Kedro-Viz vs MLflow
Side-by-side — pricing, strengths, tradeoffs, and fit.
Kedro-Viz
Visualize and track Kedro pipelines for data science projects.
MLflow
Manage the ML lifecycle with experimentation, reproducibility and deployment.
At a glance
Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0
Overview
One-liner
One-liner
Visualize and track Kedro pipelines for data science projects.
One-liner
Manage the ML lifecycle with experimentation, reproducibility and deployment.
Maturity
Maturity
EstablishedMaturity
EstablishedOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Teams working with Kedro who need to visualize and debug their pipelines
Projects that require detailed tracking of experiments and data flow
Developers looking for a tool to optimize the performance of their machine learning workflows
✓ Best for
Data science teams needing a unified platform for experiment tracking, model deployment, and reproducibility.
Organizations looking to standardize their machine learning workflows across different projects and teams.
Not ideal for
✕ Not for
Users not familiar with Python or Kedro framework
Teams requiring real-time visualization updates during pipeline execution
✕ Not for
Teams requiring real-time analytics or streaming data processing as MLflow focuses on batch operations.
Projects that need a fully managed cloud service without the overhead of self-hosting.
Strengths
↑ Strengths
Interactive visualization of Kedro pipelines
Experiment tracking and comparison
Support for large datasets and complex pipelines
Integration with Kedro project structure
↑ Strengths
Experiment tracking
Model registry for versioning and lifecycle management
Deployment tools to serve models in production
Weaknesses
↓ Weaknesses
Limited to Kedro pipelines only
Performance issues with very large pipelines
Documentation lacks depth on advanced use cases
Dependence on Python ecosystem
↓ Weaknesses
Limited native support for non-Python languages
Performance issues at scale