How We Compare

Given the popularity of the Media Mix Modelling (MMM) approach, numerous packages are available. Below is a concise comparison highlighting how the features of PyMC-Marketing stand against other popular options:

Feature	PyMC-Marketing	Robyn	Orbit KTR	Meridian*
Language	Python	R	Python	Python
Approach	Bayesian	Traditional ML	Bayesian	Bayesian
Foundation	PyMC	-	STAN/Pyro	TensorFlow Probability
Company	PyMC Labs	Meta	Uber	Google
Open source	✅	✅	✅	✅
Out-of-Sample Forecasting	✅	❌	✅	❌
Budget Optimizer	✅	✅	❌	✅
Time-Varying Intercept	✅	❌	✅	✅
Time-Varying Coefficients	✅	❌	✅	❌
Custom Priors	✅	NA	❌	✅
Custom Model Terms	✅	❌	❌	❌
Lift-Test Calibration	✅	✅	❌	✅
Hierachical Geographic Modeling	✅	❌	❌	✅
Standardized Database Connectors	✅ (with Fivetran)	❌	✅	✅ (limited to Google ecosystem)
Unit-Tested	✅	❌	✅	✅
MLFlow Integration	✅	❌	❌	✅
Multiple Sampling Backends	✅	NA	❌	✅
GPU Sampling Acceleration	✅	NA	❌	✅
Consulting Support	Provided by Authors	Third-party agency	Third-party agency	Third-party agency

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

Last updated: 2025-10-17

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Key Takeaway

Four of the five major libraries for MMMs implement different flavors of Bayesian models. While they share a broadly similar statistical foundation, they differ in API flexibility, underlying technology stack, and implementation approach.

PyMC-Marketing stands out as the most widely used library by PyPI downloads (see plot below), offering unmatched flexibility and a comprehensive set of advanced features. This makes it ideal for teams looking for a highly customizable, state-of-the-art solution. Its breadth and depth open the door to deeper understanding and mastery for those willing to explore its full capabilities.

However, other libraries have their own strengths — for example, Robyn is popular in the R community and provides extensive tutorials and documentation.

Your optimal choice should depend primarily on:

1. Your team’s technical expertise

2. Your primary advertising channels

3. Preference for an independent open-source solution vs. one sponsored by Ad Networks

Detailed Performance Benchmark

When it comes to Bayesian Media Mix Modeling the two most used options are PyMC-Marketing and Google Meridian. Our comprehensive technical benchmark comparing PyMC-Marketing against Google Meridian across realistic datasets (from startup to enterprise scale) reveals PyMC-Marketing’s superior performance: 2-20x faster sampling, 40% lower error in channel contribution estimates, and successful scaling to large enterprise datasets where Meridian fails to converge. PyMC-Marketing’s flexible sampling backends (NumPyro, BlackJAX, Nutpie) provide significant advantages over Meridian’s fixed TensorFlow Probability implementation. See our detailed benchmark analysis for complete results and open-source methodology.

Our Recommendation

Choose Meta Robyn if:

• Your team primarily uses R instead of Python

• You prefer a simpler but less rigorous approach than Bayesian Models (Ridge regression)

• You want direct integration with Meta/Facebook advertising data

Choose Google Meridian if:

• You want a simplified (albeit less flexible) API to build models across geographies

• Direct integration with the Google advertising ecosystem is important

• You can allow for reduced predictive accuracy and explainability

Choose PyMC-Marketing if:

• Maximum flexibility for complex, unique business requirements is necessary

• You need advanced statistical modeling capabilities (e.g., Gaussian Processes)

• Production ready setup and integration into broader data science workflows is important (MLflow)

• You prefer independence from major ad publishers and networks

• Professional independent consulting support is desirable info@pymc-labs.com