MLflow and Kubeflow come up together as MLOps tooling, framed as a choice between them. That framing misleads, because they operate at very different scopes and weights. MLflow is a lightweight library for tracking and managing the ML lifecycle; Kubeflow is a heavy, Kubernetes-native platform for running ML workloads at scale. The right question is not which is better but which matches your scale and your appetite for infrastructure — and whether you want both.
MLflow: lightweight lifecycle tracking
MLflow is a library you add to your existing code to get experiment tracking, a model registry, and model packaging. It is infrastructure-agnostic — it runs anywhere, needs no Kubernetes, and integrates with a few lines. Its scope is managing the ML lifecycle: recording runs, versioning models, and promoting them. It is easy to adopt and light to run, which makes it the default starting point for most teams wanting experiment tracking and model management.
Kubeflow: a Kubernetes ML platform
Kubeflow is a full platform that runs on Kubernetes and aims to handle end-to-end ML workloads — pipelines, distributed training, hyperparameter tuning, and serving — at scale on your cluster. Its scope is the compute and orchestration of ML, not just lifecycle tracking. That power comes with weight: it requires Kubernetes and meaningful operational investment to run and maintain. It suits organisations running ML at scale who already live in Kubernetes.
| MLflow | Kubeflow | |
|---|---|---|
| Scope | Lifecycle tracking, registry, packaging | End-to-end ML platform on Kubernetes |
| Weight | Lightweight library | Heavy platform, needs Kubernetes |
| Adopt when | You want tracking and model management | You run ML at scale on K8s and need orchestration + compute |
Often complementary, not either/or
Crucially, these are not mutually exclusive — many setups use both. Kubeflow orchestrates the pipelines and compute on the cluster, while MLflow handles experiment tracking and the model registry within it. Because MLflow's scope (lifecycle management) and Kubeflow's scope (workload orchestration) barely overlap, they compose naturally. "MLflow vs Kubeflow" often resolves to "MLflow for tracking, Kubeflow for orchestration," used together.
Do not adopt a Kubernetes-native platform for a two-person project that needs experiment tracking — MLflow alone will serve you, at a fraction of the operational cost. Reserve Kubeflow's power and complexity for genuine scale, distributed training, and teams already invested in Kubernetes. Choosing the heavyweight platform before your scale demands it is buying operational burden you will pay for daily and use a fraction of.
MLflow and Kubeflow are not really competitors: MLflow is a lightweight library for ML lifecycle tracking and model management, while Kubeflow is a heavy Kubernetes-native platform for orchestrating ML workloads at scale. Match the tool to your scale — MLflow for tracking without infrastructure overhead, Kubeflow for large-scale orchestration on Kubernetes — and recognise that many teams run both, MLflow tracking the lifecycle inside Kubeflow-orchestrated pipelines.
Independent software engineer in Nairobi specialising in Acumatica customisations, Laravel backends, and tax fiscalisation integrations across East and Southern Africa.