Running Kubernetes on AWS with Amazon EKS

Kubernetes on AWS usually means Amazon Elastic Kubernetes Service (EKS), a managed Kubernetes service that lets teams run clusters without operating the Kubernetes control plane themselves. AWS manages the EKS control plane, while customers remain responsible for workload architecture, cluster configuration, worker capacity, networking, security, observability, upgrades, and application operations.

The most important distinction is that EKS does not remove Kubernetes complexity; it changes which parts AWS operates for you. Teams still need solid Kubernetes fundamentals and cloud operating discipline.

Start with Kubernetes concepts before EKS features

Pods, Deployments, Services, ConfigMaps, Secrets, namespaces, controllers, scheduling, health probes, resource requests, and declarative configuration remain Kubernetes concepts regardless of cloud provider.

The Kubernetes fundamentals should be stable before platform-specific EKS features are layered on top.

Namespaces, labels, selectors, controllers, Services, and readiness/liveness probes form the operational language of the platform. AWS-specific features are easier to evaluate when you know what Kubernetes is trying to achieve independently.

Use a local or inexpensive test cluster to practice basic deployment and failure before adding cloud integrations. A managed service can hide enough infrastructure that weak Kubernetes fundamentals go unnoticed until production.

EKS manages the control plane, not the entire cluster

A managed control plane reduces the burden of operating API servers, etcd, and core control-plane components. Teams still need to choose node models, network design, storage, add-ons, access, policies, and upgrade strategy.

AWS now offers several operating modes and scaling options, but the architecture still needs to reflect workload requirements and operational maturity.

Teams still own cluster policy, workload configuration, add-on versions, node or Fargate strategy, networking, observability, backups, and application reliability. Managed control plane should be understood as reduced undifferentiated work, not zero operations.

Define the platform-team boundary clearly. Application teams need to know which cluster capabilities are self-service, which require platform support, and which responsibilities remain inside the workload.

Control-plane management reduces patching and availability work, but Kubernetes APIs and behavior still change over time. Application teams cannot assume that a managed service freezes the platform underneath them.

Define responsibility for cluster add-ons such as DNS, network plugins, storage drivers, ingress, policy, observability, and autoscaling. These components often sit between AWS-managed infrastructure and application-owned workloads.

Cost ownership belongs in the platform model too. Cluster control-plane fees, node capacity, load balancers, storage, observability, NAT, and data transfer can all contribute to application cost.

Chargeback or showback can help teams understand the economic effect of requests, replicas, storage, and idle environments without forcing every developer to become a FinOps specialist.

That ownership model should be explicit in runbooks so application teams know which failures AWS manages, which the platform team manages, and which remain inside the workload.

Worker capacity can use several operating models

Applications can run on managed node groups, self-managed EC2 capacity, Fargate for suitable workloads, and newer automated capacity patterns such as EKS Auto Mode depending on requirements.

Choose based on control, portability, scaling, performance, cost, workload constraints, and how much node lifecycle the platform team wants AWS to manage.

Capacity strategy should account for steady baseline, burst demand, interruption tolerance, specialized instance types, GPUs, and cost. Spot capacity can reduce price but requires workloads that can tolerate interruption.

Autoscaling also needs safeguards. Poor requests and limits can make schedulers and autoscalers react to inaccurate resource signals, leading to waste or instability.

Managed node groups can simplify lifecycle, while Karpenter or Auto Mode can respond more dynamically to workload needs. The right model depends on how much control the platform team needs over instance type, AMI, scheduling, and cost.

Use pod requests and limits carefully because scheduling and autoscaling depend on them. Understated requests create contention; overstated requests waste capacity and can make clusters look full when real utilization is low.

Networking connects Kubernetes abstractions with VPC reality

EKS workloads depend on VPC addressing, subnets, security controls, load balancers, DNS, routing, and the Kubernetes networking model.

Capacity planning must include pod IP behavior and subnet design. A cluster can have available compute while still failing to schedule or connect workloads because network address capacity or policy is wrong.

Understand pod addressing and the VPC CNI model, service exposure, load balancers, DNS, security groups, network policy, and egress. Kubernetes networking problems often span both cluster objects and AWS infrastructure.

Plan subnet capacity for pods as well as nodes. Large clusters can run into address pressure long before compute appears exhausted.

Network policy and security groups solve different layers of access control. Teams should know which boundary is enforced by Kubernetes and which by AWS networking so one is not assumed to replace the other.

Service exposure also matters. Internal load balancers, public load balancers, ingress controllers, service meshes, and private endpoints should be selected from application access requirements rather than one default pattern.

Identity and security require both AWS and Kubernetes controls

EKS security spans IAM, cluster access, Kubernetes RBAC, workload identity, secrets, network policy, image security, node hardening, and runtime controls.

Keep human administration separate from workload permissions. Applications should receive narrowly scoped identities rather than inheriting broad node or administrator credentials.

Use workload identity so pods receive narrowly scoped AWS permissions instead of inheriting broad node credentials. Separate cluster administration, deployment access, and application access.

Image provenance, admission controls, network policy, secrets, runtime security, and patching all contribute to the threat model. EKS does not make insecure Kubernetes manifests safe automatically.

Cluster access should be reviewed like any privileged system. Administrators, CI/CD systems, platform automation, and application developers may need different Kubernetes permissions even if they share AWS accounts.

Container images should come from trusted registries with scanning and provenance controls where practical. A secure cluster cannot compensate for a compromised image or overly privileged container.

Storage should follow workload state requirements

Stateless services are easy to reschedule; stateful applications need persistent storage, backup, consistency, and recovery planning.

AWS integrates EKS with storage such as EBS and EFS through supported drivers. The correct choice depends on access mode, performance, resilience, sharing, and lifecycle.

Stateful sets and persistent volumes require lifecycle planning. Understand whether data should survive rescheduling, node replacement, cluster rebuild, or regional disaster.

Backup should include application consistency where required. Copying a volume is not enough if the application needs coordinated database or transaction state.

Delivery should be declarative and repeatable

Kubernetes works best when manifests, Helm charts, GitOps definitions, and infrastructure configuration are version-controlled and reviewed.

Kubernetes delivery should include rollout strategy, configuration separation, secrets, validation, and rollback instead of treating `kubectl apply` as the whole deployment system.

Use environments and promotion rather than editing live resources manually. GitOps or CI/CD can make cluster state reviewable and reproducible when changes are driven from version-controlled definitions.

Rollout strategies should match application risk. Rolling, canary, blue/green, and feature-flag techniques solve different problems and should include health validation plus rollback.

Observability is essential for day-two operations

A production cluster needs metrics, logs, events, traces, workload health, node health, control-plane visibility, and alerting. Kubernetes can reschedule a failing pod without explaining why the application is unhealthy.

Observability should connect Kubernetes state with application and AWS infrastructure signals so operators can follow failures across layers.

Correlate Kubernetes events with application metrics, infrastructure state, deployment history, and cloud-service dependencies. A pod restart may be the symptom of an upstream timeout or downstream storage issue.

Define service-level indicators that represent user experience. Cluster CPU utilization alone cannot tell you whether the application is meeting its objective.

Track deployment and configuration changes alongside telemetry. A latency spike that begins immediately after a Helm release or node-image update should be investigated differently from a gradual capacity trend.

Use alerting sparingly. Kubernetes can generate enormous event volume; alerts should represent conditions that require action, while lower-value telemetry remains available for diagnosis.

Run game days for node failure, pod disruption, unavailable dependencies, and scaling events. Reliability improves when the team has seen how the platform behaves before a real incident.

Build dashboards around service questions, not cluster trivia. How many requests fail? Are users seeing latency? Are pods pending because of capacity? Is one availability zone unhealthy? Is an upstream AWS service throttling the application?

Correlate Kubernetes events with deployment identifiers and application traces so responders can move from symptom to recent change quickly.

Document ownership for alerts. A platform alert with no responding team creates noise; application alerts and cluster alerts should route to people who can actually act.

Upgrades and deprecations are continuous operational work

Kubernetes moves quickly. APIs are deprecated, versions age out, add-ons change, and cluster upgrades need planning. AWS provides release channels, upgrade guidance, and deprecation information, but customers still need compatibility testing.

Maintain an upgrade backlog and test workloads against supported versions before the maintenance window becomes urgent.

Keep an inventory of Kubernetes APIs and add-ons used by workloads. Deprecated APIs can block upgrades even when the application code itself has not changed.

Use maintenance windows and preproduction testing to validate cluster version, node image, CNI, storage drivers, ingress, observability agents, and security tooling together.

Large organizations often standardize cluster creation, networking, observability, security, and delivery through a platform team. This reduces repeated decisions and gives application teams a safer self-service path.

The platform-engineering skill set is relevant because EKS success depends as much on developer experience, guardrails, and operations as on Kubernetes syntax.

Create a supported-version policy for clusters and add-ons. Teams should know how far behind the current supported release they are allowed to fall and who owns remediation when a workload blocks upgrade.

Use deprecation insights and staging clusters before production upgrades. The best upgrade process finds incompatible APIs and add-ons while there is still time to change them safely.

Plan for add-on compatibility as part of cluster lifecycle. Ingress controllers, service meshes, policy engines, observability agents, and CSI/CNI components can lag the Kubernetes version and become the real upgrade blocker.

Document which team owns each dependency so upgrade work does not stall between application, platform, and security teams.

Standardize the paved path around cluster creation, namespaces, identities, network policy, ingress, storage classes, observability, secrets, deployment, and upgrade expectations so application teams do not reinvent the same platform decisions.

Platform teams should measure developer experience as well as cluster health. If secure deployment requires many manual tickets or undocumented exceptions, teams will bypass the platform even if its infrastructure is technically sound.

Keep escape hatches for legitimate exceptional workloads, but make them explicit and reviewed rather than allowing every team to fork the platform model.

Budget time for lifecycle work. Kubernetes platform teams that spend every sprint only on new features eventually accumulate unsupported versions, stale add-ons, and risky upgrade gaps.

Treat lifecycle as product work with planned capacity, dependency ownership, and success metrics rather than emergency maintenance.

Keep the upgrade process boring: documented prerequisites, staging validation, maintenance communication, rollback thinking, and post-upgrade checks. Reliable lifecycle work is a sign of platform maturity.