Kubernetes vs Docker: What Is the Difference and When to Use Each?

Kubernetes vs Docker: what is the difference?

Kubernetes vs Docker comes down to layers: Docker is a toolset that packages your application as a container image and runs it on a machine. Kubernetes, on the other hand, is an orchestration platform that spreads those containers across many servers, scales them, replaces failed ones and manages updates. In short, they complement each other.

In practice, two groups ask us this question most often. The first group is site and store owners whose developers suggest "moving to Kubernetes". The second group is developers who learned Docker and wonder what comes next. Both groups share the same real concern: what does this extra complexity give us, and what does it cost?

We are a digital marketing and web team, not a hosting company. For that reason we base this guide on the official Kubernetes and Docker documentation and link a source for every technical claim. We covered the basics in our guide to Docker and containers, and the setup side in our Minikube and k3s installation guide. Here we skip those topics and focus on the differences that help you decide.

What does Docker actually do?

Docker lets you package an application and its dependencies into a single image, then run that image the same way in every environment. For example, code that works on a developer laptop behaves the same on the test server and in production. Put simply, it removes most of the "it works on my machine" problem.

When people say Docker, they usually mean several parts:

• Image building: it reads the steps in a Dockerfile and produces a layered image.
• Running: Docker Engine starts, stops and removes containers from images.
• Distribution: it pushes images to a registry and pulls them back.
• Local multi-service setups: Docker Compose starts several containers from one file.

Docker focuses on a single machine. You can also run dozens of containers on that machine. However, if the server goes down, Docker does not move the workload to another server on its own. That said, this limit rarely hurts small and mid-sized projects, because one well-monitored server with good backups stays sufficient for a long time.

Docker also shrinks the gap between development and production. A new developer can run the whole project locally with a few commands. As a result, hours spent on setup documents drop. Moreover, you ship the same image to test and production, so surprise bugs become less likely.

What does Kubernetes actually do?

The official documentation describes Kubernetes as a portable, extensible, open source platform for managing containerized workloads and services. It also supports declarative configuration and automation. Source: Kubernetes overview page.

The word "declarative" matters here. You tell Kubernetes "run three copies of this app", and Kubernetes keeps watching the current state and moves it toward that goal. So if one copy dies, it starts a new one. If a server fails, it moves that work to healthy servers.

The same page lists the main capabilities:

• Service discovery and load balancing.
• Storage orchestration.
• Automated rollouts and rollbacks.
• Automatic bin packing based on resource needs.
• Self-healing.
• Secret and configuration management.
• Horizontal scaling.

Note, however, that Kubernetes does not build images or compile your source code. The documentation states it plainly: Kubernetes is not a CI/CD tool. Therefore you still build the image with Docker or a similar tool.

The same page also stresses that Kubernetes is not a traditional PaaS. In other words, it does not ship a database, a cache or a message queue; you pick those parts and add them to the cluster. Kubernetes gives you a strong foundation, but your team still builds the rest of the house.

Where does each tool sit in the stack?

The easiest way to remember the difference is to split the work into layers. The Kubernetes blog post "Don't Panic: Kubernetes and Docker" describes Docker as a whole tech stack rather than a single piece. According to the same post, Docker itself uses containerd under the hood to run containers. Source: official Kubernetes blog.

With that in mind, you can picture the stack like this:

1. Image layer: you package your application. Docker and the Dockerfile live here.
2. Runtime layer: you run the image as a real container. A container runtime such as containerd or CRI-O works here.
3. Orchestration layer: you manage many containers on many servers. Kubernetes takes this job.

Think of a port. Docker is the crane that loads goods into a standard shipping container. Kubernetes is port management, which decides which container goes on which ship and where the cargo goes if a ship sinks. So the question "Docker or Kubernetes?" is usually the wrong question. Instead, the real question is whether you need the orchestration layer today.

How does Kubernetes vs Docker look in a table?

The table below sums up Kubernetes vs Docker on the points that matter most when you decide. Read the Docker column as Docker Engine plus Compose on a single server.

The Docker rows do not cover Swarm mode; we look at Swarm separately below. Also, this table is not a quality ranking. For example, the maintenance row explains why Kubernetes often feels like too much for a small team.

Does Kubernetes run containers with Docker?

In current Kubernetes releases, no. Kubernetes talks to the container runtime through an interface called the Container Runtime Interface (CRI). Docker Engine does not support this interface directly, so Kubernetes used to place a bridge called "dockershim" in between.

The Kubernetes project removed that bridge. According to the official dockershim FAQ, the deprecation came with the v1.20 release and the actual removal happened in v1.24. Source: Kubernetes dockershim FAQ.

Today you will usually find one of these runtimes on Kubernetes nodes:

• containerd: the same runtime Docker uses internally.
• CRI-O: a CRI runtime built specifically for Kubernetes.
• Docker Engine with cri-dockerd: a separate adapter for teams that want to keep Docker Engine.

This tells you one thing: Kubernetes does not depend on Docker. At the same time, it runs images that Docker builds without trouble. That distinction is the source of the misunderstanding in the next section.

Why should you care? Because when a hosting or cloud provider says "our Kubernetes nodes use containerd", it does not mean your Docker images will fail. The question to ask is whether your images follow the OCI standard; images you build with Docker already do.

Did removing dockershim mean Docker stopped working?

No, Docker still works. This is one of the most repeated myths online. Removing dockershim only changed which runtime starts containers on a Kubernetes node. Docker itself, its image building and its use on a single server all continue as before.

Official Kubernetes sources make three points clear:

• Images you build with Docker keep working, because they follow the OCI standard and every CRI runtime can run them.
• You can also keep using the docker build command to create images.
• The change concerns cluster operators, not developers; swapping the runtime on nodes is an infrastructure task.

In practice this means the following. As a developer, you keep writing Dockerfiles, building images and pushing them to a registry. If you run your own cluster, however, you check the runtime on your nodes. If you use a managed Kubernetes service, the provider usually handles that switch. Still, we recommend you read your provider's release notes.

Where does Docker Compose fit in?

Docker Compose lets you define an application made of several containers in one YAML file. Per the official Docker documentation, the Compose file defines the computing parts of the app as services, the communication between them as networks and persistent data as volumes. Source: Docker Compose application model.

A simple example defines a web app and its database in the same file:

services:
  web:
    image: example/web:1.0
    ports:
      - "8080:80"
    depends_on:
      - db
  db:
    image: postgres
    volumes:
      - dbdata:/var/lib/postgresql/data
volumes:
  dbdata:

Then you start both services together with docker compose up -d. You can find the details of running databases in containers in our PostgreSQL and MySQL in Docker guide.

In practice, Compose covers most "orchestration" needs of small and mid-sized projects on one server. For that reason, before you consider Kubernetes, we suggest you write down concretely why Compose no longer fits.

Is Docker Swarm an alternative to Kubernetes?

To a degree, yes. The Docker documentation describes Swarm mode as an advanced feature for natively managing a cluster of Docker Engines. With Swarm you can also use several servers as one cluster without extra orchestration software. Source: Docker Swarm mode documentation.

According to the official docs, Swarm offers these features:

• A declarative desired state that the manager keeps reconciling.
• A replica count per service, with scaling up and down.
• Overlay networking across several hosts.
• Service discovery, load balancing and rolling updates.

So Swarm is a simpler take on the core ideas behind Kubernetes. Because its definitions look a lot like Compose files, a team that knows Docker picks it up quickly. In contrast, Kubernetes has a much broader ecosystem, more room for extension and far more managed service options. So when you decide, look beyond today's needs and ask which tool your team can sustain in the long run.

How do Kubernetes vs Docker differ on scaling?

On scaling, the Kubernetes vs Docker difference comes down to who makes the call. With Docker on one server, you change the replica count yourself and all copies share that machine's resources. When the machine fills up, you move to a bigger server; we call that vertical scaling.

Kubernetes, by contrast, puts horizontal scaling at the center. Per the official docs, you can scale your app with a command, through a UI or automatically based on CPU usage. For example, you change the replica count of a Deployment with this command:

kubectl scale deployment/web --replicas=3

Kubernetes then places the new copies on suitable nodes in the cluster. It also weighs the CPU and memory each container requests while doing so. As a result, a traffic spike does not hit the ceiling of one server; you grow capacity by adding nodes to the cluster.

We should be honest here, though. Specifically, autoscaling only works if your app can run as several copies. An app that keeps sessions in server memory and writes files to local disk will not scale on Kubernetes; you need to fix the architecture first. We cover the caching side in our Redis vs Memcached guide.

How does self-healing work in each tool?

Docker offers basic recovery on one server: the restart policy. If a container exits unexpectedly, Docker restarts it on the same machine. That often suffices for simple crashes. If the server itself goes down, however, every container on it goes down too.

Kubernetes self-healing goes further. According to the official docs, Kubernetes does the following:

• It restarts containers that fail.
• It replaces containers when needed.
• It kills containers that do not respond to your health check.
• Until containers report ready, it keeps them away from traffic.

If a node becomes unreachable, Kubernetes recreates its workload on healthy nodes in the cluster. As a result, a single server failure does not take your whole site offline.

Still, this protection is not free. You need at least two nodes, ideally more, plus solid health checks and careful handling of persistent data. To quickly check from the outside whether your site is reachable, you can use our is it down checker.

How do rolling updates and rollbacks compare?

On a single server, a Docker update usually goes like this: you pull the new image, stop the old container and start the new one. Compose turns that into one command. However, you may see a short outage between the moment the old container stops and the new one becomes ready. Getting that to zero takes extra setup, for example a reverse proxy in front and two copies.

In Kubernetes, the rolling update is the default rollout method. The official docs describe it as moving the actual state to the desired state at a controlled rate. Kubernetes first starts new copies and then shuts down old ones once the new ones are ready. If the new release misbehaves, you roll back to the previous one:

kubectl rollout undo deployment/web

This feature pays off for teams that ship several releases a day. On the other hand, a company site that updates weekly and has low night traffic can usually live with a short maintenance window. In short, how much zero-downtime deploys matter depends on your traffic pattern.

Solid release management also needs order on the code side. For that, see our Git and GitHub guide.

How do service discovery and load balancing differ?

When an app consists of several services, those services need to find each other. Docker Compose solves this simply on one server: services on the same Compose network reach each other by service name. For instance, the web service connects to the database using the name "db".

In Kubernetes, containers can move around all the time. Say one copy stops on one node and then starts on another with a new IP address. That is why Kubernetes uses the Service object as a stable entry point. Per the official docs, Kubernetes can expose a container using a DNS name or its own IP address, and it spreads traffic across copies when load is high.

The practical result looks like this:

• One server and a few services: the Compose network is enough, and you need no extra parts.
• Many servers and frequently changing copies: the Kubernetes Service model makes life much easier.
• External traffic: in Kubernetes you also set up a load balancer or an ingress layer to reach the outside world.

If you are considering microservices, your language choice also plays a role. We cover that in our Python vs Go microservices comparison.

How different are the learning curve and maintenance load?

A handful of concepts gets you going with Docker: image, container, volume, network and the Compose file. So a developer can build a working setup with those in a short time. If you want a visual interface for management, our Portainer setup guide will help.

With Kubernetes the list grows. Pod, Deployment, ReplicaSet, Service, Ingress, ConfigMap, Secret, PersistentVolume, namespaces and RBAC are only some of them. On top of that, the cluster itself needs care:

• Kubernetes version upgrades and compatibility checks.
• Backups and availability of the control plane.
• Security updates for the nodes.
• Monitoring, log collection and alerting.
• Network plugin and storage driver management.

The official docs also note that Kubernetes does not dictate a logging, monitoring or alerting solution. In other words, you choose, install and maintain those parts yourself. So do not think of Kubernetes as "set it up once and it runs itself". If nobody on your team has time for this work, a managed service or a simpler setup is the smarter path.

Is Kubernetes more expensive to run?

In most scenarios, yes, at least at the start. We suggest you think about cost in two parts: infrastructure and people. The second one usually weighs more.

On the infrastructure side, Kubernetes only makes sense with several nodes. You can run a single node cluster, but then you lose the most valuable part of self-healing, which is surviving a server failure. In addition, monitoring, log collection and load balancers consume resources too.

On the people side, cluster upkeep, version upgrades and troubleshooting take regular time. For example, a developer who runs one server with Docker and Compose often handles these tasks alongside daily work. With Kubernetes, that load grows noticeably.

That said, the balance can shift as you grow. At some point, the cost of managing many services by hand makes Kubernetes automation the cheaper option. Therefore the right question is not "which one is cheaper?" but "which one needs less total effort at our scale?". Also, estimate your own team hours honestly when you run that math.

Which project should use which tool?

We recommend you choose based on your real business needs, not on how popular a technology is. The list below is a starting framework from field experience, not a hard rule:

• Company site, blog, small online store: shared hosting or a single VPS is often enough; you may not even need containers.
• A web app with a few services on one server: Docker and Compose are the most balanced choice.
• A project that must span a few servers but has a small team: look at Swarm or a managed platform first.
• Many services, many servers, high availability and frequent releases: this is where Kubernetes creates real value.

If you are unsure about the server type, our VPS vs cloud server vs VDS guide is a good starting point. We also gathered general hosting criteria in our guide on how to choose web hosting.

Also keep in mind that a project that starts with Docker can move to Kubernetes later. Your images run in both environments, so the move does not mean a rewrite.

When should you not set up Kubernetes?

We have covered the strengths of Kubernetes; now for the honest part. In the following cases we do not recommend that you run Kubernetes yourself:

• One server handles all your traffic comfortably.
• Nobody on your team can set aside regular time for cluster upkeep.
• Your app keeps sessions in memory and writes to local disk, so it cannot yet run as several copies.
• Your real problem is a slow database query or bloated pages.

The last point matters most. A slow site is often slow because of the app, not the infrastructure. In that case Kubernetes does not fix anything; instead, you face the same issue in a costlier, more complex setup.

Also, exposing a Kubernetes cluster to the internet is a serious security responsibility. Risk grows if you get API server access, secret handling or role based permissions wrong. If your team lacks that knowledge, leaving the job to your hosting provider or a managed Kubernetes service is the safer choice.

How do you handle persistent data in each setup?

Containers are temporary by nature; you can delete and recreate them at any time. For that reason, where your persistent data lives, such as the database and uploaded files, is the most critical decision with both tools.

In Docker you keep persistent data in volumes. A volume stays on the server disk even after you delete the container, so your data survives a redeploy. On a single server this approach is simple and clear. However, backups are entirely on you; if the server disk fails, the volume goes with it.

In Kubernetes, the capability the official docs call "storage orchestration" comes into play. For example, you can mount local disks, cloud provider disks or network storage to containers automatically. This flexibility is powerful. Still, when a pod moves to another node, its data must be reachable there too. So you should pick your storage approach before you build the cluster.

In practice, many teams keep the database outside Kubernetes in a managed database service and run only stateless app services in the cluster. That keeps cluster upkeep simpler. Whichever path you choose, test regularly that you can actually restore your backups.

When does managed Kubernetes make sense?

Major cloud providers and some hosting companies offer managed Kubernetes. In this model the provider runs the control plane, which is the brain of the cluster. You then manage only your apps and your node pool.

Here is what a managed service gives you:

• The provider handles control plane setup, backups and availability.
• Version upgrades usually take only a few steps.
• Parts like load balancers and persistent disks come integrated with the provider's infrastructure.

Some duties still stay with you, though. Your app architecture, resource definitions, security settings and cost control remain your job. Also, if you lean too hard on provider-specific features, moving to another provider later gets harder.

Our advice is simple. If you truly need Kubernetes and your team is small, start with a managed service instead of building your own cluster from scratch. For learning and experiments, the local setups in our Minikube and k3s guide are enough.

Where should you start when moving from Docker to Kubernetes?

Once you decide to move, taking the steps in order lowers the risk. We suggest this sequence:

1. Make the app stateless: move sessions and files outside the container.
2. Move configuration into environment variables; never bake passwords into the image.
3. Add a health check endpoint to every service.
4. Push your images to a registry with version tags.
5. Try Kubernetes on a local setup or a test cluster first.
6. Move a non-critical service first, then the rest.

The first three steps improve your app even if you never adopt Kubernetes. For example, a stateless app is easier to back up and move on a single server as well. So treat these steps as a general health exercise, not just "Kubernetes prep".

If you would like to review your app architecture together, our team can plan these steps with you as part of our custom software development service.

How can you sum up Kubernetes vs Docker?

If we had to put Kubernetes vs Docker in one sentence: Docker builds and runs containers, while Kubernetes manages many containers on many servers. They also sit at different layers of the same stack and often work together.

Here are the points worth remembering:

• Removing dockershim did not kill Docker; only the runtime on Kubernetes nodes changed.
• For one server, Docker and Compose are often enough and much simpler.
• The value of Kubernetes shows up when you need many servers and high availability.
• The maintenance load of Kubernetes is real; if your team lacks capacity, choose a managed service.

Finally, let business goals drive the infrastructure decision, not tech trends. First measure the real bottleneck of your site or app, then pick the simplest tool that removes it.