How We Compare

Given the popularity of the Media Mix Modelling (MMM) approach, numerous packages are available. Below is a concise comparison highlighting how PyMC-Marketing stands 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	✅	✅	✅	✅
Model Building	🏗️ Build	🏗️ Build	🏗️ Build	🏗️ Build
Out-of-Sample Forecasting	✅	❌	✅	❌
Budget Optimizer	✅	✅	❌	✅
Time-Varying Intercept	✅	❌	✅	✅
Time-Varying Coefficients	✅	❌	✅	❌
Custom Priors	✅	❌	❌	✅
Lift-Test Calibration	✅	✅	❌	✅
Geographic Modeling	✅	❌	❌	✅
Unit-Tested	✅	❌	✅	✅
MLFlow Integration	✅	❌	❌	❌
GPU Sampling Accelleration	✅	-	❌	✅
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

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Key Takeaway

Four of the five major libraries for MMM models 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. However, its breadth and depth also make it the most sophisticated option, which may require a steeper learning curve. Other libraries have their own strengths—for example, Google Meridian features a more opinionated API and seamless integration with the Google ecosystem, which can be advantageous for organizations already embedded in Google’s stack.

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

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 want strong integration with other Google products such as Collab

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 consulting support is desirable