Recast alternatives

3 tools to consider instead of Recast, shown against it.

	Recast	Robyn	Meridian	Haus
Vendor	Recast	Meta	Google	Haus
Pricing model	Quote only	Open source + paid options	Open source + paid options	Quote only
Free tier	No	Yes	Yes	No
Deployment	Cloud	Self-hosted	Self-hosted	Cloud
Open source	No	Yes (MIT)	Yes (Apache-2.0)	No
Best for	Advertisers wanting rigorous MMM output without building and maintaining the statistical model in-house.	Teams with an in-house data scientist and multi-year weekly spend history who want full control over their MMM rather than a managed SaaS.	Teams with a data scientist comfortable in Python and Bayesian statistics who want full control over their MMM rather than a managed vendor.	Advertisers with enough scale to run randomized holdout experiments and want causal proof of channel lift, not modeled estimates.
Pricing	Custom quotes based on ad spend and number of channels modeled; pricing is not published. Pricing has not been verified yet — see the vendor's site.	Free, open-source R package; the only cost is the compute and the data scientist's time to run and maintain it. Pricing has not been verified yet — see the vendor's site.	Free, open-source Python library; the only cost is compute and the data scientist's time to build and maintain the model. Pricing has not been verified yet — see the vendor's site.	Custom quotes based on ad spend and number of tests; pricing is not published. Pricing has not been verified yet — see the vendor's site.
Features	Managed Bayesian media mix modeling Geo-experiment design to validate model estimates Diminishing-returns and saturation curves by channel Budget allocation and scenario simulation Regular model refreshes as new data arrives Statistician-assisted setup and interpretation	Ridge regression with adstock and saturation transformations Genetic-algorithm-driven hyperparameter search Budget allocation optimizer output Support for multiple media and non-media variables Model diagnostics and one-pager output plots Community Python port available	Bayesian marketing mix modeling in Python Optional geo-level modeling for regional data Incorporates reach and frequency signals where available Budget optimization and scenario simulation Successor to Google's LightweightMMM project Model diagnostics and posterior-uncertainty reporting	Randomized geo holdout and matched-market experiment design Statistical significance testing on experiment results Incremental lift measurement by channel Experiment calibration for marketing mix models Automated test scheduling and monitoring Channel-level budget recommendation from test results

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