Kedro and MLflow are complementary tools, not competitors. Kedro is a pipeline framework that enforces how your data science code is structured, organized, and executed. MLflow is a lifecycle platform that tracks experiments, manages models, and monitors LLM applications in production. Kedro gives your team consistent, reproducible, and modular pipelines. MLflow gives your team visibility into what happened across every experiment and a path to deploy models and agents to production. Many production ML teams use both together through the kedro-mlflow integration plugin.
| Feature | Kedro | MLflow |
|---|---|---|
| Primary Focus | Reproducible data pipeline framework enforcing software engineering best practices | End-to-end ML lifecycle platform covering tracking, registry, deployment, and LLM observability |
| Pipeline Approach | Dataset-driven workflow with automatic dependency resolution between pure Python functions | Run-centric workflow tracking experiments, metrics, parameters, and artifacts per execution |
| Experiment Tracking | No built-in tracking; integrates with MLflow and other tracking tools via plugins | Full-featured tracking UI with metric comparison, artifact storage, and run history |
| Deployment Model | Framework-agnostic deployment to Airflow, Kubeflow, Argo, Prefect, Databricks, and more | Built-in model serving, Agent Server with FastAPI, and integrations with cloud platforms |
| Community Size | 10,800+ GitHub stars, backed by QuantumBlack and Linux Foundation LF AI & Data | 25,400+ GitHub stars, 900+ contributors, 30M+ monthly downloads, backed by Databricks |
| Best For | Teams needing standardized, modular pipeline code with enforced project structure | Teams needing experiment tracking, model management, and LLM/agent observability at scale |
| Metric | Kedro | MLflow |
|---|---|---|
| GitHub stars | 10.9k | 25.7k |
| TrustRadius rating | — | 8.0/10 (3 reviews) |
| PyPI weekly downloads | 191.2k | 8.0M |
| Docker Hub pulls | — | 0 |
| Search interest | 0 | 3 |
| Product Hunt votes | 14 | — |
As of 2026-05-04 — updated weekly.
| Feature | Kedro | MLflow |
|---|---|---|
| Pipeline & Workflow Management | ||
| Pipeline Orchestration | Dataset-driven DAG with automatic dependency resolution between pure Python functions and modular pipeline composition | No built-in pipeline orchestration; focuses on tracking individual runs and experiments |
| Pipeline Visualization | Kedro-Viz provides interactive data lineage, execution time, node status, and dataset statistics in a dedicated UI | Experiment comparison UI with metric charts, parameter tables, and artifact browsers across runs |
| Project Scaffolding | Standardized project template with cookie-cutter Starters, enforced directory structure, and coding standards | No project scaffolding; designed as a library that integrates into existing project structures |
| Experiment Tracking & Model Management | ||
| Experiment Tracking | No native experiment tracking; relies on integrations with MLflow or other tracking platforms | Comprehensive tracking of metrics, parameters, artifacts, and code versions with comparison UI and search |
| Model Registry | No model registry; models are managed as Data Catalog entries within the pipeline | Production-grade model registry with versioning, stage transitions, and approval workflows |
| Artifact Management | Data Catalog abstraction layer with connectors for S3, GCP, Azure, sFTP, DBFS, and local filesystems | Artifact store supporting local files, S3, Azure Blob, GCS, and HDFS with run-level organization |
| LLM & AI Agent Support | ||
| LLM Observability | Not a core capability; Kedro focuses on data pipeline structure rather than LLM monitoring | Full trace capture for LLM applications and agents built on OpenTelemetry with production monitoring |
| Prompt Management | No prompt management features; not designed for LLM workflow management | Version, test, and deploy prompts with lineage tracking and automatic optimization algorithms |
| Agent Deployment | Not applicable; Kedro orchestrates data pipelines, not AI agent serving | Agent Server with FastAPI-based hosting, request validation, streaming support, and built-in tracing |
| Code Quality & Standards | ||
| Testing Framework | Built-in test-driven development with pytest integration and standardized test directory structure | No built-in testing framework; provides evaluation APIs with 50+ built-in metrics and LLM judges |
| Code Documentation | Sphinx-based documentation generation and ruff-enforced linting baked into project template | No code documentation tooling; provides extensive platform documentation and tutorials |
| Reproducibility | Enforced through pipeline DAGs, Data Catalog versioning, configuration management, and deterministic execution | Achieved through experiment logging, artifact snapshots, environment tracking, and run replay |
| Integration & Deployment | ||
| Orchestrator Integration | Deployable to Apache Airflow, Kubeflow, Argo, Prefect, AWS Batch, Databricks, and Dask | Integrates with orchestrators as a tracking backend; not an orchestration tool itself |
| Framework Ecosystem | Integrates with Spark, Pandas, Dask, Matplotlib, Plotly, and MLflow for tracking | 100+ integrations including LangChain, OpenAI, PyTorch, and supports Python, TypeScript, Java, and R |
| IDE Support | Dedicated VS Code extension with enhanced code navigation and autocompletion for Kedro projects | No dedicated IDE extension; provides CLI tools, Python SDK, and web-based UI |
Pipeline Orchestration
Pipeline Visualization
Project Scaffolding
Experiment Tracking
Model Registry
Artifact Management
LLM Observability
Prompt Management
Agent Deployment
Testing Framework
Code Documentation
Reproducibility
Orchestrator Integration
Framework Ecosystem
IDE Support
Kedro and MLflow are complementary tools, not competitors. Kedro is a pipeline framework that enforces how your data science code is structured, organized, and executed. MLflow is a lifecycle platform that tracks experiments, manages models, and monitors LLM applications in production. Kedro gives your team consistent, reproducible, and modular pipelines. MLflow gives your team visibility into what happened across every experiment and a path to deploy models and agents to production. Many production ML teams use both together through the kedro-mlflow integration plugin.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Kedro and MLflow solve different problems in the ML lifecycle. Kedro is a pipeline framework that structures how you write and organize data science code, enforcing software engineering best practices like modular functions, standardized project templates, and automatic dependency resolution. MLflow is a lifecycle platform that tracks what happens when you run experiments, stores model versions, and manages deployment. Kedro tells you how to build your pipeline; MLflow tells you what happened when you ran it. The two tools complement each other, and Kedro lists MLflow as an official integration partner.
Yes, and this is a common production pattern. Kedro provides the pipeline structure, code organization, and reproducible execution framework, while MLflow handles experiment tracking, metric logging, and model registry. The kedro-mlflow plugin connects them seamlessly, allowing Kedro pipeline runs to automatically log parameters, metrics, and artifacts to an MLflow tracking server. Teams using both get the best of both worlds: Kedro's enforced code quality and pipeline visualization alongside MLflow's experiment comparison and model versioning capabilities.
MLflow is the clear winner for LLM and AI agent workflows. MLflow has expanded significantly into the LLM space with observability built on OpenTelemetry, prompt versioning and optimization, an AI Gateway for managing multiple LLM providers, and an Agent Server for deploying agents to production. Kedro remains focused on data pipeline structure and does not offer LLM-specific features. If your primary work involves building and deploying LLM applications or AI agents, MLflow provides the tooling you need out of the box.
MLflow has a substantially larger community with 25,400+ GitHub stars, 900+ contributors, and over 30 million monthly package downloads. It integrates with 100+ AI frameworks and supports Python, TypeScript, Java, and R. Kedro has a focused but smaller community with 10,800+ GitHub stars and strong backing from QuantumBlack (McKinsey) and the Linux Foundation. Both projects are actively maintained under Apache-2.0 licenses with recent releases in April 2026. MLflow's broader adoption reflects its wider scope as a lifecycle platform, while Kedro's community is deep in the data engineering and pipeline development space.