Kedro vs Amazon SageMaker
Side-by-side comparison to help you choose the best tool.
Kedro
freeKedro is an open-source Python system for creating reproducible, maintainable, and modular data science code with pipeline orchestration. Developed by McKinsey QuantumBlack and donated to the Linux Foundation, it brings software engineering best practices like modularity and testing to data science projects. Kedro provides a standardised project structure, a data catalogue, and pipeline visualisation.
Amazon SageMaker
paidAmazon SageMaker is the leading fully managed ML platform for building, training, and deploying ML models at scale on AWS. Its features span data labeling, feature engineering, model training, automated tuning, and deployment - with SageMaker JumpStart providing pre-built models and tools. Used by thousands of enterprises for production ML workloads across every industry.
| Feature | Kedro | Amazon SageMaker |
|---|---|---|
| Pricing | free | paid |
| Category | - | - |
| Rating | 4.2 | 4.4 |
| Best For | Data science teams who want to apply software engineering best practices to their projects | Enterprise data science teams on AWS needing a fully managed ML platform for the complete model development and deployment lifecycle |
| Views | 5 | 6 |
Pros
- Excellent code organisation and modularity
- Strong software engineering principles
- Good documentation
Cons
- Learning curve for data scientists unfamiliar with software engineering
- Less real-time monitoring than alternatives
Pros
- Most mature managed ML platform
- JumpStart provides hundreds of pre-built solutions
- Scales to enterprise-level training workloads
Cons
- Complex pricing with many components
- Steep learning curve for full feature utilisation
- Modular pipeline nodes
- Data catalogue abstraction
- Project templating
- Pipeline visualisation
- Plugin ecosystem
- Managed ML training & deployment
- SageMaker JumpStart (pre-built models)
- Automated hyperparameter tuning
- Real-time & batch inference
- Feature Store & data processing