Docker and Kubernetes are two of the most popular container tools used for modern DevOps. Docker is primarily a platform for creating and running container images, while Kubernetes is an orchestrator that focuses on deploying, scaling, and managing containers in production environments.
Docker and Kubernetes overlap in several ways but also have unique differences that affect the situations where they’re used. You can use one tool without the other or allow them to complement each other by combining both in your workflows.
In this article, we’ll explore their similarities and differences, then discuss the advantages of using each tool.
We will cover:
Docker is a platform for building and running containers. You can use it to create new container images from Dockerfiles, then start containers from your images.
Docker was instrumental in establishing the container movement that’s transformed DevOps over the past decade. Most developers begin using containers with Docker. It’s available as a graphical desktop interface for developer use, and a CLI and daemon for server environments.
Kubernetes (K8s) is a system for running containers with flexible scaling and high availability. Developed at Google to simplify the use of containers in production, Kubernetes automates key administration tasks to help you reliably operate your containers.
Several Kubernetes distributions are available. Compact versions like Minikube are ideal for local use, while managed services such as Google GKE and Amazon EKS allow you to rapidly provision new cloud-based clusters.
Docker and Kubernetes each revolve around containers. You can use them to run containers, link them together, attach persistent storage, and provide network services.
Both Docker and Kubernetes are compatible with all OCI container images, runtimes, and container registries. Images created with Docker can be used in Kubernetes, for example.
The two tools include their own networking models and storage abstractions. You can also use them to replicate container instances across multiple hosts–this is fundamental in Kubernetes, whereas Docker offers it as part of its optional Swarm mode.
Beyond their high-level support for running containers, Docker and Kubernetes each possess several differences that lend them advantages and drawbacks in specific situations.
These differences can be summarized by assessing the use cases that the tools focus on: Docker is primarily a platform for building and running containers, whereas Kubernetes is an operations-oriented orchestrator for deploying containers at scale.
Here’s how Docker and Kubernetes compare across some key characteristics:
Docker and Kubernetes are both container tools, but they focus on different areas within the overall containerization space. Docker provides functionality for building and running containers, whereas Kubernetes is focused on their deployment and operation.
Docker includes components such as BuildKit for writing Dockerfiles and building images from them. You can also search Docker Hub to find images relevant to your workload, and access built-in tools for image security scans and SBOM generation.
Kubernetes only works with existing images and provides fewer features for managing them. You can run services inside your cluster that offer functions similar to those bundled with Docker, but they’re not part of the core Kubernetes experience. The power of Kubernetes is its ability to scale containers across multiple physical Nodes, then ensure they remain accessible after Node failures.
Docker generally works with individual containers. In practice, most applications are formed from multiple services that need to be managed as a collective stack. Docker ecosystem tools, including Compose and Swarm Mode, facilitate this, but they’re relatively narrow in scope.
Kubernetes, meanwhile, is a purpose-built orchestrator for scaling containers across multiple nodes. It includes a robust set of features for assigning containers to nodes, maintaining resource capacities, and adjusting scaling configuration in response to actual demand. Although the initial setup procedure is more demanding, Kubernetes is better suited to the demands of real applications in live environments.
Another benefit of Kubernetes is its declarative-first design. Deployments are usually configured as YAML manifests, which describe your desired state. When you apply a manifest, Kubernetes automatically reconciles the changes and applies any actions required to transition your cluster to the new state.
By contrast, most users interact with Docker using imperative CLI commands; Compose and Swarm mode are required for declarative configuration of replicated services, but the result is still more limited than Kubernetes.
Docker is a relatively low-level tool. It directly manages containers, and you need to use other tools, such as Compose, to link them together.
Kubernetes takes a higher-level view that includes more abstraction layers. Concepts such as Pods, ReplicaSets, and Deployments present a learning curve initially but allow more accurate and powerful modeling of your application.
Kubernetes can manage your supporting infrastructure too. It can provision networking components such as load balancers and storage volumes for you, using the resources available from your cloud provider. Abstracting away the differences between different clouds gives you more flexibility when deploying your workloads.
Docker comes with extremely minimal ops features for overseeing your deployments. You can get basic resource utilization information from docker stats, or use docker logs to access a container’s log stream. Beyond these features, there are no other observability capabilities, nor are there mechanisms for controlling access to Docker itself. This simplifies local use on your own machine but is restrictive in production.
Kubernetes comes with essential operations controls built-in. It has extensive support for Role-Based Access Control (RBAC), for example, which allows you to control the operations different users can perform. Kubernetes is also easily integrated with advanced observability solutions such as Prometheus, which can run in your cluster while simultaneously scraping metrics from it.
In summary, Kubernetes is designed around the requirements of operators, as it originated from this field. Docker is positioned as a developer-oriented tool, which means it lacks robust ops-specific features.
Images you build with Docker can be deployed to a Kubernetes cluster. The two tools operate independently, however: Kubernetes does not directly depend on Docker. You don’t need Docker installed to use Kubernetes, and vice versa.
Both Docker and Kubernetes are compliant with the Open Container Initiative (OCI) specifications. This project defines standards for interoperability between different container runtimes and image formats. Docker outputs OCI-compatible images, which can be run by any OCI runtime. The default runtime used by Docker and Kubernetes is containerd, but alternatives such as CRI-O are available.
It’s worth noting that Kubernetes did historically use Docker to run the containers in your cluster. A component called Dockershim provided an interface between Docker and Kubernetes. Dockershim was removed in Kubernetes v1.24; an OCI-compatible runtime must now be used instead.
There’s no definitive answer to this question. It depends on the container features you require and how they will be used. Nowadays, it’s common for development teams to deploy both technologies as part of their container workflows, allowing them to complement each other.
Using Docker and Kubernetes
Docker’s typically used to build images and run containers on developer workstations, while the enhanced operational abilities of Kubernetes are chosen for production deployments.
Docker’s simplicity means it’s often the most convenient option during development. It’s ideal for smaller applications with few components. Docker also has the advantage of familiarity and a low learning curve: most developers quickly become proficient at building images and starting containers.
Kubernetes is more complex, but it’s also much more flexible and better suited to the requirements of operators running containers in production. Its support for scalability, high availability, container lifecycle management, and observability can’t be replicated by Docker.
Additionally, the ability to quickly provision new Kubernetes clusters using managed cloud services can significantly accelerate your route to production.
Both Docker and Kubernetes are great tools for working with containers in DevOps situations. They’re both capable of running containers, but they’re typically used in different parts of the development workflow.
Docker is focused on application components. It’s useful for building your container images and running them locally as you work. However, Docker lacks the operations features essential to the use of containers in production.
Kubernetes accommodates this use case by providing a platform for automating container deployments and lifecycle management. It helps you operate containers by providing features for dynamic scaling and robust fault tolerance.
Kubernetes is also easy to integrate with IaC and CI/CD solutions. You can use Spacelift to provision Kubernetes clusters with Terraform, for example, allowing you to automate infrastructure configuration. You can use Kubernetes to run all your container workloads across production, development, and CI/CD jobs, but might still want to choose Docker for quick testing on your laptop.
The Most Flexible CI/CD Automation Tool
Spacelift is an alternative to using homegrown solutions on top of a generic CI. It helps overcome common state management issues and adds several must-have capabilities for infrastructure management.