How We Compare

Given the popularity of the Media Mix Modelling (MMM) approach, numerous packages are available. Below is a concise comparison:

Feature	PyMC-Marketing	Robyn	Orbit KTR	Meridian*	AMMM
Language	Python	R	Python	Python	Python
Approach	Bayesian	Traditional ML	Bayesian	Bayesian	Bayesian
Foundation	PyMC	-	STAN/Pyro	TensorFlow Probability	PyMC + JAX
Company	PyMC Labs	Meta	Uber	Google	Independent
Open source	Yes	Yes	Yes	Yes	Yes
Model Building	Yes	Yes	Yes	Yes	Yes
Out-of-Sample Forecasting	Yes	No	Yes	No	No
Budget Optimiser	Yes	Yes	No	Yes	Yes
Time-Varying Intercept	Yes	No	Yes	Yes	Yes
Time-Varying Coefficients	Yes	No	Yes	No	No
Custom Priors	Yes	No	No	Yes	Yes
Custom Model Terms	Yes	No	No	No	No
Lift-Test Calibration	Yes	Yes	No	Yes	Yes
Geographic Modelling	Yes	No	No	Yes	No
Unit-Tested	Yes	No	Yes	Yes	Yes
MLflow Integration	Yes	No	No	Yes	No
GPU Sampling Acceleration	Yes	N/A	No	Yes	Yes
Prophet Integration	No	No	No	No	Yes
Automated Outlier Handling	No	No	No	No	Yes
Model Selection (ELPD)	No	No	No	No	Yes
Transfer Entropy Analysis	No	No	No	No	Yes
Stationarity Testing	No	No	No	No	Yes
Consulting Support	Provided by Authors	Third-party agency	Third-party agency	Third-party agency	Provided by Author

*Meridian has been released as successor of Lightweight-MMM, which has been deprecated by Google

Last updated: 2025-08-07
Last reviewed: 2025-10-06

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Key Takeaway

These libraries for MMM models implement different flavours of Bayesian models. While they share a broadly similar statistical foundation, they differ in API flexibility, underlying technology stack, and implementation approach.

PyMC-Marketing is a widely used open-source library with an extensive feature set and strong community support. Its flexibility makes it suitable for teams with complex requirements, though this breadth comes with a significant learning curve. While AMMM is built using PyMC (the underlying probabilistic programming framework), it is not a fork of PyMC-Marketing. AMMM represents a distinct implementation philosophy focused on statistical rigour, model stability, and practical usability for typical MMM use cases.

Other libraries have their own strengths. For example, Google Meridian features a more opinionated API and integration with the Google ecosystem, which can be advantageous for organisations already embedded in Google’s stack.

Your optimal choice should depend primarily on:

1. Your team’s technical expertise
2. Complexity of your data and client use cases
3. Preference for an independent open-source solution vs. one that is closed source

Our Recommendation

Choose Meta Robyn if:

• Your team primarily uses R instead of Python
• You prefer a “simpler” but less rigourous approach than Bayesian Models (Ridge regression)
• Your MMM data tends to be relatively simple and number of channels small

Choose Google Meridian if:

• You want a simplified (albeit less flexible) API to build models across geographies
• You want strong integration with other Google products such as Collab
• You have the expertise to work with and debug TFP (which can be non-trivial)

Choose PyMC-Marketing if:

• You want its advanced statistical modelling capabilities (e.g., Gaussian Processes) and understand the complexity–interpretability–stability trade-offs
• Integration into broader data science workflows is important (MLflow)
• You prefer independence from major ad publishers and networks
• Professional consulting support is available (but costly)

Choose AMMM if:

• Statistical rigour and model stability are your top priorities
• You want efficient use of degrees of freedom (Prophet integration for holidays vs individual dummy variables)
• Automated seasonality detection is preferred over manual specification (Prophet vs knots/splines)
• You need rigourous model diagnostics and selection criteria (ELPD, transfer entropy, stationarity tests)
• Your dataset has outliers that need robust handling (quasi-winsorisation)
• You prefer 100% in-sample inference for typical MMM sample sizes (52-104 weeks)
• You value a statistically sound, battle-tested approach over extensive customisation options
• Consulting support from the package author is available

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Glossary

• Out-of-Sample Forecasting: Producing predictions for future time periods beyond the observed time horizon. This is distinct from evaluating a model on a held-out test split within the observed data.