# The best marketing mix modeling tools > Marketing mix modeling tools compared: free ones from Google and Meta, plus Recast, Haus, Measured and Lifesight. What each does and costs, checked 2026-10-20. - **Author**: Marxx - **Published**: 2026-11-18 - **Category**: Reporting tools - **URL**: https://marxx.ai/library-best/best-marketing-mix-modeling-tools --- The best marketing mix modeling (MMM) tools are free open-source code from Google (Meridian), Meta (Robyn) and PyMC Labs (PyMC-Marketing), and paid platforms such as Recast, Haus, Measured and Lifesight. Free code costs nothing but needs an analyst. The paid platforms hide their prices, so you book a demo to get a quote. MMM is a statistical model that reads your past spend and sales by channel. It tells you how much each channel added to sales. It needs no cookies or user tracking. For how it differs from click-based tracking, read [MMM vs MTA](/compare/mmm-vs-mta). ## Quick comparison | Tool | Type | Best for | Price | |---|---|---|---| | Google Meridian | Open source, Python, Apache-2.0 | Teams with an analyst and geo-level data | Free to use. You run it | | Meta Robyn | Open source, R, MIT licence | Teams that know R | Free to use. You run it | | PyMC-Marketing | Open source, Python, Apache 2.0 | Data scientists who want Bayesian models | Free to use. You run it | | Recast | Paid platform | Brands that want forecasts and planning | Not listed publicly | | Haus | Paid platform | Brands that want MMM tied to experiments | Not listed publicly | | Measured | Paid platform | Larger brands | Not listed publicly | | Lifesight | Paid platform | Teams that want MMM, tests and attribution together | Not listed publicly | Checked on each tool's own site or repository on 2026-10-20. ## The free tools **Google Meridian.** Google calls it an MMM framework that lets advertisers run their own in-house models. It uses Bayesian methods, works with geo-level data and can also model at national level. It can use experiment results to calibrate the model. It is on [GitHub](https://github.com/google/meridian). **Meta Robyn.** Meta Marketing Science maintains it. The repository calls it a semi-automated, experimental tool. The core package is written in R, and the Python version is in beta. **PyMC-Marketing.** A Python library from PyMC Labs for Bayesian marketing analysis. It covers media effect models and customer lifetime value models. ## The paid platforms **Recast.** Describes a Bayesian MMM, geo experiments, forecasting, budget optimisation and an MCP server. Its page shows no price and asks you to book a demo. **Haus.** Offers incrementality experiments, a causal MMM it says updates every week, and causal attribution. It lists four plans, Basics, Core, Plus and Enterprise, with no prices. Basics is aimed at DTC brands selling on Shopify and Amazon. **Measured.** Combines experiments with a test-calibrated MMM. Its site speaks to enterprise brands and shows no price. **Lifesight.** Sells annual subscriptions scoped to your business. Causal MMM is on its Precision plan and not on Performance. It also offers an MCP server for Claude and ChatGPT. ## What an MMM tells you Say your model reports that INR 10,00,000 of Meta spend drove INR 30,00,000 in sales, and INR 5,00,000 of Google spend drove INR 7,50,000. 1. Meta returns INR 30,00,000 / INR 10,00,000 = INR 3.00 for each rupee spent. 2. Google returns INR 7,50,000 / INR 5,00,000 = INR 1.50 for each rupee spent. 3. The model also says the last INR 1,00,000 on Google brought only INR 60,000, because each extra rupee earns less. You would move some Google budget to Meta, then check the result with a test. These figures are made up. See [incrementality](/glossary/incrementality) and [geo-lift tests](/glossary/geo-lift-test). ## How to choose 1. Do you have an analyst? If yes, try Meridian or Robyn first. 2. No analyst? Ask two paid vendors for a quote and a sample report. 3. Is your spend small? An MMM may be more than you need. A [lift test](/best/best-incrementality-testing-tools) is a lighter first step. We make Marxx. It is not an MMM. It reads your connected Meta ad account in full, with creative tagged by hook, angle, format and offer, and reads Google Ads spend, campaigns and search terms. That shows the ads behind the channel totals a model gives you. For click-based tools, see the [best attribution tools for D2C brands](/best/best-attribution-tools-for-d2c-brands). ## FAQ ### What data does MMM need? Spend and sales by channel over time, often by week. Google's Meridian also accepts geo-level data. ### Is MMM better than attribution? They answer different questions. MMM looks at totals over time and needs no user tracking. Attribution follows individual clicks. ### Can a small brand use MMM? The free tools are open to anyone, but you need someone to run them. A lift test is a lighter first step. --- - [More Reporting tools articles](https://marxx.ai/library-best/category/reporting-tools) - [All articles](https://marxx.ai/library-best)