Kedro
Claim this toolAn open-source framework for building production-ready data engineering and data science pipelines.
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Tracked since2026
The Bottom Line
Entry price
Free, no paid tier
Biggest pro
Promotes clean, reproducible, and maintainable code for data pipelines.
Biggest con
Requires familiarity with Python and software engineering concepts.
TL;DR - Kedro
- Applies software engineering best practices to data and ML pipelines.
- Provides tools for pipeline visualization, data cataloging, and project standardization.
- Enables reproducible, maintainable, and modular data science and engineering code.
Pricing: Free forever
Best for: Individuals & startups
What is Kedro?
Kedro is an open-source Python framework that applies software engineering best practices to the development of data and machine learning pipelines. It provides a structured approach to building reproducible, maintainable, and modular code, helping data scientists and engineers transition seamlessly from exploratory development to production.
The framework offers scaffolding for complex projects, standardizes code organization, and includes tools for pipeline visualization, data cataloging, and flexible deployment. By abstracting away tedious 'plumbing' tasks and automating dependency resolution, Kedro allows teams to focus on problem-solving and collaborate more effectively on data and ML initiatives.
Available on: Web
Pros & Cons
Pros
- Promotes clean, reproducible, and maintainable code for data pipelines.
- Standardizes project structure and encourages team collaboration.
- Reduces time spent on 'plumbing' and allows focus on core problems.
- Facilitates seamless transition from development to production.
- Offers extensive integrations with popular data and ML tools.
Cons
- Requires familiarity with Python and software engineering concepts.
- May have a learning curve for users new to structured pipeline development.
Preview
Key Features
Pipeline Visualisation (Kedro-Viz) with data lineage and execution detailsData Catalog with lightweight connectors for various file formats and systems (S3, GCP, Azure, Pandas, Spark, etc.)Project Template for standardizing configuration, code, tests, and notebooksDedicated IDE support for Visual Studio Code (code navigation, autocompletion)Pipeline Abstraction with dataset-driven workflow and automatic dependency resolutionCoding Standards (pytest, Sphinx, ruff, Python logging)Flexible Deployment (single/distributed machine, Argo, Prefect, Kubeflow, AWS Sagemaker, Databricks)
Pricing Plans
Pricing checked Sep 15, 2026
Open Source
Free
- Clean Code
- Handles Complexity
- Standardisation
- Production-Ready
- Pipeline Visualisation (Kedro-Viz)
- Data Catalog
- Integrations (Amazon SageMaker, Apache Airflow, Apache Spark, Azure ML, Dask, Databricks, Docker, fsspec, Jupyter Notebook, Kubeflow, Matplotlib, MLflow, Plotly, Pandas, VertexAI)
- Project Template
Is Kedro worth the price?
100/100
Kedro's pricing is exceptionally generous as it is entirely free, offering a robust set of features for building production-ready data pipelines.
This makes it an incredibly fair and accessible option compared to proprietary solutions. It is best for data scientists and engineers looking for a powerful, open-source framework without any financial commitment.
Reviews
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Kedro FAQ
How does Kedro help data scientists build production-ready solutions?
Kedro applies software engineering best practices to data and machine learning pipelines, providing a structured approach for reproducible, maintainable, and modular code. This framework helps data scientists and engineers move smoothly from exploratory development to production environments.
Which teams benefit most from using Kedro?
Kedro is best suited for data science and data engineering teams that need to standardize project structure and improve collaboration on data and ML initiatives. It helps teams focus on core problem-solving by abstracting away tedious 'plumbing' tasks.
How does Kedro compare to Prefect for building data pipelines?
Kedro is an open-source Python framework focused on applying software engineering best practices to pipeline development, emphasizing code structure and reproducibility. Prefect is also a data workflow management system, but Kedro specifically provides scaffolding and standardization for project organization from development to production.
What kind of limitations should users be aware of when adopting Kedro?
Users adopting Kedro should be familiar with Python and general software engineering concepts. There may also be a learning curve for individuals new to structured pipeline development methodologies.
Does Kedro include a free tier?
Kedro is free to use as an open-source Python framework. There are no paid plans required to access its features for building data engineering and data science pipelines.
Can Kedro integrate with other data and ML tools?
Yes, Kedro offers extensive integrations with popular data and machine learning tools. It provides features like data cataloging and flexible deployment options to work within various ecosystems.
How does Kedro facilitate collaboration among team members?
Kedro standardizes code organization and project structure, which promotes clean, reproducible, and maintainable code. This standardization helps teams collaborate more effectively on data and ML initiatives by providing a consistent framework.
Source: kedro.org