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Comparison
Odins vs. Google Meridian
Google Meridian is a serious open-source MMM framework — and a genuine step forward for the field. But open source and ready to use are not the same thing. Here's an honest look at where Meridian and Odins differ, who each is built for, and what that means in practice.
Marketing intelligence that drives growth
600+ managed integrations vs. build every pipeline yourself
Odins connects to over 600 marketing platforms and manages every integration end to end — setup, maintenance, monitoring. APIs change, tokens expire, schemas update. We handle all of it. Your data flows in daily, structured to your business hierarchy, without your team touching an API.
Meridian includes no data connectors. Your engineering team builds and maintains every pipeline — to Google Ads, Meta, TikTok, your CRM, your offline channels. Data cleaning, campaign mapping, and transformation are all manual. For most organizations, this is the single largest cost of running Meridian: not the model, but the data infrastructure around it.
- Odins: fully managed data pipeline, monitored daily
- Meridian: bring your own data — every source, every pipeline
- Odins: digital via API, offline via simple templates
- Meridian: estimated 3–4 FTEs for data engineering alone
Designed for single-country markets — not just the American one
Meridian was built with the US market in mind. Its hierarchical geo-level model works best with 50+ geographic units — the 210 US DMAs are ideal. Google's own data platform provides pre-built DMA-level data for US markets.
In Norway, the Netherlands, or most of Europe, you have one national time series. Maybe a handful of regions. Meridian's geo-level advantages largely disappear, while the implementation burden stays the same.
Odins was built for this reality. Our Bayesian approach uses carefully constructed informative priors — encoding business knowledge and market context — to produce credible estimates even with single-country data. You don't need 50 states to get a useful model. You need the right priors and the right methodology.
- Meridian: designed for 50+ geo units (US DMAs)
- Odins: built for single-country European markets
- Google's data platform has limited coverage outside the US
- Bayesian priors compensate for fewer geographic units
Expert priors built in — not a blank slate you configure yourself
Meridian gives you the mechanism for informative priors, but the thinking is on you. You decide what the priors should be for every channel, every parameter. Google's own documentation warns that using identical uninformative priors across channels can lead to ROI estimates that 'differ by orders of magnitude.' Getting this right requires deep Bayesian expertise and marketing domain knowledge.
Odins takes a fundamentally different approach. We sit down with your team — typically 2–3 sessions — and document what you believe about each channel's effectiveness. We then encode that knowledge as priors, drawing on our experience across dozens of companies in similar markets. The data refines those beliefs. The result is a model that starts informed, not ignorant — and converges faster because it has somewhere sensible to start.
- Odins: priors set collaboratively with business experts
- Meridian: priors are the user's responsibility to configure
- Odins: cross-company experience informs starting assumptions
- Meridian: Google warns that default priors can be misleading
Independent measurement — not built by the platform being measured
Meridian is built by Google. Google is also one of the largest advertising platforms in the world. This creates a structural tension that independent analysts — including Forrester — have flagged.
Meridian natively integrates Google Ads data, YouTube reach and frequency data, and Google Search query volume. No equivalent integration exists for Meta, TikTok, Amazon, or any other platform. Richer data for Google channels mechanically improves their measured performance relative to channels with sparser data. This isn't a conspiracy — it's a natural consequence of the design.
Odins has no advertising business. We don't sell media. We don't benefit when one channel looks better than another. The model treats every channel with the same methodology and the same data standards. Independent measurement means the numbers are yours to trust.
- Meridian: built by Google, which also sells the ads being measured
- Odins: no advertising business — purely independent
- Meridian: native data integration only for Google channels
- Odins: same data standards and methodology for every channel
A team behind the model — not just the model itself
Meridian is a Python library. To use it, you need data scientists who can write code, run Bayesian models, diagnose convergence issues, and interpret posterior distributions. You need data engineers for the pipelines. And you need marketing analysts to translate outputs into decisions. Industry estimates put a full Meridian implementation at 3–4 FTEs over 6–12 months.
Odins is a managed service. We build the model, maintain the integrations, retrain monthly, and review every recommendation before it reaches you. Your involvement is typically 2–3 onboarding sessions, then a monthly review of results and recommendations. You get the rigor of a quantitative system and the judgment of a team that's seen this across dozens of companies.
- Meridian: requires data science team to implement and maintain
- Odins: fully managed — we build, run, and review the model
- Meridian: 6–12 month implementation timeline
- Odins: first model delivered in 4–6 weeks
Weeks, not months
From kickoff to first model in 4–6 weeks. Data connections are managed, the model is built by our team, and recommendations start flowing monthly. No 6-month implementation project.
Built for Europe
Designed from the ground up for single-country markets where you don't have 50 geographic units. Informative priors, expert knowledge, and Bayesian methodology that works with the data you actually have.
Decisions, not notebooks
Every month you get specific, ranked recommendations — reviewed by our team. Not a Jupyter notebook you need to interpret, but concrete actions with expected outcomes and confidence ranges.
Aprila Bank: from kickoff to 37% more applications
Aprila Bank — a fast-growing Norwegian fintech — wanted to optimize marketing spend across channels but didn't have the data science team to run an open-source framework. They chose Odins.
Within weeks, all marketing data was connected and structured. The model identified saturated channels and reallocated budget to channels with headroom. The result: total loan applications increased by over 37%, with cost efficiencies across the board.
"Odins.ai has been instrumental in helping us optimize our budgets and tackle the complexities of modern marketing. They've built a model that sharpens our understanding of resource allocation, ensuring more reliable decision-making across the board."
— Nicole Lenouvel Hanssen, Marketing Manager, Aprila Bank
When Meridian might be the right choice
We respect what Google has built with Meridian, and there are scenarios where it makes sense:
- You have a large in-house data science team that wants full control over every modeling decision and is comfortable maintaining Bayesian models in production.
- You operate primarily in the US with DMA-level data across 50+ geographic units, where Meridian's hierarchical model has the most statistical power.
- You want to deeply customize the model architecture — changing priors, adstock functions, or saturation parameters at the code level.
- Budget is less constrained than time — you can invest 6–12 months and 3–4 FTEs in implementation.
If that doesn't describe your situation — particularly if you're in a European market, need results in weeks not months, or don't have a dedicated data science team — Odins was built for you.
Is Meridian really free?
The software is free and open source (Apache 2.0). But the total cost of ownership — data engineers, data scientists, infrastructure, and ongoing maintenance — typically exceeds the cost of a managed platform. Industry estimates put a full implementation at 3–4 FTEs over 6–12 months.
Can I use Meridian without a data science team?
Not realistically. Meridian requires Python programming, Bayesian statistics knowledge, and the ability to diagnose model convergence issues. It's a library for data scientists, not a product for marketers. Without that expertise in-house, you'd need to hire or contract it.
Is the Google bias concern real?
It's a structural issue, not a conspiracy. Meridian natively integrates Google Ads, YouTube, and Search data — no equivalent exists for other platforms. Richer data mechanically improves measured performance. Forrester and AdExchanger have both flagged this as a legitimate concern for independent measurement.
What if we've already started with Meridian?
That's fine — the data work you've done isn't wasted. Odins can ingest data from existing pipelines, and any incrementality experiments you've run can inform the priors in our model. Some of our customers started with open-source frameworks before switching to a managed approach.