Kedro vs MLflow

Kedro

Framework for production-quality, reproducible data science code

MLflow

Open-source AI platform for agents, LLMs & ML models

Feature Kedro MLflow
Category AI / ML AI / ML
Sub-category ML Pipeline ML Pipeline
Maturity stable mature
Complexity intermediate intermediate
Performance tier medium enterprise grade
License Apache-2.0 Apache-2.0
License type permissive permissive
Pricing fully free fully free
GitHub stars 10.0K 20.0K
Contributors 250 800
Commit frequency weekly daily
Plugin ecosystem none large
Docs quality excellent excellent
Backing org McKinsey QuantumBlack Linux Foundation / Databricks
Funding model corporate foundation
Min RAM 512 MB 1 GB
Min CPU cores 1 1
Scaling pattern single_node horizontal
Self-hostable Yes Yes
K8s native No Yes
Offline capable No No
Vendor lock-in none none
Languages Python Python, Java, R
API type SDK REST, SDK
Protocols HTTP HTTP
Deployment pip pip, docker
SDK languages python python, java, r, javascript
Team size fit solo, small, medium solo, small, medium, enterprise
First release 2019 2018
Latest version

When to use Kedro

  • Standardize ML project structure across teams
  • Build reproducible data transformation pipelines
  • Visualize data dependencies with Kedro-Viz
  • Transition from notebooks to production code

When to use MLflow

  • Track and compare ML experiments across teams
  • Version and deploy models to production
  • Monitor LLM applications with tracing
  • Manage AI Gateway for multi-provider LLM access
  • Evaluate and optimize prompts systematically

Kedro anti-patterns

  • Opinionated project structure may not fit all teams
  • Learning curve for catalog system
  • Less suited for real-time or streaming
  • Smaller community than Airflow/MLflow

MLflow anti-patterns

  • Not a training framework itself
  • Self-hosted tracking server needs PostgreSQL setup
  • UI can be slow with very large experiment counts
Full Kedro profile → Full MLflow profile → All comparisons