Meridian vs Robyn

Verdict

Meridian (Google) and Robyn (Meta) are the two default choices when a team decides to build its own marketing mix model rather than buy a managed one. Neither is a hosted product: both are free, open-source code released by an ad platform, with no dashboard, account, or support line, and both require a data scientist comfortable with statistical modeling plus multiple years of weekly, ideally geo-level, spend and outcome data before the output is trustworthy. The real difference is language and method emphasis: Meridian is a Python library using Bayesian inference, built as the successor to Google's earlier LightweightMMM and able to ingest Google-specific reach and frequency data where available. Robyn is an R package (with a community Python port) using ridge regression combined with a genetic-algorithm-driven search for adstock and saturation parameters. If your data team lives in Python and wants a Bayesian approach, Meridian fits naturally; if your team is R-native or wants Robyn's automated hyperparameter search, that points the other way.

Choose Meridian if

• Your data science team works primarily in Python and is comfortable with Bayesian modeling.
• You use Google Ads heavily and want to incorporate reach and frequency signals where Google exposes them.
• You want a model that reports posterior uncertainty rather than a single point estimate.

Choose Robyn if

• Your team works primarily in R, or is comfortable using the community Python port.
• You want an automated, genetic-algorithm-driven search across many candidate models rather than configuring priors by hand.
• You want the option to fall back on regression-based diagnostics that are widely documented, given Robyn's longer track record in the open-source MMM community.

What they share

Both are entirely free aside from compute cost, both are self-hosted code rather than SaaS, both require the same underlying commitment — years of clean, weekly spend and outcome history, plus someone qualified to validate the model's output against business reality — and both exist specifically because their parent platforms wanted an alternative to paying a third-party MMM vendor. Choosing either is choosing to become your own MMM vendor, with the platform's library as a starting point rather than a finished tool.

The honest caveat

Neither tool has a support line, and a wrong or overconfident model from either one will produce a fluent, plausible-looking budget recommendation that is nonetheless incorrect — there's no vendor to catch that mistake. If you don't have someone in-house who can validate model output against a real experiment or business judgment, a managed alternative such as Recast — which pairs Bayesian MMM with geo-experiment calibration as a paid service — removes that risk at the cost of a subscription.