Comparison
MLflow vs Kubeflow: experiment tracking or a Kubernetes ML platform?
MLflow and Kubeflow compared: what each actually does, tracking and model registry versus Kubernetes pipelines, training and serving, GenAI tracing in MLflow 3, managed options, operating effort and how teams combine them.
Hemant Walchale
· 3 min read
MLflow and Kubeflow are often compared as if they were alternatives, but they solve different problems. MLflow keeps the record of machine learning work: experiments, metrics, models and their versions. Kubeflow runs machine learning work on Kubernetes: pipelines, training jobs and tuning. Many teams need one; some need both.
The short answer
- Choose MLflow if you need experiment tracking, a model registry and evaluation, especially for GenAI apps and agents, without running Kubernetes.
- Choose Kubeflow if you already run Kubernetes and want ML pipelines, distributed training and tuning to run on it as native resources.
- Use both when Kubernetes is your compute platform: Kubeflow Pipelines for orchestration, MLflow for tracking and the registry.
How they compare
| MLflow | Kubeflow | |
|---|---|---|
| Core job | Track and manage the ML lifecycle | Run ML workloads on Kubernetes |
| Main pieces | Tracking, model registry, evaluation, tracing, model packaging | Pipelines, training operators, Katib tuning, notebooks |
| Needs Kubernetes | No | Yes |
| GenAI | Tracing, evaluation and prompt management (MLflow 3) | Not a focus; runs whatever workloads you containerise |
| Model serving | Packages models for many serving targets | Usually through KServe, which began as Kubeflow Serving |
| Governance | Linux Foundation project | CNCF incubating project since 2023 |
| Managed options | Databricks, SageMaker AI, Azure Machine Learning | Kubeflow distributions; Vertex AI Pipelines runs Kubeflow Pipelines definitions |
| Licence | Apache 2.0 | Apache 2.0 |
| Operating effort | Low | High: a Kubernetes platform to run and upgrade |
What usually decides it
- Do you run Kubernetes already? If not, Kubeflow means adopting Kubernetes as well. That is rarely worth it just for ML.
- What is missing today. Teams that lose track of which data, code and parameters produced a model need MLflow first. Teams with training jobs too big for one machine need distributed compute, which Kubeflow (or Ray, or Spark) provides.
- GenAI work. MLflow 3 made models and GenAI traces first-class, so it now covers evaluating agents and LLM apps as well as classical ML.
- Platform you already use. On Databricks, MLflow is built in and governed by Unity Catalog. On Google Cloud, Vertex AI Pipelines accepts Kubeflow Pipelines definitions.
The short version
They are not really rivals. MLflow is the lifecycle record and registry and runs anywhere; Kubeflow is a Kubernetes-native platform for running ML pipelines and training. Start with MLflow; add Kubeflow when Kubernetes is already your compute platform.
Related: SageMaker vs Databricks, Dask vs Ray and what is Unity Catalog. Our AI-ready data service prepares data platforms for machine learning.
Sources
- MLflow 3 and MLflow 3 launch, MLflow
- Kubeflow documentation, Kubeflow
- Kubeflow becomes a CNCF incubating project, InfoQ
Further reading from other practitioners
- A comprehensive comparison between Kubeflow and MLflow, Valohai
- Kubeflow vs MLflow, Canonical
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