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Kubernetes is more than a way to start Pods: it is a set of API objects and independent control processes that continually work to bring a cluster toward the state you declare. To understand it, match each resource to the job it is meant to manage—whether that is interchangeable application replicas, stable identities, node-local software, scheduled tasks, network access, configuration, or scaling.
How Kubernetes works beyond starting containers
Kubernetes describes itself as a “portable, extensible, open source platform for managing containerized workloads and services that facilitate both declarative configuration and automation.” In practice, you submit objects to the Kubernetes API that describe desired state—for example, how many application replicas should run. Controllers observe the cluster and act to move actual state toward that declared state.
This is not a fixed sequence of commands that starts a container and then stops. Different controllers handle different resources and responsibilities, and they can work independently. Pods are the units that run workloads, but other objects express how Pods should be created, replaced, reached, configured, and scaled.
Control plane and worker nodes
A cluster has a control plane, which makes cluster-level decisions and responds to events, and worker nodes, which host workload Pods. How components are placed varies by cluster design. A managed Kubernetes service may operate parts of the control plane on your behalf, but the cluster still relies on the same basic division between coordination and workload execution.
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What Kubernetes does—and does not—include
Kubernetes supplies building blocks for managing workloads; it is not an all-inclusive platform-as-a-service. Logging, monitoring, alerting, and machine configuration and maintenance are not provided as one comprehensive, built-in operational system. A distribution or hosted cluster may include additional components, while another may leave those choices to its operators.
Choose a workload controller by lifecycle
A controller is a better fit when its assumptions match what your application needs. The key questions are whether replicas are interchangeable, whether each Pod needs a stable identity or storage association, whether work is tied to a node, and whether the task should finish or remain running.
| Resource | Use it when | Lifecycle assumption |
|---|---|---|
| Deployment | You run a stateless service, such as interchangeable web application replicas. | Replicas can be replaced without preserving a particular Pod identity. |
| StatefulSet | Pods need distinct, stable identities or an association with persistent storage. | Identity and storage relationships matter across Pod replacement. |
| DaemonSet | You need node-local software, such as a driver, network component, or node management agent. | A Pod runs on every eligible node or on nodes matching selection rules. |
| Job | You need a task to run to completion, such as a one-off batch operation. | The work finishes rather than serving continuously. |
| CronJob | You need Jobs created repeatedly on a schedule. | Scheduled executions create completion-oriented Jobs. |
Deployment: interchangeable replicas
Use a Deployment when the service is stateless and any healthy replica can handle a request. For example, a web tier whose instances can be replaced without preserving per-Pod identity is a natural fit. The Deployment manages the desired set of Pods; it does not make stateful application behavior stateless by itself.
StatefulSet: stable identity or storage association
A StatefulSet is useful when the application relies on distinct Pod identities or persistent storage relationships. It is a workload-management choice, not a guarantee of database high availability. Replication, backups, and disaster recovery still depend on the application architecture and storage system.
DaemonSet: work that belongs on nodes
Use a DaemonSet for software that needs to run locally on cluster nodes—for example, a node agent or a component that provides node-level functionality. It can target all nodes or only those matching selection rules, so it is not necessarily one Pod on every machine.
Job and CronJob: work that ends
A Job manages a task intended to complete. A CronJob creates Jobs on a schedule, which is useful for recurring batch work. These differ from a continuously available service: the expected outcome is completion of a run, not a permanent set of request-serving replicas.
Keep access stable as Pods change
Pod membership and IP addresses can change as workloads are replaced or rescheduled. A Service gives clients a stable network abstraction for a logical set of backends, commonly selected Pods, instead of requiring them to track each Pod address. EndpointSlices provide information about the current backends.
Internal access and external HTTP routing
| Resource or API | Role | Consideration |
|---|---|---|
| Service | Provides a stable endpoint for a logical backend set. | Useful to clients that should not depend on individual Pod addresses. |
| Ingress | Consolidates HTTP routing rules at a cluster entry point. | It addresses HTTP routing rather than replacing the workload’s Service. |
| Gateway API | An extension API family for traffic routing with capabilities beyond Ingress and Service. | Available behavior depends on the implementation installed in the cluster. |
Think of these as different layers, not interchangeable names for the same object: a Service exposes a workload endpoint, while Ingress or Gateway API can describe how external HTTP traffic is routed into the cluster. Gateway API support and capabilities depend on the cluster’s installed implementation.
NetworkPolicy: traffic control depends on the network implementation
NetworkPolicy is an API for controlling network traffic, but creating a policy object alone does not establish that it will be enforced. Whether policies take effect depends on the cluster’s network implementation. Check the cluster’s networking documentation before relying on a policy as a security boundary.
Separate application configuration from container images
ConfigMaps and Secrets let a Pod consume configuration separately from its image. This can make it easier to provide different settings to the same application image in different environments.
- ConfigMap: stores non-confidential key-value configuration. Pods can consume it through environment variables, command arguments, or mounted files.
- Secret: is intended for confidential values such as passwords, tokens, and keys. Its presence as a Secret object does not, by itself, establish how the value is protected at rest; verify the cluster’s security configuration.
Base64 encoding is not encryption. Treat Secret data as sensitive and check how your cluster controls access to it and stores it. The actual protections depend on that cluster’s configuration and security practices.
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Scaling can mean changing how many application replicas run, adjusting the resources assigned to each replica, or changing the capacity of the cluster itself. Those are different actions and may involve different mechanisms.
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| Scaling concern | What changes | Relevant approach |
|---|---|---|
| Horizontal workload scaling | Number of workload replicas. | HorizontalPodAutoscaler can adjust replicas based on observed utilization. |
| Vertical workload scaling | Resources assigned to each replica. | Adjust per-replica CPU or memory resources. |
| Node scaling | Cluster node capacity. | Node autoscaling is separate from changing workload replica count. |
HorizontalPodAutoscaler
HorizontalPodAutoscaler is an API resource and controller that periodically adjusts the number of replicas based on observed utilization. The result depends on metrics being available, workload behavior, and cluster configuration; creating the resource does not remove those dependencies.
Event-driven and node autoscaling
Event-driven workload autoscaling is a separate approach; Kubernetes documentation describes KEDA, a CNCF-graduated project, in this context. Node autoscaling addresses cluster capacity rather than the number of application replicas. A workload can need more Pods while the cluster also needs more nodes to host them, so treat those as distinct scaling questions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Extend Kubernetes carefully
Kubernetes can be extended through mechanisms including CustomResourceDefinitions and API aggregation. These let a cluster expose additional APIs beyond the built-in resources. Hosted clusters and distributions may already install extensions, so check what your cluster provides before adding another component or assuming a particular API is available.
Because extensions and implementations vary, cluster-specific documentation matters for features such as Gateway API support, NetworkPolicy enforcement, and autoscaling integrations. Kubernetes’ portable API model does not mean every cluster has identical add-ons or operational behavior.
A practical way to choose
- Decide whether the workload runs continuously or completes. Choose a Deployment or StatefulSet for ongoing workloads; choose a Job for completion-oriented work, or a CronJob for scheduled Jobs.
- Ask whether replicas are interchangeable. If they are, a Deployment is usually the relevant controller. If individual Pods need stable identity or storage association, consider a StatefulSet.
- Check whether the software belongs on nodes. For node-local functionality, consider a DaemonSet and define which nodes it should target.
- Plan how clients reach the workload. Use a Service for a stable backend endpoint; add HTTP routing through Ingress or a supported Gateway API implementation when needed.
- Identify what kind of scaling you need. Separate replica count, per-replica resources, and node capacity before choosing an autoscaling approach.
- Verify cluster-specific behavior. Check installed extensions, metrics availability, network implementation, and security configuration instead of assuming those are uniform across clusters.
Further reading
For a book-length introduction, O’Reilly lists Kubernetes: Up and Running, 3rd Edition by Brendan Burns, Joe Beda, Kelsey Hightower, and Lachlan Evenson; its revision history gives a first release date of August 2, 2022. For version-sensitive behavior and current API details, use the Kubernetes documentation.
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