Both orchestrate workflows. Both are widely adopted. But they're solving fundamentally different problems — and picking the wrong one for your ML team is a painful mistake to undo.
You need to orchestrate an ML pipeline. Someone on your team says "use Airflow — everyone uses Airflow." Someone else says "we should be on Kubeflow — it's built for ML." Now you're in a meeting that's going nowhere.
This article ends that debate. Airflow and Kubeflow both orchestrate workflows, but they come from entirely different worlds — one from data engineering, one from machine learning infrastructure. Picking the right one depends on what your team already runs, how deep your Kubernetes experience goes, and whether you need a general-purpose workflow tool or a dedicated ML platform.
Airflow was born inside Airbnb's data engineering team in 2014 to solve a specific problem: orchestrating complex, multi-step data pipelines that touched dozens of different systems. It was donated to the Apache Foundation in 2016 and is now one of the most widely deployed workflow tools in the world.
The core concept is the DAG (Directed Acyclic Graph) — a Python file that defines tasks and the dependencies between them. You write Python, Airflow handles scheduling, retries, logging, and the dependency graph. It runs on any infrastructure — a single VM, a Docker Compose setup, or a Kubernetes cluster.
Kubeflow was created by Google and released in 2018 as the answer to a specific question: "How do we run ML workflows natively on Kubernetes?" It's not just a pipeline tool — it's a complete ML platform that includes notebooks (JupyterHub), pipeline orchestration, distributed training (via TFJob, PyTorchJob), hyperparameter tuning (Katib), and model serving (KServe).
Everything in Kubeflow runs as Kubernetes resources. Pipelines are compiled to Argo Workflows under the hood. Each pipeline step runs in its own container. This gives you powerful isolation, GPU scheduling, and scalability — but it comes with a real prerequisite: your team needs to understand Kubernetes.
| Feature | Apache Airflow | Kubeflow |
|---|---|---|
| Primary audience | Data engineers | ML engineers |
| ML-native features | ✕ No | ✓ Yes |
| Kubernetes required | ✕ No | ✓ Required |
| Distributed training | ✕ No | ✓ Built-in |
| GPU scheduling | Limited | ✓ Native |
| Experiment tracking | ✕ No | ✓ Yes |
| Model serving | ✕ No | ✓ KServe |
| Setup complexity | Low | High |
If you're a solo ML engineer or a small team just getting started: use Airflow. The investment to get Kubeflow production-ready on Kubernetes is significant and rarely worth it until you're at a scale where Airflow's limitations are a real daily friction point.