PyMC-Marketing vs Google Meridian: Which MMM Framework Fits Your Team?

PyMC-Marketing and Google Meridian are the two open-source Bayesian marketing mix modeling frameworks getting the most attention in 2026. They sit in the same conceptual space, both built on probabilistic programming, both designed to replace older closed-box MMM tools, and both produce the same headline outputs: channel contributions, ROI, response curves, and budget recommendations. But the technical and organizational tradeoffs between them are real, and the right choice has less to do with which framework is theoretically best and more to do with your team's existing tooling, the level of transparency you need, and how you plan to operationalize the model after the first build.

Why this comparison matters now

MMM has gone through three waves. The first was econometric MMM run by consultancies on quarterly cycles. The second was lightweight, automated MMM frameworks that prioritized speed over transparency. The third, where we are now, is Bayesian MMM that exposes priors, adstock, saturation, and uncertainty in a way teams can actually inspect. PyMC-Marketing and Meridian are both squarely in that third wave. Choosing between them is one of the first technical decisions a modern marketing science team makes, and it shapes everything from data engineering work to how stakeholders see the results.

Architecture at a glance

Both frameworks share the same statistical heritage and ultimately fit Bayesian regression-style models with non-linear transformations on media. The biggest practical differences show up in the surrounding ecosystem, the language, and how opinionated the API is.

Neither approach is universally better. PyMC-Marketing tends to feel more familiar to teams that already work in scientific Python, especially if analysts are comfortable inspecting model objects, priors, and posterior samples directly. Meridian is closer to a turnkey pipeline: more decisions are made for you, and the workflow assumes you are running the recommended pattern rather than customizing the model.

Adstock and saturation

Both libraries implement standard MMM transformations, but the level of explicit control differs.

• PyMC-Marketing: ships geometric, delayed, and Weibull adstock, plus logistic and Hill saturation, all exposed as configurable transformation classes you can swap or extend.
• Meridian: provides Google's recommended adstock and saturation forms with sensible defaults, optimized for the patterns Google has seen across advertisers.
• Practical impact: PyMC-Marketing makes it easier to test alternative functional forms or write a custom transformation. Meridian makes it easier to ship a defensible, opinionated default without re-debating modeling choices.

Priors, transparency, and inspectability

This is where the two frameworks diverge most for stakeholder communication. In PyMC-Marketing, every prior is a first-class object you can print, plot, and override. You can ask, in plain English, what does the model believe about TV before it sees the data, and the answer is a distribution you can show. In Meridian, priors are still configurable, but the typical workflow leans on Google's recommended structure.

Lift tests and calibration

Modern MMM workflows lean heavily on experiment calibration. Both frameworks support incorporating lift tests and incrementality results, but they expose calibration differently.

• PyMC-Marketing supports calibration through informative priors and lift-test likelihood terms, which Bayesian-fluent teams will find natural.
• Meridian provides built-in mechanisms for combining experiment results with the model in a more guided way, which can shorten the path from a lift test to a calibrated model.
• Either way, the quality of calibration depends on the quality of the experiments, not the framework.

Customization and extensibility

If your business has unusual measurement requirements (long-tailed B2B sales cycles, multi-touch retail funnels, multi-brand portfolios with shared media, geo-level structure across many markets), the framework's flexibility starts to matter more than its defaults.

Performance and scaling

Both frameworks can scale to realistic advertiser datasets: hundreds of weeks, dozens of channels, multiple regions or brands. PyMC-Marketing benefits from PyMC's NUTS sampler and JAX backend options, which are continuously improving. Meridian benefits from Google's optimization work for large hierarchical structures. In practice, the bottleneck for most teams is not raw sampler speed but how often they re-fit and how clean their data is.

Community and trajectory

• PyMC-Marketing has an active open-source community, regular releases, public roadmap discussions, and adoption from agencies and in-house teams.
• Meridian benefits from Google's brand recognition and the trust some advertisers have in tooling that comes from a major platform.
• Both have responsive maintainers, and both are improving rapidly. Neither is a frozen library.

Operationalizing the model

This is the part that the framework choice does not solve. Whichever you pick, you still need to decide how data lands in the model, how often you refresh, who owns the inputs, how outputs reach decision-makers, and how scenario analysis happens after fitting. A great framework with a notebook-only workflow can still leave the team manually emailing screenshots every quarter.

When to choose PyMC-Marketing

• Your team is already in scientific Python and wants direct control over model structure
• Stakeholders care about inspecting priors and assumptions
• You need to extend the model with custom transformations or likelihoods
• You want a framework with a community-driven, transparent roadmap

When to choose Meridian

• You prefer an opinionated, end-to-end pipeline with strong defaults
• Your team trusts Google's modeling perspective and recommended patterns
• You are already invested in the TensorFlow ecosystem
• You want a more turnkey path from data to fitted model

Where SIMBA fits

SIMBA is built on PyMC-Marketing because we believe the transparency story is decisive for marketing science teams that need to defend their numbers internally. We keep PyMC-Marketing as the modeling engine and add the operational layer most teams build themselves: a guided data upload and validation workflow, ROI and contribution dashboards, scenario planning, and budget optimization. Read more about that approach on the PyMC-Marketing platform page, or see Bayesian MMM for the methodology underneath.