apache-airflow-providers-cncf-kubernetes
Provider package apache-airflow-providers-cncf-kubernetes for Apache Airflow
Decision gist · record as of 2026-08-14
Yes. This is a production-stable, actively maintained official Airflow provider with no known vulnerabilities, low install friction, and permissive licensing. Install it if you run Airflow and need to orchestrate Kubernetes workloads. Verify your Airflow version meets the >=2.11.0 minimum and that you have Kubernetes cluster access before deploying.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an existing Airflow installation (>=2.11.0) and a Kubernetes cluster or kubeconfig access.
- Low install friction; pure Python wheel.
- Actively maintained with recent release (4 days old).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license; you may use, modify, and distribute freely with attribution and no warranty.
last release 2026-08-10 (4 days) · last repo commit 2026-08-14 · 46,489 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 13,484,376 downloads/mo, #1,280 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-cncf-kubernetes
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from airflow import DAG
with DAG('k8s_example') as dag:
task = KubernetesPodOperator(
task_id='run_pod',
image='my-image:latest',
namespace='default'
)- Whether async support (kubernetes_asyncio dependency) is automatically enabled or requires explicit configuration.
- Whether the provider handles kubeconfig discovery from standard locations or requires explicit credential setup.
What it is and what it does
This is an Apache Airflow provider package that bridges Airflow's task orchestration engine with Kubernetes clusters. It allows you to define and execute containerized workloads as Airflow tasks, leveraging Kubernetes' native scheduling, resource management, and pod lifecycle features. The package provides operators and hooks that translate Airflow DAG definitions into Kubernetes pod specifications, handling authentication, namespace management, and async execution through its kubernetes and kubernetes_asyncio dependencies.
The provider is production-stable and actively maintained as part of the official Apache Airflow ecosystem. It requires Airflow 2.11.0 or later and supports Python 3.10 through 3.14. Installation is straightforward via pip on top of an existing Airflow setup, with no compiled dependencies. Use it when you need to run containerized jobs within Kubernetes clusters as part of larger Airflow workflows, or when you want Kubernetes' resource isolation and scheduling alongside Airflow's DAG orchestration.
Use it for
- Run containerized data processing jobs (ML training, ETL pipelines) on Kubernetes from Airflow DAGs.
- Orchestrate multi-container applications where each task runs as a Kubernetes pod with defined resource limits.
- Integrate Kubernetes-native workloads into Airflow workflows without maintaining separate orchestration systems.
- Execute tasks with dynamic resource allocation by leveraging Kubernetes' autoscaling and node management.
- Deploy Airflow workflows across multiple Kubernetes clusters or namespaces for multi-tenant environments.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
This is a production-stable, actively maintained official Airflow provider with no known vulnerabilities, low install friction, and permissive licensing. Install it if you run Airflow and need to orchestrate Kubernetes workloads. Verify your Airflow version meets the >=2.11.0 minimum and that you have Kubernetes cluster access before deploying.
Install
apache-airflow-providers-cncf-kubernetes on PyPI
Before you install
Low install friction; pure Python wheel. Actively maintained with recent release (4 days old). Requires Apache Airflow >=2.11.0 and kubernetes library >=35.0.0,!=36.0.0,<37.0.0; verify your Airflow version meets the minimum.
Requires an existing Airflow installation (>=2.11.0) and a Kubernetes cluster or kubeconfig access.
License in practice
Apache-2.0 permissive license; you may use, modify, and distribute freely with attribution and no warranty.
Quickstart
pip install apache-airflow-providers-cncf-kubernetes
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from airflow import DAG
with DAG('k8s_example') as dag:
task = KubernetesPodOperator(
task_id='run_pod',
image='my-image:latest',
namespace='default'
)Verify before relying
- Whether async support (kubernetes_asyncio dependency) is automatically enabled or requires explicit configuration.
- Whether the provider handles kubeconfig discovery from standard locations or requires explicit credential setup.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesaiofilesapache-airflowapache-airflow-providers-common-compatasgirefcryptographykubernetesurllib3kubernetes_asyncio |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 13,484,376 / month, #1,280 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_cncf_kubernetes-10.21.0-py3-none-any.whl
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See also apache-airflow-providers-apache-flink · apache-airflow-providers-celery · apache-airflow-providers-apache-spark · apache-airflow-providers-apache-kafka · apache-airflow-providers-google · apache-airflow-providers-dbt-cloud · apache-airflow-providers-zendesk · apache-airflow-providers-neo4j · apache-airflow-providers-git · apache-libcloud