Make (Integromat) vs MLflow: Complete Comparison (2026)
Choosing between Make (Integromat) and MLflow is a common decision for ai & machine learning buyers in 2026. Make (Integromat) has been in the market since 2012, giving it a 6-year head start over MLflow (founded 2018). Make (Integromat) serves 800K+ users while MLflow has 500K+ users globally. Make (Integromat) differentiates with visual workflow builder and 1500+ app integrations, while MLflow leads with experiment tracking and model registry. In this head-to-head comparison, Make (Integromat) earns a higher hiltonsoftware.co score of 94/100 — but the right choice depends on your specific needs, budget, and team size.
Quick Comparison
Feature-by-Feature Comparison
Pros & Cons at a Glance
After comparing Make (Integromat) and MLflow across features, pricing, and user satisfaction, Make (Integromat) takes the lead with a score of 94/100 versus MLflow's 88/100. Make (Integromat)'s key advantages include "very powerful and flexible automation" and "better than zapier for complex flows". That said, MLflow has its own strengths — particularly "free and open-source" — making it a viable alternative for specific use cases.
Both Make (Integromat) and MLflow offer free plans, lowering the barrier to entry. Make (Integromat)'s paid plans start at $9/mo while MLflow begins at Free. Evaluate which paid features — AI tools, Error handling (Make (Integromat)) vs Model serving, Project packaging (MLflow) — justify upgrading for your team.
Bottom line: Choose Make (Integromat) if you need power users building complex, multi-step automations between apps. Go with MLflow if your priority is ml teams wanting free, open-source experiment tracking and model management. Both are strong ai & machine learning tools — we recommend trying the free plan of each before committing.
Power users building complex, multi-step automations between apps.
ML teams wanting free, open-source experiment tracking and model management.