.
We would add one caution of our own: whichever route you choose, test your model against an experiment in your own business before you move large budgets. Results from other companies, including any we have published, are a prompt for validation, not a substitute for it.
— Gabriele Franco
FAQ
Is open source MMM really free?
The license is free, but operating it is not. You pay for data engineering, compute (Meridian's maintainers recommend a GPU), analyst time and the experiments needed for calibration. Coverage of the space notes that expertise remains a key obstacle even as costs fall.
Which open source MMM should I start with?
If your team is comfortable with Python and Bayesian methods and wants uncertainty intervals, Meridian is a natural candidate. If you prefer a regularized-regression workflow, look at Robyn. Run either on synthetic data with known effects first, then on a single well-understood market before scaling up.
How do I know my MMM is accurate?
Use out-of-sample validation and error metrics rather than in-sample R-squared alone, and compare model estimates against incrementality experiments. Our article on Bayesian marketing mix modeling for analysts covers how priors and calibration work in practice.
Can a small team run an open source MMM?
It can, if it has at least one person with modeling experience and access to clean, consolidated data. Without both, the data preparation and model selection stages tend to stall the project. In that case a managed service or platform is usually faster.
Do I still need experiments if I have an MMM?
Yes. An observational model can be biased, and experiments such as geo holdouts give it a causal anchor. Practitioners describe combining the two as the state-of-the-art approach to decision-grade insight.
Sources
- Open Source MMM Tools Cut Costs but Not Expertise Barriers, ORM News
- Marketing Mix Modeling at Qonto, Part I, The Qonto Way
- Bayesian Marketing Mix Modeling: adstock, saturation, calibration and budget optimization in 2026
- Meridian is now available to everyone, Google
- google/meridian on GitHub
- facebookexperimental/Robyn on GitHub
- graphsimulacrum on PyPI