-1.png?width=1132&height=292&name=Asset%201@2x%20(12)-1.png "Odins.ai Logo")
Compare
Odins vs building in-house
With Meridian and PyMC-Marketing free to use, building your own MMM is a fair question. Some companies should. Most underestimate what owning a model costs after the first version ships.
Reviewed August 2026 · sources cited on this page
Odins
Managed marketing investment platform
In-house MMM
Built on open frameworks by your team
At a glance
Side by side.
The short version of the whole page. Details and sources below.
Effort estimates for the open frameworks: Runge and Pauwels (2026), SSRN 6317979. In-house staffing examples: companies running MMM in-house at scale typically dedicate 5 to 10 data scientists to it.
Sometimes building is right.
This is not a scare page. Companies with large, mature data organizations run serious in-house MMM, and for some of them it is the correct call.
- Teams like Bolt and HelloFresh run in-house MMM on 5 to 10 dedicated data scientists
- Full control: methodology, code and data never leave the building
- If you are hiring modeling talent anyway, an open framework is a strong starting point
- The frameworks themselves (Meridian, PyMC-Marketing) are good, and free to license
The costs that surface later.
The build decision usually gets made on license price and the first model. The real cost sits in years two and three: retraining, validating, and keeping an interface alive while the people who built it move on.
- We have watched well-resourced companies build in-house, slip on timelines, and struggle to put the result to work
- A model that is not retrained goes stale within quarters, and quietly
- A model without a marketing-team interface becomes a report nobody opens
- Verification is the part in-house builds skip first, and the part trust depends on
The choice
Who should choose which.
Run the total-cost-of-ownership math before choosing either way. It usually decides this.
Choose building in-house if
- You have, or will hire, 5 to 10 data scientists with Bayesian depth and a long-term mandate
- Marketing measurement is core intellectual property for your business model
- You can wait months for the first decision-grade model
Choose Odins if
- You want decision-grade MMM without hiring for it
- Time to value matters: first model in weeks, verified from the start
- You would rather own the decisions than the infrastructure
The honest overlap
The honest math is salaries, months of build time and permanent maintenance against a subscription. If in-house still wins after that calculation, build. For most mid-market teams it does not.
Frequently asked questions.
Can we not just use Google Meridian ourselves?
Yes, it is open source and technically strong. Plan for 2 to 3 data scientists, months to a production setup, and permanent ownership of retraining and validation. We compare against it directly on the Meridian page.
What do companies that succeed in-house have in common?
Large, mature data organizations, 5 to 10 people dedicated to the model, and modeling as a long-term strategic capability rather than a project.
How long does an in-house build take?
The 2026 academic review estimates a single modeling run at a day to two weeks depending on the framework, after the data is prepared. A production system with data pipelines, retraining, validation and an interface is a different project, and it is measured in months.
We already started building. Is that wasted?
No. The data work transfers directly, and what your team learned makes the prior sessions better. Some teams also run Odins alongside an internal model as a benchmark.
Which framework should we pick if we do build?
Honestly: PyMC-Marketing if you want flexibility and have deep Bayesian skills, Meridian if you want the most packaged Bayesian option with a strong optimizer. Robyn is the fastest first run but frequentist, and Meta has stopped active development.
Marketing spend, modeled like an investment.
A 30-minute walkthrough of what the managed version of this would look like on your channels.
Book a demo