Docker builds, packages, shares, and runs containers; Kubernetes manages containerized workloads across a cluster. They work at different layers, so the choice is often not Docker or Kubernetes: teams commonly use Docker tooling to create images and Kubernetes to schedule and operate those workloads.
What is the difference between Docker and Kubernetes?
Docker is a platform and toolset for the application container lifecycle: developing an app, packaging its code and dependencies into an image, sharing that image, and running it as a container. Docker’s overview describes this lifecycle.
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Kubernetes is a system for managing containerized applications across a cluster of machines. It can place workloads on nodes, coordinate updates and rollbacks, scale workloads, provide service discovery and load balancing, and recover workloads when problems occur. Its overview and cluster architecture documentation describe those responsibilities.
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What does Docker do?
A Docker image packages an application and the dependencies it needs. Docker’s tools let developers build, test, share, and run that image as a container, helping make an application’s environment more repeatable across development and deployment.
Docker can be enough when the goal is to run an isolated application or a local development environment and there is no need for cluster-level scheduling, scaling, or recovery. A cluster orchestrator is a separate decision, not a prerequisite for using containers.
What does Kubernetes do?
Kubernetes manages workloads across a cluster, which consists of a control plane and worker nodes. The control plane manages the cluster; worker nodes run workloads in Pods. Kubernetes makes sense when coordinating workloads across machines or automating operational tasks such as rollouts, scaling, service discovery, and recovery matters.
That capability comes with an operational decision: someone must choose how the cluster is set up and maintained. Kubernetes’ getting-started guidance identifies maintenance, security, control, resources, and operator expertise as factors in choosing an installation approach. A managed service is one option for teams that do not want to operate the cluster themselves.
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Do Docker and Kubernetes work together?
Yes. A common workflow is to use Docker tooling to build an image, put it in a registry, and then have Kubernetes run that image as a workload. Kubernetes does not need Docker Engine on its nodes to run it.
It helps to distinguish an image from a container runtime. An image packages application files and dependencies; a runtime executes containers. Kubernetes communicates with runtimes through the Container Runtime Interface (CRI). Supported CRI runtimes include containerd and CRI-O. See the Kubernetes documentation on containers, container runtimes, and images.
Does Kubernetes still use Docker?
Kubernetes removed dockershim in version 1.24, ending its built-in integration with Docker Engine as a runtime. That change does not make Docker-built images unusable: Kubernetes runtimes can run images built with Docker tooling. Nor does it prevent developers from using Docker to build and test applications.
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The distinction is between the tool used to build an image and the runtime Kubernetes uses on cluster nodes. The Kubernetes project explains the transition and image compatibility in its 2020 article on Kubernetes and Docker.
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Can you use Kubernetes locally?
Yes. Docker Desktop includes Docker tools and Kubernetes, so it can be used to learn and test locally. A local setup is useful for development, but a production cluster has a control plane and worker nodes, and its setup requires decisions about maintenance, security, resources, and who will operate it. Docker Desktop’s Kubernetes documentation describes the local option.
Which should you use?
| Need | Likely fit | Reason |
|---|---|---|
| Build and run an application in an isolated, repeatable container | Docker | It provides tools for developing, packaging, sharing, and running containers. |
| Run a small service or local development environment without cluster-level requirements | Docker | Docker supports application development, testing, and deployment workflows. |
| Coordinate workloads across machines, with automated rollout, scaling, service discovery, or recovery | Kubernetes | These are cluster-management capabilities. |
| Develop with Docker and deploy workloads to a cluster | Both | Docker tools can build images; Kubernetes manages workloads using compatible runtimes. |
Before choosing Kubernetes, consider whether its cluster-level automation is needed and whether your team can handle the setup and operating responsibilities—or prefers a managed cluster. For a straightforward local container workflow, Docker alone may be the simpler fit.
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How to compare them fairly
- Job to be done: Is the need to build and run containers, or to manage workloads across a cluster?
- Workload shape: Is one application running locally, or are workloads distributed across machines?
- Automation: Do you need automated placement, rollouts, scaling, service discovery, and recovery?
- Environment: Is this for local development and testing, or production cluster operations?
- Operational capacity: Can your team maintain cluster infrastructure, or would a managed service better fit?
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