Modellix VS mbuzz.co

mbuzz.co's answer:

Every attribution tool runs a model under the hood and reports its number like it came from physics. mbuzz is the only one that shows the model. Eight of them side by side, plus a SQL-like DSL to edit or write your own. You stop arguing about which channel works and start arguing about which model you should trust.

mbuzz.co's answer:

Dreamdata, HockeyStack, and Northbeam all ship with a proprietary "data-driven" model you can't see inside. You pay $1,400–$5,000 a month to trust their math. mbuzz runs eight models you can inspect, lets you edit the logic in a SQL-like DSL, keeps your data exportable on every plan, and starts at $0. For a $1–100M company spending $20K–$1M a month on ads, that's the difference between renting an attribution tool and owning an attribution stack.

Modellix's answer

AI developers, AI SaaS builders, indie developers, cross-border e-commerce teams, marketing agencies, content creators, digital product teams, ISVs, and enterprises integrating AI media generation

mbuzz.co's answer:

Technical marketers, marketing ops, growth engineers, and data-savvy CMOs at startups and mid-market SaaS, DTC, fintech, and healthtech companies spending $20K–$1M a month on paid media. Specifically the ones who've stopped trusting their dashboard — who want to audit the math themselves, not hear "trust our algorithm."

Modellix's answer

I am part of the Modellix team, focused on making AI model access simpler for developers and businesses. The goal is to provide one reliable platform where teams can explore, compare, and integrate leading AI models without the hassle of managing multiple APIs.

mbuzz.co's answer:

Years of wrestling with the limitations of various existing solutions, platform-inflated ROAS, and enterprise attribution tools that cost more than the budgets they were measuring. Every tool I tried picked one model and hid the math. I wanted to compare models, argue with them, and write my own rules — so I built one. mbuzz is the attribution platform I wished existed when I was trying to explain channel performance to a CFO who didn't believe the Meta pixel.

Modellix's answer

Node.js, Python, React, Next.js, TypeScript, PostgreSQL, Redis, AWS, Docker, Stripe, Cloudflare

mbuzz.co's answer:

Ruby on Rails (backend + dashboard), PostgreSQL, Sidekiq for background jobs, Stimulus/Turbo for the frontend. Open-source SDKs in Ruby, Node, Python, and PHP. Deployed via Kamal on DigitalOcean.