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Docker builds, packages, and runs containers; Kubernetes coordinates containerized applications across a cluster. They work at different layers, so they are often used together rather than as direct alternatives: Docker tooling can create an application image, while Kubernetes schedules and manages workloads that use that image.
What is the difference between Docker and Kubernetes?
Docker is a platform and toolset for developing applications, packaging them with their dependencies as container images, sharing those images, and running containers. Its documented lifecycle spans development, testing, distribution, and deployment. Docker Docs describes Docker as a platform for developing, shipping, and running applications.
Kubernetes is a system for managing containerized workloads and services across a cluster of machines. It can place workloads on nodes, perform automated rollouts and rollbacks, support scaling and service discovery, and help recover from failures. In short, Docker focuses on the container and application lifecycle; Kubernetes focuses on coordinating workloads across a cluster.
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Docker: create and run containers
A Docker image packages application code and dependencies. Docker tooling helps build and distribute that image, then run it as a container in development or deployment environments. This is useful when you need an isolated, repeatable way to build or run an application without managing a cluster.
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Kubernetes: manage workloads across a cluster
A Kubernetes cluster has a control plane and worker nodes. The control plane manages the cluster and its Pods, while worker nodes run workloads. Kubernetes provides cluster-level capabilities such as resource-aware placement, service discovery, load balancing, storage orchestration, automated updates and rollbacks, self-healing, and horizontal scaling. See the project’s Kubernetes overview and cluster architecture documentation.
Can Docker and Kubernetes be used together?
Yes. A common workflow is to use Docker tooling to build an image, publish it to a registry, and then run it as a workload on Kubernetes. Kubernetes manages the workload; it does not need Docker Engine to execute it.
The distinction is between an image and a runtime. An image packages the application and its dependencies. A container runtime executes containers. Kubernetes communicates with runtimes through the Container Runtime Interface (CRI); compatible options include containerd and CRI-O. Kubernetes documentation covers containers, images, and the CRI.
Does Kubernetes still use Docker?
Kubernetes removed dockershim in version 1.24, so Docker Engine is no longer a built-in Kubernetes runtime through that integration. That did not make Docker-built images unusable: Kubernetes runtimes can run compatible images created with Docker tooling. The Kubernetes project explains the change in its 2020 dockershim transition article and its image documentation.
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When should you use Docker, Kubernetes, or both?
| Your need | Likely fit | Why |
|---|---|---|
| Build and run an application in an isolated, repeatable container | Docker | Docker supports application development, packaging, sharing, and execution. |
| Run a small service or local development environment without cluster-level requirements | Docker | Docker supports development, testing, and deployment workflows. |
| Coordinate workloads across machines and automate placement, updates, scaling, service discovery, or recovery | Kubernetes | These are cluster-management capabilities provided by Kubernetes. |
| Develop locally, then deploy a larger workload to a cluster | Both | Docker tooling can produce images; Kubernetes manages workloads using compatible runtimes. |
Kubernetes is not automatically the better choice just because an application uses containers. A cluster brings infrastructure and operational work. The Kubernetes getting started and setup guidance identifies maintenance, security, control, available resources, and operator expertise as factors in choosing how to run a cluster; a managed service is an option for teams that do not want to operate cluster infrastructure themselves.
How to choose between them
Start with the job you need done, not a product-versus-product scorecard:
- Container lifecycle: If the immediate need is to build, package, share, or run an application container, Docker addresses that work.
- Workload scale and distribution: If you need to coordinate workloads across multiple machines, Kubernetes provides cluster-level scheduling and management.
- Automation and recovery: Consider whether automated rollouts, scaling, service discovery, and self-healing are requirements or unnecessary complexity for the service.
- Environment: Docker Desktop includes Docker tooling and Kubernetes, allowing local learning and testing. A production cluster has a control plane and worker nodes, and requires separate decisions about how it is operated.
- Operational capacity: Account for the team’s ability to maintain and secure cluster infrastructure, or consider a managed Kubernetes service.
Bottom line
Docker and Kubernetes solve related but different problems: Docker helps create and run containers, while Kubernetes manages containerized workloads across a cluster. Use Docker when container packaging and execution are the goal; add Kubernetes when cluster-level coordination and automation justify its operational responsibilities.
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