dagster-k8s
A Dagster integration for k8s
Decision gist · record as of 2026-08-14
Yes, if you are already using Dagster and deploying on Kubernetes. The package is actively maintained with no known vulnerabilities and low install friction. It is the standard way to run Dagster jobs on k8s. Install only if you have a running k8s cluster and need Dagster's orchestration on that infrastructure; otherwise, use Dagster's local or other executor options.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Kubernetes cluster and kubeconfig configured; google-auth and kubernetes client libraries must be able to authenticate to the cluster.
- Active maintenance with a release 7 days old.
- Low install friction: pure Python wheel with three runtime dependencies (dagster, google-auth, kubernetes).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for infrastructure tools in this ecosystem.
last release 2026-08-07 (7 days) · last repo commit 2026-08-13 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,871,063 downloads/mo, #2,020 on PyPI
Alternatives
Verify before relying
pip install dagster-k8s
import dagster_k8s
from dagster import asset, define_asset_job
@asset
def my_asset():
return data
my_job = define_asset_job("k8s_job")- Specific Kubernetes versions or API compatibility requirements beyond Python 3.10–3.14.
- Whether dagster-k8s handles multi-tenant or namespace isolation out of the box.
- Performance characteristics and resource overhead for typical workloads on k8s.
- Exact executor configuration options and resource specification capabilities.
What it is and what it does
dagster-k8s is a Dagster integration that runs Dagster asset definitions and jobs on Kubernetes infrastructure. It bridges Dagster's declarative asset model—where you define data pipelines as Python functions—with Kubernetes' container orchestration, allowing you to scale asset execution across a k8s cluster. The package depends on dagster (the core orchestrator), google-auth (for credential handling), and kubernetes (the Python client library), and is designed for teams deploying Dagster in containerized environments.
The integration lets you configure how your assets run on k8s with resource specifications and executor settings. It is part of the broader Dagster ecosystem, which provides observability, lineage tracking, and a web UI for monitoring asset health and dependencies. This is most useful when your data infrastructure already runs on Kubernetes or when you need the scalability and isolation that k8s provides.
Use it for
- Run Dagster asset jobs on Kubernetes clusters instead of local or single-machine executors.
- Scale data pipeline execution across multiple k8s nodes for parallel asset computation.
- Deploy Dagster in containerized environments where k8s is the standard orchestration layer.
- Isolate asset execution in separate k8s pods for resource control and fault isolation.
- Integrate Dagster with existing k8s-based data infrastructure and CI/CD pipelines.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Dagster and deploying on Kubernetes.
The package is actively maintained with no known vulnerabilities and low install friction. It is the standard way to run Dagster jobs on k8s. Install only if you have a running k8s cluster and need Dagster's orchestration on that infrastructure; otherwise, use Dagster's local or other executor options.
Install
dagster-k8s on PyPI
Before you install
Active maintenance with a release 7 days old. Low install friction: pure Python wheel with three runtime dependencies (dagster, google-auth, kubernetes). Widely used in the top 5000 PyPI packages tier.
Requires a running Kubernetes cluster and kubeconfig configured; google-auth and kubernetes client libraries must be able to authenticate to the cluster.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for infrastructure tools in this ecosystem.
Quickstart
pip install dagster-k8s
import dagster_k8s
from dagster import asset, define_asset_job
@asset
def my_asset():
return data
my_job = define_asset_job("k8s_job")Verify before relying
- Specific Kubernetes versions or API compatibility requirements beyond Python 3.10–3.14.
- Whether dagster-k8s handles multi-tenant or namespace isolation out of the box.
- Performance characteristics and resource overhead for typical workloads on k8s.
- Exact executor configuration options and resource specification capabilities.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdagstergoogle-authkubernetes |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 5,871,063 / month, #2,020 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_k8s-0.29.17-py3-none-any.whl
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See also dagster-celery-k8s · dagster · dagster-cloud-cli · dagster-webserver · dagster-docker · dagster-aws · dagster-dg-core · dagster-rest-resources · dagster-spark · dagster-pyspark