Kubernetes vs. Docker: Key Differences and How They Work Together

A working docker-compose.yml is one of the most satisfying files in a backend repository. One command brings up your web service, database, and queue on any machine with Docker installed. The follow-up request usually comes when those services need to survive a host failure, and with 82 percent of container users now running Kubernetes in production, it reaches most teams eventually. Framing the move as “Kubernetes vs. Docker” sets up a false choice, because the two tools solve different problems at different layers of the same stack.

This guide covers what each tool does, where they differ on scope, self-healing, scaling, networking, and configuration, and how a Docker-built image becomes a running Kubernetes Pod. It closes with a practical path from Compose to a cluster, including what Kompose converts for you, the operational work it leaves behind, and why your telemetry needs a new home once Pods start moving.

What Is Docker?

Docker is a container runtime plus the developer toolchain that builds and ships container images. Each instruction in a Dockerfile produces an immutable, cached layer, and the finished image is a read-only template that Docker Engine runs as an isolated container sharing the host kernel. Engine pairs the long-running dockerd daemon with the docker command-line interface (CLI). On docker run, the daemon pulls the image if it’s missing and hands container creation to containerd, which starts the process through runc.

The default public registry is Docker Hub, which stores container images and makes them available for pulls. Compose defines multi-container applications in one compose.yaml file (or the older docker-compose.yml) that declares services, networks, and volumes. Docker Engine is open source, while Docker Desktop is a separate product for Windows, macOS, and Linux that bundles Engine, the CLI, Compose, and a local Kubernetes cluster. Swarm mode ships inside Engine and can cluster several Docker hosts.

What Is Kubernetes?

Kubernetes (K8s) is a container orchestration system that decides where containers run across a cluster of machines and keeps them in the state you declared. Its control plane components are the application programming interface (API) server, etcd as the store for all cluster data, the scheduler that assigns unbound Pods to nodes, and the controller manager that responds when nodes go down. Every worker node runs a kubelet that keeps Pods running, plus an optional kube-proxy that maintains the network rules behind Services. Joe Beda made the first commit in June 2014, and the project became the Cloud Native Computing Foundation (CNCF)’s first graduated project in March 2018.

Kubernetes no longer talks to Docker Engine directly. The project deprecated dockershim, the shim between the kubelet and Docker, in version 1.20 and removed it in version 1.24, which means nodes now run a container runtime interface (CRI) implementation such as containerd or CRI-O, or cri-dockerd for teams that still want Docker Engine underneath. On top of that runtime, a Pod is the smallest deployable unit, a Deployment manages a set of Pods for a usually stateless workload, a Service exposes those Pods on the network, and a Namespace isolates groups of resources inside one cluster.

Key Differences: Kubernetes vs. Docker

The difference between Docker and Kubernetes shows most clearly when a host dies or traffic doubles. Docker’s tooling assumes one daemon on one host, while Kubernetes assumes many nodes and a control plane that keeps pulling actual state toward the state you declared. The table below compares Docker Engine and Compose with Kubernetes across the five dimensions where those assumptions diverge.

Dimension	Docker Engine and Compose	Kubernetes
Scope	One daemon host, with bridge networks confined to that host	Control plane and worker nodes spread across multiple machines
Self-healing	Restart policies (no, always, on-failure, unless-stopped) restart containers in place	Controllers replace failed Pods to hold the replica count, and liveness probes restart stuck containers
Scaling	Manual, via docker compose up --scale	Declarative, via the Horizontal Pod Autoscaler on resource or custom metrics
Networking	Default bridge driver with embedded name resolution on user-defined networks	Network plugins assign each Pod an address, and CoreDNS creates records for Services
Configuration	compose.yaml per the Compose Specification	Declarative manifests, often packaged as Helm charts

Self-healing and scaling separate the two tools most sharply. A Compose restart policy restarts a container on the same host, which means one failed host takes every service down with it, while a Kubernetes ReplicaSet replaces failed Pods and the scheduler assigns each replacement to a node that meets its requirements. The Horizontal Pod Autoscaler (HPA) computes desired replicas as ceil(currentReplicas × currentMetricValue / desiredMetricValue), so 50 replicas averaging 90 percent central processing unit (CPU) utilization against a 75 percent target scale out to 60. When the HPA adds replicas, Coralogix Kubernetes monitoring shows how each Pod responds during the scale-out alongside node health, which tells you whether the new replicas absorbed the load. On the networking side, Container Network Interface (CNI) plugins such as Calico or Flannel give every Pod its own address, and CoreDNS resolves a Service named my-service in namespace my-ns at my-service.my-ns.

How Docker and Kubernetes Work Together

Docker builds and packages the image, and Kubernetes runs and manages it. Kubernetes never touches the Dockerfile or the build step, which stays in your continuous integration (CI) pipeline or on a developer laptop. A typical pipeline splits into five handoffs between your build tooling and the cluster:

• Image build: You write a Dockerfile and run docker build locally or on a CI runner.
• Registry push: You tag the image and push it to a registry such as Docker Hub, Amazon Elastic Container Registry (ECR), or Google Artifact Registry.
• Deployment reference: A Deployment manifest references that image by name and tag.
• Pod scheduling: The scheduler binds each Pod to a node with enough unreserved CPU and memory to cover the Pod’s requests.
• Image pull: The node’s runtime pulls the image and uses imagePullSecrets when the registry is private.

Each of those handoffs works because Docker and Kubernetes agree on one image format. The Open Container Initiative (OCI) is the Linux Foundation project that Docker seeded by donating its container format and runc, and OCI images run the same way under Docker Engine and under any Kubernetes runtime. Kubernetes maintainers confirmed during the dockershim removal that Docker-built images still work with every CRI implementation. Older images are the exception, since containerd 2.0 disables Schema 1 pulls by default, and anything that old needs a rebuild before it reaches a containerd node.

When to Use Docker vs. Kubernetes

A single server is a simple deployment target for a Compose app. Compose keeps every container on that node, which means one host’s uptime sets the ceiling for your service’s uptime. Your failure domains and load patterns are a better guide than raw service count when choosing between Compose and Kubernetes, and four situations show where each tool fits:

• Local development and CI: Compose brings up a full stack on a laptop or CI runner. Docker Desktop’s bundled Kubernetes cluster lets you test manifests locally too.
• Single-host production: Internal tools and small services that can tolerate brief downtime during a host reboot can run on one server with restart policies.
• High availability requirements: When losing one machine can’t take the service down, you need a multi-node control plane and Pod replacement across nodes.
• Many teams and services: Services spread across microservices architectures and owned by several teams benefit from Namespaces, resource quotas, and per-team access control.

Published service-count thresholds for switching vary widely and rarely come with the data behind them. A more reliable trigger is the moment your Compose stack outgrows one host or one failure domain. At that point, Kubernetes adds a cluster management layer on top of the images Docker already builds for you.

The Transition from Docker Compose to Kubernetes

Kompose is the conversion tool the Kubernetes documentation points to for this move, and it turns a Compose file into Kubernetes manifests. Running kompose convert beside your compose.yaml writes one manifest per object, and kubectl apply loads them into the cluster. For a Compose service named frontend, the sequence looks like this:

# Convert the Compose file, then apply the frontend manifests.
kompose convert
kubectl apply -f frontend-deployment.yaml,frontend-service.yaml

What Kompose Maps and What It Drops

The first command produces frontend-deployment.yaml and frontend-service.yaml because Kompose maps each Compose service to one Deployment and one Service by default. Inside those files, deploy: replicas becomes Deployment.Spec.Replicas, ports becomes Service.Spec.Ports, and volumes become PersistentVolumeClaims that Kompose can only create when a PersistentVolume already exists in the cluster. Several keys don’t carry over cleanly, since network_mode has no equivalent under Kubernetes cluster networking, network aliases and static addresses are unsupported, and deploy: resources maps only to limits. The output works as a first draft of your manifests, and it will change over several rollouts. Coralogix’s Kubernetes monitoring correlates YAML changes and rollout history with spikes or failures, which puts a bad edit to a probe or a limit next to the errors it caused.

The Operational Work Kompose Leaves to You

Kubernetes adds operational surface that Compose never asked you to manage, starting with resource sizing. The scheduler places Pods by their requests while the kubelet enforces their resource limits, and when you set only a limit, Kubernetes copies it as the request, which means Kompose’s limits-only output reserves the full limit on every node. A container that exceeds its memory request becomes an eviction candidate when its node runs short of memory, which makes request sizing a reliability decision and a Kubernetes cost decision at once. Coralogix prices by data ingested with unlimited hosts, so the nodes your autoscaler adds never create per-host observability charges. You’ll also own Namespaces, role-based access control (RBAC) bindings, and ResourceQuota policies, none of which Kompose generates.

Why Observability Gets Harder in Kubernetes Environments

Monitoring ranks as a top container challenge for 35 percent of organizations, and the reason shows up the first time a scheduler moves your containers between nodes. The kubelet keeps only one terminated container’s logs after a restart, and when Kubernetes evicts a Pod, its containers leave along with their container logs, so anything you need for root cause analysis has to reach durable storage first. On the collection side, the OpenTelemetry (OTel) Kubernetes Attributes Processor adds Pod metadata to spans, metrics, and logs, which gives every signal the same Pod identity for cross-signal correlation. A common layout pairs an OTel Collector DaemonSet on each node with a gateway Deployment, while distributed tracing follows each request across Service boundaries. Choosing which signals to track at each cluster layer is Kubernetes monitoring work that sits on top of the runtime choice covered here.

Coralogix, the cross-stack observability platform, ships an OpenTelemetry Integration Chart that installs a DaemonSet agent on every node and a Cluster Collector for Kubernetes events and cluster metrics in one Helm release. Telemetry lands in your own cloud storage bucket in open Parquet format with infinite retention, and you can query an evicted Pod’s logs weeks later without a rehydration step. Coralogix offers a free 14-day trial with an eight-unit quota and nhttps://coralogix.com/platform/o credit card required, which is enough to install the Integration Chart on your own staging cluster and query a rescheduled Pod’s logs from your bucket.

Frequently Asked Questions About Kubernetes vs. Docker

Docker and Kubernetes handle different layers of your container workflow. Image builds stay with Docker, while Kubernetes schedules the resulting containers across a cluster. Your deployment requirements determine whether Compose is enough or a cluster’s self-healing and scaling justify the operational work.

Can Kubernetes replace Docker?

No, because the two operate at different layers. Docker builds images and runs individual containers, while Kubernetes schedules and heals those containers across a cluster. Kubernetes still needs a node container runtime, usually containerd since dockershim’s removal, but it has no image-building toolchain of its own, which is why Docker typically stays in the pipeline as the build step.

Why are some companies moving away from Kubernetes?

Running Kubernetes requires orchestration-layer expertise somewhere on the team, and cultural change with development teams tops the list of container challenges at 47 percent. Gitpod (now Ona) left Kubernetes after six years of running cloud development environments on it, citing noisy neighbor effects and teams that underestimated the complexity of running it. Smaller teams often find Compose, Amazon Elastic Container Service (ECS), or Cloud Run sufficient until self-healing and autoscaling outweigh the overhead.

How does Docker Swarm compare with Kubernetes?

Swarm mode ships inside Docker Engine and clusters several Docker hosts, with a manager that reconciles each service toward the number of tasks you declare. Scaling a Swarm service means changing its replica count, while Kubernetes adds metric-driven autoscaling through the HPA, Namespaces with quotas, and Helm for packaging manifests. Swarm fits teams that want multi-host failover with Compose-style service definitions and little new tooling to learn.

Can you use Docker without Kubernetes?

Docker runs on its own for local development, CI pipelines, and single-server production through Compose. Many teams build and test with Docker, push images to a registry, and introduce Kubernetes only at the production stage. Compose files also stay useful after a migration, since they keep local development environments quick to start.