What Is Kubernetes? Definitions, Components & Use Cases [2025]

What Is Kubernetes? 

Kubernetes is an open-source platform to automate deploying, scaling, and managing containerized applications. It schedules and runs containers across clusters of hosts, managing the lifecycle of applications and ensuring they remain available and performant as load and resource requirements change. 

Kubernetes abstracts the complexity of the underlying infrastructure, allowing developers and operators to focus on application logic rather than server and container management.

By standardizing how containers are orchestrated, Kubernetes enables organizations to achieve greater consistency and reliability in their application delivery workflows. It supports self-healing, allowing failed containers to be automatically restarted or rescheduled. Its platform-agnostic approach makes Kubernetes compatible with private, public, and hybrid cloud environments.

This is part of a series of articles about Kubernetes cost optimization

A Brief History of Kubernetes and What’s Next 

Kubernetes originated at Google, where it was developed as an internal project called Borg. Borg was used to manage the company’s large-scale container deployments, but its details remained proprietary. Drawing on this experience, Google introduced Kubernetes as an open-source alternative.

The Kubernetes project was publicly released in June 2014. It combined lessons from Borg with ideas from other container orchestration tools. Just a year later, in 2015, Kubernetes hit version 1.0 and was donated to the newly formed Cloud Native Computing Foundation (CNCF), which helped standardize and foster community-driven development.

Primary innovations in the next versions of Kubernetes (i.e., Kubernetes 1.32) include:

Kubernetes 1.10

• Performance improvements: Kubernetes 1.1 (which aligns with early 1.x development, including 1.10 milestones) focused heavily on scalability and performance enhancements. Cluster capacity significantly increased, with support for running thousands of nodes and tens of thousands of pods per cluster.
• Enhanced networking support: This release introduced native support for load balancers and Ingress, allowing external traffic routing to Kubernetes services. These features simplified exposing applications to users outside the cluster.
• kubectl improvements: The kubectl command-line tool gained usability and feature enhancements, making cluster interaction and troubleshooting more user-friendly.
• Horizontal Pod Autoscaling (HPA): Kubernetes introduced Horizontal Pod Autoscaling, allowing clusters to automatically adjust the number of pod replicas in response to observed CPU utilization. This feature improved workload responsiveness and resource efficiency.
• Cluster federation (early work): Initial efforts began towards Kubernetes federation, targeting multi-cluster management for improved availability and geographic distribution.

Kubernetes 1.20

• Docker runtime deprecation: Kubernetes 1.20 marked the deprecation of Docker as a container runtime. Kubernetes began shifting towards containerd and CRI-O to align with the Container Runtime Interface (CRI) standard.
• Volume snapshot operations become GA: The Volume Snapshot feature, which allows users to create, restore, and manage snapshots of persistent volumes, graduated to General Availability (GA), making it stable for production use.
• API server logging enhancements: A structured logging feature was introduced for the kube-apiserver. This change improves log readability and simplifies downstream log analysis tools' integration.
• Process namespace sharing for pods: The ability for containers within the same pod to share a process namespace became generally available. This allows better debugging and management of processes across containers in the same pod.

Kubernetes 1.30

• Improved sidecar container support: Kubernetes 1.30 introduced stable support for sidecar containers with the new startupProbe and terminationGracePeriodSeconds settings. This change improves lifecycle management for sidecar containers by enabling better coordination between application containers and their sidecars.
• Node-scoped volume limits: Node-level volume limits for CSI drivers became generally available. This ensures Kubernetes can accurately track and enforce the number of volumes attached to nodes, improving cluster stability.
• Gateway API enhancements: The Gateway API reached beta maturity, introducing more flexible and expressive routing capabilities for Kubernetes networking. This feature offers enhanced support for HTTP routing and traffic management beyond what Ingress resources can provide.
• Built-in APIs for pod scheduling controls: Kubernetes 1.30 added APIs for node selection policies, giving users more control over where pods run based on node properties and scheduling preferences.

Kubernetes 1.31

• Dynamic resource allocation API: Kubernetes 1.31 introduced the Dynamic Resource Allocation API as a beta feature. This allows workloads to request non-standard or device-specific resources, such as GPUs with special drivers or AI accelerators.
• Node log query API: A new alpha Node Log Query API enables users to query logs directly from nodes without needing to install a full log collection pipeline, simplifying debugging and troubleshooting in some use cases.
• Volume group snapshots: Support for creating snapshots of multiple persistent volumes as a single, consistent group reached alpha status. This is useful for applications that require consistent backups across multiple volumes.
• Persistent volume health monitoring: Kubernetes added alpha support for monitoring persistent volume health via CSI drivers. This allows early detection of storage-related issues before they impact application performance.

Kubernetes 1.32 

• Control plane node taints in GA: Kubernetes 1.32 graduated the control plane node taint feature to General Availability. This feature prevents non-control-plane workloads from being scheduled on control plane nodes by default, improving cluster stability and security.
• CronJobs reach GA: After years in beta, the CronJob resource, which manages time-based job scheduling in Kubernetes, became generally available. This ensures long-term API stability for workload scheduling on defined time intervals.
• CSI storage capacity tracking in GA: The CSI storage capacity tracking feature graduated to GA, helping the Kubernetes scheduler make better decisions about where to place pods that require storage, especially in dynamic environments.
• ReadWriteOncePod access mode GA: Kubernetes 1.32 introduced ReadWriteOncePod (RWO-P) volume access mode as a GA feature. This restricts volume access to a single pod at a time, enhancing control for stateful workloads needing exclusive volume access.
• Service internal traffic policy enhancements: Enhancements to the internal traffic policy for Services allow more control over internal versus external traffic routing, giving operators more flexibility in service exposure and traffic management.

Here are some upcoming innovations expected in future versions of Kubernetes:

• AI-driven operations and automation: Future Kubernetes releases aim to integrate more intelligent automation, using AI and machine learning to optimize scaling, predict failures, and manage workloads with minimal human intervention.
• Improved multicluster management: Efforts are underway to simplify and enhance multicluster setups, making it easier to manage multiple clusters from a single control plane and improve workload distribution across them.
• Advanced security features: Upcoming versions will include stronger defaults and enhanced runtime protections, such as improved identity management, zero trust networking, and better integration with security tools.
• Edge and IoT enhancements: Kubernetes is evolving to better support edge computing and IoT use cases, with reduced footprint deployments and optimized networking for resource-constrained environments.
• Refined developer experience: The Kubernetes ecosystem is introducing faster tooling, simplified APIs, and improved documentation to make onboarding and day-to-day development tasks easier for engineers.

The Benefits of Kubernetes 

Kubernetes provides features that simplify the management of containerized applications. Its architecture and capabilities improve operational efficiency, boost application reliability, and enable scalability across environments.

Here are some key benefits of using Kubernetes:

• Automated scheduling and placement: Kubernetes automatically schedules containers based on resource requirements and availability, ensuring efficient use of infrastructure and minimizing manual intervention.
• Self-healing capabilities: If a container fails, Kubernetes can automatically restart or replace it, ensuring application availability and reducing downtime.
• Horizontal scaling: Applications can be scaled up or down automatically based on CPU usage or custom metrics.
• Service discovery and load balancing: Kubernetes provides built-in service discovery and load balancing, routing traffic to the appropriate containers and distributing it evenly.
• Declarative configuration: Users can define application infrastructure using declarative manifests (YAML or JSON), making deployments reproducible and easier to manage.
• Extensibility and ecosystem: Its plugin-based architecture and broad ecosystem (including tools like Helm and Prometheus) allow customization and integration with CI/CD, monitoring, and security tools.
• Resource efficiency: By co-locating workloads on shared infrastructure and managing resource allocation, Kubernetes helps improve utilization and reduce costs.

Core Components and Architecture of Kubernetes 

Control Plane Components

The control plane is responsible for maintaining the desired state of the Kubernetes cluster. It makes global decisions about the cluster, such as scheduling and responding to cluster events.

• kube-apiserver: Acts as the front-end for the Kubernetes control plane. It exposes the Kubernetes API and is the entry point for all administrative tasks.
• etcd: A consistent and highly available key-value store that stores all cluster data, including configuration and state.
• kube-scheduler: Assigns newly created pods to nodes based on resource availability and defined policies.
• kube-controller-manager: Runs various controllers that regulate the state of the cluster, such as the replication controller, endpoint controller, and namespace controller.
• cloud-controller-manager: Integrates with cloud service APIs to manage resources like load balancers, storage, and networking.

Node Components

Node components run on every node and maintain the running pods and their associated containers.

• kubelet: An agent that ensures containers are running in a Pod as specified. It receives PodSpecs from the API server and manages their lifecycle.
• kube-proxy: Maintains network rules on nodes and handles traffic routing to ensure network communication to and from Pods.
• Container runtime: The software responsible for running containers. Kubernetes supports multiple runtimes, including containerd, cri-o, and Docker (though Docker is deprecated as a runtime).

Kubernetes Objects

Kubernetes objects represent the desired state of the system. They describe what applications are running and how they interact.

• Pod: The smallest deployable unit, which encapsulates one or more containers that share storage, network, and a specification.
• Service: An abstraction that defines a logical set of Pods and a policy by which to access them, typically used for load balancing.
• Deployment: Manages stateless applications, handling updates and ensuring a specified number of Pods are always running.
• StatefulSet: Used for managing stateful applications, ensuring stable network identities and persistent storage.
• ConfigMap and Secret: Used to inject configuration data and sensitive information (like passwords) into Pods without hardcoding them into images.
• Namespace: Provides a way to divide cluster resources between multiple users or teams, supporting multi-tenancy.

Kubernetes vs. Docker Container 

Docker and Kubernetes serve different but complementary purposes in the container ecosystem. Docker is a container runtime that enables the creation, deployment, and running of containers, while Kubernetes is an orchestration platform that manages and automates container operations across clusters.

• Scope and functionality: Docker focuses on packaging applications into containers and managing them on a single host. It includes tools like the Docker CLI and Docker Engine to build, run, and manage containers locally. Kubernetes, by contrast, manages applications composed of many containers distributed across multiple nodes, providing features like load balancing, self-healing, and service discovery.
• Container lifecycle management: While Docker can start and stop individual containers, it lacks native capabilities for managing the lifecycle of complex, multi-container applications across a cluster. Kubernetes adds these capabilities, supporting rolling updates, automated restarts, scaling, and health monitoring.
• Networking and load balancing: Docker provides basic networking and port mapping between containers and the host. Kubernetes extends this with a built-in service model that includes automatic load balancing and DNS-based service discovery.
• Configuration and secrets management: Docker supports environment variables and volumes for configuration, but lacks built-in mechanisms for secure secret handling. Kubernetes addresses this with ConfigMaps and Secrets, allowing separation of configuration data and sensitive information from container images.
• Deployment and scaling: In Docker, scaling typically involves manually launching more containers or using Docker Compose. Kubernetes automates this with declarative configurations and built-in support for horizontal scaling.

In summary, Docker is ideal for building and running containers, while Kubernetes is essential for orchestrating and managing containerized applications at scale. Most Kubernetes environments today run Docker or its alternatives (like containerd) under the hood as the container runtime.

Key Kubernetes Use Cases 

Microservices Deployment

Kubernetes is well-suited for deploying microservices architectures, where multiple loosely coupled services interact to form a single application. Its service discovery, load balancing, and automated scaling capabilities simplify the management and evolution of multi-component applications. Developers can independently update, scale, or deploy individual microservices without impacting others.

Namespaces, network policies, and resource quotas help enforce logical separation, security boundaries, and resource allocation for each microservice within the cluster. This capability promotes organizational autonomy and supports diverse deployment patterns, such as multi-tenancy or team-specific environments.

Application Scaling

One of Kubernetes’s core strengths lies in its ability to automate scaling of application workloads based on demand. Horizontal Pod Autoscaling (HPA) continuously monitors CPU utilization or custom metrics and automatically increases or decreases the number of pod replicas to match usage patterns. This feature ensures that applications remain responsive as traffic changes.

Additionally, Kubernetes supports vertical scaling and cluster autoscaling, allowing it to allocate more or fewer resources to pods, or even add/remove nodes from the underlying infrastructure as needed. This elastic behavior reduces operational overhead and cloud infrastructure costs while maintaining reliability and performance for both stateful and stateless applications.

CI/CD

Kubernetes fits naturally into modern continuous integration and continuous deployment (CI/CD) pipelines, helping teams achieve fast, repeatable, and reliable application delivery. Developers can automate deployments with declarative manifests, reduce environment drift, and consistently promote changes from development to production. This integration allows pipelines to spin up ephemeral test environments on demand.

By leveraging Kubernetes for CI/CD, organizations gain fine-grained control over rollout strategies, including blue-green deployments and canary releases. Built-in features like rolling updates and rollbacks enable rapid iteration without service disruption. Kubernetes also enables easy integration with tools such as Jenkins, GitLab CI, and ArgoCD.

Hybrid and Multi-Cloud Deployments

Kubernetes provides a consistent abstraction layer that allows organizations to deploy and manage applications across on-premises data centers, public clouds, or a combination of both. This hybrid and multi-cloud capability helps prevent vendor lock-in and supports workload portability, disaster recovery, and global distribution. With Kubernetes, companies can place services where they make the most sense operationally or economically.

Kubernetes also supports federation and cluster management tools that synchronize deployments across multiple clusters in different environments. This simplifies governance, policy enforcement, and monitoring across complex hybrid or multi-cloud landscapes while enabling seamless migration, scaling, and failover strategies.

AI, Machine Learning, and Big Data Workloads

Kubernetes increasingly powers AI, machine learning (ML), and big data workloads by orchestrating distributed and resource-intensive processing environments. It can manage GPU and TPU resources, ensure proper scheduling of computational tasks, and coordinate data pipeline execution. This helps teams standardize infrastructure while meeting specialized requirements for model training, serving, and data processing at scale.

The Kubernetes ecosystem offers integrations with frameworks such as Kubeflow for ML workflows and supports deployment of scalable analytics platforms like Spark or TensorFlow clusters. Automated scalability, self-healing, and resource isolation streamline operations.

Notable Kubernetes Challenges 

Configuration Complexity

Configuring Kubernetes clusters can be daunting due to the sheer number of objects, options, and best practices involved. From crafting declarative YAML manifests to setting up networking, storage, and security policies, organizations must invest time and expertise to avoid misconfigurations. Human error or inconsistency in configuration can introduce security vulnerabilities, resource inefficiencies, or service disruptions if not carefully managed and reviewed.

Moreover, maintaining configuration across environments (development, staging, production) increases complexity. Tools like Helm, Kustomize, and GitOps workflows can mitigate some pain points, but they introduce learning curves and operational overhead of their own.

Resource Management and Cost Optimization

Efficiently managing resources in a Kubernetes cluster requires careful planning, monitoring, and tuning. Defining accurate resource requests and limits for CPU and memory prevents scenarios where applications starve each other or consume excessive resources. Poorly configured resource allocations can lead to cluster instability, performance bottlenecks, or excessive operational costs.

Kubernetes provides tools such as ResourceQuota, LimitRange, and the Vertical Pod Autoscaler to help with resource governance. However, effective resource management often demands continuous analysis of application metrics, proactive adjustment, and a solid understanding of workload profiles.

Networking Challenges

Kubernetes relies on a flat, unified networking model where every pod can communicate with every other pod across nodes. This design simplifies service discovery but introduces networking complexity. Understanding and configuring networking components like Services, Ingress controllers, Network Policies, and overlay networks is a frequent source of issues for operators. Misconfigured networking can lead to intermittent failures, degraded performance, or security exposure.

Integrating Kubernetes clusters with existing infrastructure—such as firewalls, load balancers, or legacy systems—can be complex. Network troubleshooting tools and cloud-provider-specific integrations are maturing, but often require advanced expertise.

Observability and Monitoring

Effective observability and monitoring are essential for managing applications at scale in Kubernetes. Native logging, monitoring, and alerting capabilities are limited, pushing operators to adopt solutions such as Prometheus, Grafana, ELK stack, or commercial observability platforms. These tools must be correctly configured to collect, store, and visualize performance indicators, error rates, and system health metrics.

However, deploying, scaling, and maintaining observability stacks increase operational burden and can introduce additional security or resource concerns. Correlating application and infrastructure metrics, logs, and traces across distributed microservices landscapes is complex and may require custom instrumentation.

Learn more in our detailed guide to Kubernetes cost monitoring 

Best Practices for Implementing Kubernetes in Production 

1. Implement Infrastructure as Code and GitOps Practices

Embracing Infrastructure as Code (IaC) is crucial for managing Kubernetes clusters consistently and reproducibly. Tools like Terraform, Pulumi, or Ansible enable declarative and version-controlled configuration of your cloud resources and cluster components. This practice minimizes human error, reduces inconsistencies between environments, and accelerates disaster recovery and new cluster provisioning.

When combined with GitOps workflows, IaC empowers teams to automate change management, peer review, and rollbacks for cluster definitions alongside application code. Maintaining both infrastructure and application manifests as code brings operational discipline and clarity.

2. Implement High Availability

Building high availability (HA) into a Kubernetes environment is essential for minimizing downtime and ensuring business continuity. This involves provisioning multiple control plane nodes and distributing workloads across multiple worker nodes and availability zones. HA setups are designed to withstand single points of failure, such as node crashes or network outages, and allow platform services to recover automatically.

Additionally, administrators should pay attention to resilient networking, storage backends, and backup strategies. Implementing health checks, replica sets, and readiness or liveness probes further contributes to workload availability. Testing failure scenarios through chaos engineering or game days validates that recovery mechanisms work as intended.

3. Automate Deployments with CI/CD Pipelines

Automating Kubernetes deployments via CI/CD pipelines streamlines application delivery, increases deployment frequency, and reduces risk. Pipelines frequently include steps for building images, running tests, scanning vulnerabilities, and applying manifests to clusters as part of a reliable and repeatable workflow. Automation supports fast feedback and rapid rollback if issues are detected post-deployment.

Integration with Kubernetes-native CD tools like ArgoCD or Flux further standardizes and secures deployment operations. Automated pipelines improve developer productivity and eliminate manual deployment errors.

4. Implement Role-Based Access Control (RBAC)

Kubernetes Role-Based Access Control (RBAC) provides fine-grained security over who can access and modify cluster resources. By creating granular roles and binding them to users or service accounts, organizations can enforce the principle of least privilege and protect sensitive workloads. Effective RBAC policies reduce the attack surface and support regulatory compliance.

Maintaining and auditing RBAC configurations requires ongoing monitoring as teams and workloads evolve. Coupling RBAC with authentication integrations (e.g., OIDC, LDAP) ensures that access to the cluster aligns with organizational identity policies.

5. Plan Kubernetes Upgrades to Minimize Downtime

Kubernetes evolves rapidly, with regular releases introducing new features, improvements, and security patches. Upgrading clusters requires careful planning to avoid application downtime and service disruptions. Administrators should test upgrades in staging environments, evaluate deprecated APIs, and validate compatibility with third-party integrations before touching production clusters.

Automating upgrade workflows and leveraging managed Kubernetes services or upgrade tools (such as kubeadm or Cluster API) can further reduce the risk and manual effort involved. Clear communication with stakeholders, robust rollback plans, and comprehensive observability are essential to ensure that system reliability is maintained throughout the upgrade process.```

6. Use Advanced Tools for Cost Optimization 

Cost optimization in Kubernetes involves continuous visibility into resource usage and proactive adjustments to avoid waste. This includes analyzing how workloads consume CPU, memory, and storage across nodes, and identifying underutilized or overprovisioned resources. Accurate right-sizing of pods and aligning request and limit settings with actual workload behavior can reduce overhead and ensure better node density.

In production environments, teams can implement policies and automation to clean up unused resources like orphaned volumes, idle services, and forgotten namespaces. Scheduling batch workloads during off-peak hours, applying resource quotas, and leveraging spot or preemptible instances are additional strategies to cut costs without sacrificing performance. Regular audits and dashboards help detect anomalies and track progress toward budget goals.

Why you should consider Finout when managing Kubernetes costs

Finout's FinOps solution excels in managing complex Kubernetes environments by enabling dynamic shared cost reallocation across the entire infrastructure. This capability is crucial for businesses operating in multi-cloud or hybrid environments, where cost attribution can become complicated due to the intermingling of resources across different platforms and services.

The ability to perform on-the-fly cost reallocation allows Finout to provide a nuanced view of financial data, aligning costs with actual usage. This is especially beneficial in Kubernetes settings where resources are dynamically scaled and vary significantly between teams or projects. By reallocating costs based on current usage, Finout ensures that each department or project is accurately charged for the resources they consume, enhancing accountability and promoting efficient resource use.

Moreover, Finout’s robust allocation features support complex financial management tasks such as showback and chargeback, making it easier for organizations to understand their spending and make informed budgeting decisions. This level of financial granularity and control is essential for companies looking to optimize their cloud expenditure and maximize their return on investment in cloud technologies.

Learn more about Finout for Kubernetes cost management