Introduction: Why EKS vs GKE vs AKS Misconfigurations Keep Biting Teams
Every time I review a cluster incident post-mortem, the same pattern shows up: the team thought they were using “plain Kubernetes,” but subtle EKS vs GKE vs AKS misconfigurations turned a small mistake into a major outage or security gap. On paper, these managed services all run Kubernetes; in practice, each cloud provider layers on unique defaults, integrations, and limitations that can quietly diverge your environments.
What I’ve seen across multiple organizations is that people copy a working configuration from one provider, tweak it just enough to deploy on another, and assume they’re done. Months later, they discover that node IAM roles, network policies, pod security, or ingress behavior don’t match between clusters. That drift often stays invisible until the first real failure: a workload can’t scale, a pod can talk to something it shouldn’t, or a new region rollout breaks because a cloud-specific feature was accidentally baked into the manifest.
In my experience, the most painful incidents weren’t caused by exotic edge cases but by very basic assumptions—like expecting the same load balancer behavior, the same default storage class, or the same network policy enforcement across EKS, GKE, and AKS. This article focuses on those recurring misconfigurations, why they happen, and how to harden your setups so that your Kubernetes deployments stay reliable, secure, and portable across all three clouds.
1. Treating EKS, GKE, and AKS Networking as Identical
Networking is where EKS vs GKE vs AKS misconfigurations show up fastest. On one project, I watched three “identical” clusters behave completely differently under load purely because each cloud’s CNI, load balancer, and IP management had its own quirks. The YAML looked portable, but the underlying networking model was not.
At a glance, it’s all just Kubernetes Services, Ingress, and NetworkPolicies. Under the hood, though, EKS leans on AWS VPC and its CNI, GKE has its own VPC-native modes and optional Dataplane V2, and AKS rides on Azure VNet with different IP strategies. Assuming these are equivalent is how teams end up with IP exhaustion, flaky load balancers, or network policies that don’t actually isolate anything.
When I deploy to multiple clouds now, I start with the networking model first, not last: CNI choice, IP strategy, and how Services map to cloud load balancers. Once that’s clear, I adjust manifests and cluster config instead of pretending one-size-fits-all Kubernetes networking exists.
Different CNI Plugins and IP Address Management
The most common failure I see is IP exhaustion in one cloud but not the others, even with the same number of pods. That usually comes from ignoring how each managed service wires the CNI into its network fabric.
EKS (AWS VPC CNI by default): Pods can get IPs directly from the VPC subnet. This is great for deep VPC integration but burns through IPs quickly if you over-provision nodes and pods.
GKE: Has routes-based and VPC-native (alias IP) modes. Alias IPs dramatically change how pod IPs are allocated and how you size your subnets.
AKS: Offers kubenet and Azure CNI. Kubenet uses NAT and can conserve IPs; Azure CNI assigns IPs from the VNet and has different scaling and planning implications.
One thing I learned the hard way was to never reuse the same subnet sizing assumptions across EKS, GKE, and AKS. I now document, per cloud, how many pods per node and nodes per subnet I can safely support.
Even basic NetworkPolicy behavior can differ depending on whether you enable something like GKE Dataplane V2 or use a third-party CNI in EKS or AKS. I always test a simple deny-all and allow-specific policy in each environment to confirm enforcement works as expected.
Load Balancer Behavior and Ingress Differences
Another recurring pitfall is assuming a Kubernetes Service of type LoadBalancer is the same across clouds. In reality, each provider maps that to different load balancer SKUs, health check models, and annotations.
EKS: A LoadBalancer usually becomes a Classic or NLB/ALB depending on annotations and the AWS Load Balancer Controller. Health checks, cross-zone balancing, and TLS termination need explicit configuration.
GKE: Ingress is tightly integrated with Google Cloud HTTP(S) Load Balancing. Path-based routing, managed certificates, and global vs regional behavior all hinge on specific annotations and controller versions.
AKS: Azure Load Balancer and Application Gateway behave differently from AWS and GCP equivalents, especially around idle timeouts, session persistence, and SSL offload.
When I first tried to “standardize” Ingress across all three, I ended up with a pile of provider-specific annotations. What works better for me now is a small compatibility layer: a base Ingress manifest plus per-cloud overlays (Helm values, Kustomize patches, or separate YAML) to capture the load balancer details.
Here’s a simple example of a base Service that I keep cloud-agnostic and then override with provider-specific annotations in separate files:
On EKS I might apply an extra manifest with AWS-specific annotations, while on GKE or AKS I use their own annotations or dedicated Ingress resources. Keeping that separation has saved me from accidental coupling to one cloud’s load balancer behavior.
Service CIDRs, Pod CIDRs, and Cross-Cloud Connectivity
Cross-cloud communication is where hidden EKS vs GKE vs AKS misconfigurations often explode. If you’re building multi-region or multi-cloud meshes, overlapping pod and service CIDRs are a silent killer.
Service CIDRs: Each cluster gets a virtual network range for Services. If two clusters share that range and you later connect their networks (VPN, peering), traffic can go to the wrong place.
Pod CIDRs: Different CNI choices dictate whether pod IPs are routable across networks or hidden behind NAT. That affects service discovery, mTLS, and debugging.
DNS and service discovery: Some teams rely on external DNS, others on service mesh sidecars; misalignment here can cause intermittent failures that are tough to trace.
When I’m planning multi-cloud networking, I now treat CIDR planning as a first-class design task, not a default-cluster afterthought. I explicitly map out non-overlapping pod and service CIDRs for each EKS, GKE, and AKS cluster and verify that the CNI supports routing between them in the way the application expects.
To avoid painful surprises, I also regularly run simple connectivity tests (for example, curl between pods in different clusters) as part of my CI/CD or smoke tests. It’s a quick way to catch networking drift before it becomes a production incident linked to subtle, cloud-specific differences.
2. Misconfiguring Identity and IAM: IRSA vs GKE Workload Identity vs Managed Identity
Identity is where EKS vs GKE vs AKS misconfigurations quietly become security incidents. The Kubernetes API gives us ServiceAccounts, but each cloud wires those to its own IAM layer differently: IRSA on EKS, Workload Identity on GKE, and Managed Identity on AKS. Early in my multi-cloud journey, I treated them as interchangeable, and I ended up with over-privileged service accounts in one cluster and broken auth in another.
The danger usually comes from two shortcuts: reusing node-level credentials for pods, or mapping multiple Kubernetes service accounts to a single cloud identity. Both make things “just work” at first, but they remove the isolation you expect between workloads and make it very hard to reason about who can access which cloud resource.
How IRSA, Workload Identity, and Managed Identity Actually Differ
Conceptually, all three aim to solve the same problem: let a pod assume a cloud identity without static credentials. In practice, their trust models and configuration are surprisingly different.
EKS IRSA (IAM Roles for Service Accounts): Uses an OIDC provider and IAM roles. A Kubernetes ServiceAccount is annotated with an IAM role ARN, and AWS STS issues temporary credentials based on a trust policy that checks the service account and namespace.
GKE Workload Identity: Binds a Kubernetes ServiceAccount to a Google Service Account (GSA). GCP validates the Kubernetes identity via an external identity token, then issues tokens scoped to the GSA’s IAM bindings.
AKS Managed Identity: Uses Azure AD workload identities (or older pod-managed identities) to map Kubernetes service accounts to Azure AD applications/managed identities, which then carry role assignments in Azure RBAC.
When I first tried to keep a single Helm chart working across all three, I realized quickly that I needed a clear abstraction: the chart defines which capabilities a workload needs (for example, read from object storage), and each cloud environment defines how that maps to IRSA, Workload Identity, or Managed Identity.
Here’s a simplified Kubernetes snippet I often start with as a “base” identity-aware service account:
apiVersion: v1
kind: ServiceAccount
metadata:
name: app-sa
namespace: payments
# Cloud-specific annotations are added per environment
On EKS, I patch this with an IRSA role annotation; on GKE, I bind it to a GSA; on AKS, I associate it with an Azure workload identity. Keeping the base manifest clean makes it easier to avoid cross-cloud leakage of provider-specific details.
Common Failure Modes: Over-Privileged or Non-Functional Pods
Across real-world reviews, I keep seeing the same identity-related mistakes repeat.
Relying on node credentials: On EKS, skipping IRSA and letting pods use the node IAM role. It’s fast to set up but means every pod on that node can do everything the node can do (list S3 buckets, read secrets, etc.). I’ve seen staging clusters where a debug pod could access production data simply because they shared the same node role.
Shared cloud identity for many service accounts: Teams often bind multiple Kubernetes service accounts to a single GSA or Azure managed identity. That collapses workload boundaries, making blast radius massive if one pod is compromised.
Mismatched trust or binding config: With IRSA and Workload Identity, it’s easy to mismatch namespaces, service account names, or issuer URLs. The result is confusing 403s or fallback to node credentials if you’re not careful.
Forgetting least privilege: Even when the mapping is correct, the associated IAM policies or roles often start out with *:* permissions. Those defaults tend to survive into production way longer than anyone admits.
One thing I learned the hard way was that broken identity is often silent: pods keep running, but they either have far too many rights or quietly fail to access what they need at runtime. I now treat identity configuration as part of my application contract, not as an ops-only detail.
Practical Patterns for Safe Cross-Cloud Workload Identity
To avoid identity-related EKS vs GKE vs AKS misconfigurations, I use a few patterns that have worked well across teams.
Define capabilities, not cloud roles, in app manifests: For example, describe that app-sa needs read-only access to object storage in a specific bucket/container. Then map that to AWS IAM policies, GCP IAM roles, or Azure role assignments externally (Terraform, Pulumi, or dedicated IAM modules).
One workload, one cloud identity: As a rule, each critical workload gets its own IAM role, GSA, or managed identity. No sharing between unrelated services.
Namespace and service account scoping in trust policies: On EKS IRSA, I tightly scope the trust policy to the exact namespace and service account name instead of using wildcards. The same mindset applies to Workload Identity bindings and Azure workload identities.
Automated tests for identity behavior: I add smoke tests that verify a pod can access what it’s supposed to—and only that. For example, a test job that lists a single expected bucket but fails to list all buckets. This catches misconfigurations before they reach production.
Here’s a pseudo-pattern I’ve used in infrastructure-as-code (leaving out provider-specific details) to keep things aligned:
The IaC layer then translates storage.read.payments-bucket into S3/GCS/Blob Storage permissions, and binds that to IRSA/GSA/Managed Identity accordingly. That way, my security model is cloud-agnostic while the implementation stays cloud-specific.
3. Ignoring Control Plane and Node Upgrade Differences
One of the more subtle EKS vs GKE vs AKS misconfigurations I keep seeing is around upgrades. Teams treat all three as if they share the same control plane lifecycle, node upgrade behavior, and version skew guarantees. In reality, each provider moves at its own cadence, exposes different controls, and handles auto-upgrades differently. I’ve seen clusters where GKE silently upgraded the control plane weeks before EKS, breaking a shared deployment because of API deprecations and incompatible admission controllers.
When I plan multi-cloud Kubernetes now, I treat the upgrade story as part of the architecture: who owns the schedule, which versions are allowed, and how far I’m willing to drift between control plane and nodes on each provider.
Version Skew, Auto-Upgrades, and Hidden Drift
Each managed service enforces its own rules about supported version skew between the control plane and nodes, and about how and when upgrades are applied.
GKE: Often has auto-upgrade enabled by default (especially on Autopilot or standard clusters with default settings). The control plane may jump ahead a minor version, and node pools might follow on a schedule you didn’t explicitly plan for.
EKS: Generally keeps you in charge of both control plane and node upgrades. This is great for control, but it’s easy to end up running very old node AMIs with newer control planes if you forget to roll them, which can trigger subtle runtime issues.
AKS: Supports scheduled and automatic upgrades, but the defaults and behavior differ from GKE. I’ve seen teams assume “auto-upgrade” behaves identically and then get surprised by node pool reboots hitting peak traffic.
One thing I learned the hard way was that “Kubernetes 1.x” in three clouds doesn’t mean “identical behavior.” Cloud-specific feature gates, admission plugins, and CSI versions can all differ, even at the same nominal version. I now pin and document minimal and target versions per provider, and I validate critical API usage (Ingress, PodSecurity, PSP replacements) against the most restrictive environment first.
To keep myself honest, I also query the API for node and control plane versions regularly and alert on unexpected skew. A simple snippet like this has helped me spot drift early:
# Quick check of node versions in a cluster
kubectl get nodes -o json \
| jq -r '.items[] | .metadata.name + " -> " + .status.nodeInfo.kubeletVersion'
Upgrade Strategies That Actually Work Across Clouds
Rather than pretending upgrades are uniform, I design a per-cloud upgrade policy and then align them.
Disable or constrain auto-upgrades where necessary: On GKE and AKS, I often turn off fully-automatic node upgrades for production and instead use maintenance windows and manual triggers. That way, I can coordinate changes across EKS, GKE, and AKS in a controlled wave.
Use canary node pools: I create a small “canary” node pool in each cloud, upgrade it first, and move a low-risk workload there. If that passes, I roll the rest. This pattern has saved me from more than one CNI or CSI regression.
Continuously audit deprecated APIs: Tools like kubectl api-resources and offline linters help me find usage of deprecated APIs before a control plane upgrade removes them. I’ve baked this into CI so that new manifests can’t introduce APIs already deprecated in any of my target providers.
Align upgrade windows across providers: For multi-cloud apps, I choose a shared upgrade window and upgrade all clusters to compatible versions within that period. That keeps behavior, logs, and debugging expectations aligned.
In my experience, the teams that avoid upgrade-related outages are the ones that treat EKS, GKE, and AKS as three distinct products with different upgrade engines—not as three identical “Kubernetes” logos. Once you design around those differences, API deprecations and surprise node reboots stop being a recurring fire drill.
4. Underestimating Default Security Posture and RBAC Variations
Security is where EKS vs GKE vs AKS misconfigurations get surprisingly subtle. On paper, all three expose Kubernetes RBAC, PodSecurity (or its predecessors), and NetworkPolicies. In reality, each managed service ships with its own mix of defaults, add-ons, and admission controls. When I first helped a team go multi-cloud, we assumed that “default secure enough” meant roughly the same thing everywhere. It didn’t: one cluster allowed overly-privileged pods by default, another enforced stricter policies that silently broke our workloads.
What I’ve learned since is that you can’t copy a security model from one cloud and blindly apply it to the others. You have to understand which guardrails each provider enables (or doesn’t), then explicitly codify the security posture you want in Kubernetes itself.
RBAC Pitfalls: ClusterRoles, Cloud Integrations, and Bootstrap Users
RBAC drift is one of the easiest EKS vs GKE vs AKS misconfigurations to miss because your kubectl commands usually still work—until you rotate credentials or onboard a new team. Each provider bootstraps admin access and cloud integrations differently.
EKS: Uses aws-auth ConfigMap to map IAM roles and users into Kubernetes RBAC. I’ve seen teams drop a broad system:masters mapping for entire IAM groups, effectively giving cluster-admin to half the organization.
GKE: Integrates with Google IAM; permissions flow through Google groups and roles. It’s easy to over-assign roles/container.admin, which translates into cluster-admin across all namespaces.
AKS: Ties into Azure AD and Azure RBAC. If you don’t design role mappings carefully, you can end up with subscription-level roles effectively granting cluster-admin in ways that are hard to audit from the Kubernetes side.
One thing I started doing on every new cluster is explicitly creating a small set of Kubernetes ClusterRole and RoleBinding objects for core personas (platform admin, app operator, read-only observer), and then mapping cloud identities only to those. That way, I reduce the temptation to just give everyone cluster-admin because it’s “easier.”
Here’s a simple example I’ve reused as a baseline read-only role:
Each cloud then maps its IAM/GCP IAM/Azure AD groups to this ClusterRole instead of handing out full admin rights.
Pod Security, Admission Controls, and Legacy PSP Assumptions
After PodSecurityPolicies were deprecated, I saw a lot of teams assume that “someone else” was enforcing pod-level security. That assumption breaks differently in each managed service.
GKE: Often has opinionated security features available (like GKE Sandbox, built-in Pod Security levels, or extra admission controllers) but they’re not always enabled by default. Some clusters run essentially permissive unless you configure policies.
EKS: Leaves most pod-level security decisions to you. If you migrated from older PSPs and didn’t adopt PodSecurity admission or a policy engine like Gatekeeper or Kyverno, your EKS clusters might allow far more than you think (hostPath, privileged, hostNetwork, etc.).
AKS: Provides add-ons like Azure Policy for Kubernetes, but enforcement depends on which policies you actually assign. I’ve seen production AKS clusters with no meaningful pod security because policies were only deployed in a single test environment.
When I do security reviews now, I explicitly inventory which PodSecurity or admission policies are active in each cluster, not just what’s written in the design document. I also test a deliberately non-compliant pod (for example, privileged with hostPath) and confirm it’s rejected everywhere.
A minimal namespace-level PodSecurity configuration I often start with looks like this:
This doesn’t solve everything, but it gives me a consistent floor across EKS, GKE, and AKS while I layer on stricter policies with other tools.
Network Policies and the Illusion of Isolation
The last big blind spot I see is network policy. Many teams write NetworkPolicy manifests and assume they’re enforced, without checking whether the underlying CNI or provider fully supports them. The result is an “illusion of isolation”: manifests look tight, but any pod can still talk to anything.
EKS: The default AWS VPC CNI doesn’t enforce Kubernetes NetworkPolicy by itself. You usually need to add something like Calico or Cilium for meaningful enforcement.
GKE: Can enforce NetworkPolicy if you enable the right network mode (for example, Dataplane V2). I’ve seen clusters where policy support was accidentally disabled for cost reasons, but the policies stayed in the repo.
AKS: Behavior differs between kubenet and Azure CNI, and you must explicitly enable network policy. It’s common to see policies applied in YAML but no actual enforcement in the data plane.
In my experience, the simplest way to detect this class of EKS vs GKE vs AKS misconfigurations is to deploy a test policy that should clearly break something—and then verify it really does. For example, a NetworkPolicy that blocks all egress from a test pod, and then see if that pod can still curl an external URL.
If that pod can still reach the internet, I know network policies aren’t really being enforced—no matter what the manifests say. Once you align RBAC, pod security, and network policy behavior explicitly across EKS, GKE, and AKS, the security posture stops depending on whatever defaults the cloud provider happened to pick this year.
5. Misaligned Storage Classes, Volumes, and Backup Strategies
Storage is where I’ve seen some of the most expensive EKS vs GKE vs AKS misconfigurations. Teams assume a “default” StorageClass means the same thing everywhere—same performance, same durability, same reclaim policy. In reality, EKS, GKE, and AKS each wire their StorageClasses to different block and file backends with very different behavior. I’ve watched workloads run fine in GKE, then hit IOPS ceilings or data loss windows when lifted into EKS or AKS because nobody rethought the storage and backup plan.
These issues usually surface late: during a noisy neighbor incident, an AZ outage, or a restore drill that fails. That’s why I now treat storage class selection, volume configuration, and backup strategy as first-class design choices, not defaults I inherit from the cloud.
Default StorageClasses Are Not Equivalent
Each cloud’s “standard” StorageClass maps to a different underlying product:
EKS: Typically maps to EBS volumes (gp2/gp3 or io1/io2), zonal by default. Throughput and IOPS limits depend on volume size and type, and cross-AZ failover requires careful planning.
GKE: Often uses Compute Engine persistent disks (balanced, SSD, etc.), again zonal by default unless you opt into regional disks.
AKS: Maps to Azure Disks or Azure Files. Disk SKUs (Standard HDD/SSD, Premium, Ultra) have very different performance and regional availability characteristics.
When I first standardized manifests, I used a single storageClassName: standard everywhere. The YAML looked portable, but the actual SLAs and performance profiles were wildly different. Now I define logical classes like fast-block, standard-block, and shared-file, then implement them per-cloud.
For example, a portable PVC manifest might look like this:
On EKS, fast-block could be gp3 with tuned IOPS; on GKE, an SSD PD; on AKS, Premium SSD. The app spec stays the same, but the implementation is cloud-specific and explicit.
Access Modes, Zonal vs Regional, and StatefulSets
Another recurring trap is assuming the same access modes and failover patterns work everywhere:
RWO vs RWX: Many default StorageClasses only support ReadWriteOnce (RWO). On one project, a team assumed RWX semantics and spread a workload across nodes, only to see mysterious crashes in EKS because the underlying EBS volume couldn’t mount on multiple nodes simultaneously.
Zonal volumes: By default, most managed disks are zonal. If you spread nodes across AZs but bind a StatefulSet to a single PVC, you can get scheduling failures or long failovers when an AZ goes down.
Regional volumes: GKE makes regional persistent disks relatively easy; on AWS and Azure you might need a different combination of storage types and replication features to get similar resilience.
One thing I learned the hard way was to align StatefulSet pod topology with volume topology. I now explicitly pin stateful workloads to zones that match their volumes, or I design for regional redundancy with appropriate disk types where available.
Backups, Snapshots, and Cross-Cloud Restore Reality
Backups are often the last piece anyone thinks about, and the differences between providers are big:
Snapshots: EBS snapshots, GCE snapshots, and Azure snapshots all behave differently in terms of performance impact, cross-region copy options, and cost.
Cluster-level backups: Tools like Velero, Cloud-specific backup services, and database-native backups need different configuration per cloud. I’ve seen teams assume “Velero covers it” while never testing a full restore into another environment.
Portability: Volume snapshots are almost never portable across clouds; you usually restore into the same provider and then replicate data at the application or database layer.
When I design multi-cloud disaster recovery now, I don’t pretend I can ship raw volumes from GCP to AWS. Instead, I:
Use provider-native snapshots for fast, same-cloud recovery.
Rely on application-level replication (for example, database streaming replication or periodic logical backups) for cross-cloud resilience.
Automate and test restore procedures in each cloud separately, including recreating PVCs and StorageClasses.
Here’s a simple pattern I’ve used in CI to at least sanity-check PVC and backup assumptions:
If this job fails unexpectedly in one provider but not the others, it’s usually a sign that my storage or identity assumptions aren’t actually aligned. For a broader perspective on designing storage abstractions across clouds, see Kubernetes Storage 101: Concepts and Best Practices – Cloudian.
6. Overlooking Cloud-Specific Autoscaling and Cost Controls
Autoscaling is one of the easiest EKS vs GKE vs AKS misconfigurations to overlook, because everything looks fine in dev and then explodes in production bills. Each provider has its own cluster autoscaler behavior, node pool options, and integrations with spot/preemptible capacity. Early on, I tried to use the same HorizontalPodAutoscaler (HPA) settings and node sizing across all three; the result was unstable performance in one cloud and runaway costs in another.
These problems usually come from mixing three things incorrectly: pod autoscaling, cluster autoscaling, and pricing models. If they aren’t tuned per provider, you either overpay for idle capacity or thrash the cluster under load.
Different Autoscalers, Same YAML, Very Different Outcomes
All three platforms support HPAs, and all three offer some form of cluster autoscaler, but the knobs and defaults differ.
EKS: Commonly uses the Kubernetes Cluster Autoscaler deployed by you, alongside Karpenter or similar tools. You must wire it to your node groups, define min/max sizes, and explicitly allow scale-down. If you get the AWS IAM or node group labels wrong, pods can scale up but the cluster never follows.
GKE: Has built-in autoscaling for node pools and even autoprovisioning of new node pools. Defaults can be surprisingly aggressive, and GKE Autopilot hides nodes completely but charges per pod resources. I’ve seen teams under-request CPU and memory, which kept costs low on EKS but caused throttling and higher effective spending on Autopilot.
AKS: Integrates with the cluster autoscaler on top of VM scale sets. If you enable multiple node pools with different VM sizes or spot vs on-demand, you have to carefully define which workloads go where via taints and labels or you end up running critical workloads on spot nodes unintentionally.
These differences become painful when you reuse the same HPA everywhere. For example, a low target CPU on GKE plus aggressive node autoscaling can create constant churn, while the same HPA on EKS might never trigger if you’ve oversized nodes and disabled scale-down.
Here’s a basic HPA template I use as a starting point, then tune separately per cloud once I see real behavior:
In my experience, I’ll start with something like this, then adjust minReplicas, maxReplicas, and utilization targets separately on EKS, GKE, and AKS based on how their cluster autoscalers behave and how quickly node capacity shows up under load.
Cost Controls, Spot/Preemptible Nodes, and Right-Sizing
The other side of autoscaling is cost. Each provider has its own discounted capacity story, and misusing it can either save a fortune or cause brutal instability.
Spot / Preemptible / Low-Priority: EKS Spot, GCE preemptible VMs, and Azure Spot VMs are not interchangeable. Preemption behavior, notice periods, and availability patterns differ. I’ve seen teams treat them all as “cheap nodes” and deploy stateful or latency-sensitive workloads onto them without disruption-tolerant design.
Node sizing and bin-packing: In one project, we used large nodes in EKS and smaller nodes in GKE, but kept the same pod resource requests. That led to poor bin-packing in EKS (lots of wasted headroom per node) and frequent scaling churn in GKE. Once we right-sized pods and aligned node shapes per provider, costs stabilized.
Autoscaling limits: Min/max node counts, budget caps, and quota differ per cloud. Forgetting to set realistic maximums led one team I worked with to briefly scale far beyond their expected budget during a load test in GKE.
These days, I always combine autoscaling with explicit cost guards:
Set conservative max node limits per node pool and monitor for approaches to that limit.
Use taints and tolerations so only tolerant workloads land on spot/preemptible nodes.
Regularly run resource request/limit reports and right-size pods so HPAs and autoscalers work with accurate data.
A small but effective trick for me has been to export per-namespace cost estimates and surface them to developers. Once teams see how EKS, GKE, and AKS react differently to the same autoscaling configuration, they’re much more motivated to tune HPAs, node pools, and spot usage per cloud instead of trusting defaults.
7. No Unified Baseline for EKS vs GKE vs AKS Configuration Management
The most painful EKS vs GKE vs AKS misconfigurations I’ve had to unwind weren’t single bad settings – they were the result of not having any unified baseline at all. Each cloud grew its own snowflake cluster: different RBAC shapes, different storage classes, different admission policies, even different naming conventions. Everything worked “okay” until we tried to roll out a security control or version upgrade consistently, and suddenly every cluster behaved differently.
In my experience, the only sustainable way to run Kubernetes across providers is to define a portable baseline and a policy layer that describe how we do Kubernetes, then let each cloud implementation plug into that model.
From Snowflake Clusters to a Single Source of Truth
Without a baseline, every ops engineer “fixes” issues locally and drifts further away from the others. One cluster ends up with PodSecurity labels, another relies on Gatekeeper, another has no network policies at all. The same app behaves differently across environments and nobody can say which behavior is “correct.”
What finally worked for me was to treat cluster configuration like an app:
Define a global baseline: core namespaces, logging, metrics, PodSecurity, network policy defaults, and minimal RBAC roles.
Apply it via GitOps or IaC so clusters converge toward the same declared state.
Layer provider-specific patches (EKS, GKE, AKS) on top, rather than forking whole manifests.
Using a tool like Kustomize or Helm, I keep one “platform” chart or base and then have overlays per cloud. For example:
The key is that teams read and review a single baseline; the overlays stay as thin as possible and only cover true provider differences.
Policy as Code: Guardrails Across Providers
Even with a baseline, clusters drift unless something is continuously enforcing your rules. That’s where policy as code has saved me a lot of trouble. Instead of relying on people to remember not to create privileged pods or public LoadBalancers, I encode rules once and apply them everywhere.
Gatekeeper / Kyverno: Enforce structural policies (for example, all namespaces must have PodSecurity labels, no images from unapproved registries, no hostPath on non-system namespaces).
OPA in CI: Validate manifests before they ever reach a cluster, so EKS, GKE, and AKS all share the same pre-flight checks.
Cloud policy tools: Azure Policy for AKS, GCP Policy Controller, AWS Config rules – but I treat these as complements, not replacements, for in-cluster policies.
One practical pattern I like is to maintain a small policy repo that’s mounted into both CI and clusters, so everyone tests against the same rules. A simple Kyverno-style rule might look like:
That same policy applies cleanly whether the cluster is running on EKS, GKE, or AKS – and it stops cloud-specific drift in image sourcing before it starts.
Observability, Drift Detection, and Continual Alignment
The last piece of a unified baseline is feedback. Even with GitOps and policies, real clusters drift: hotfixes get applied manually, CRDs evolve, cloud defaults change. Without visibility, those small changes accumulate into large behavioral differences.
These are the practices I now use to keep EKS, GKE, and AKS aligned:
Inventory and compare key resources (StorageClasses, IngressClasses, PodSecurity labels, ClusterRoles) across clusters regularly. Differences should be intentional and documented.
Drift detection in GitOps: alert when resources change outside of Git, and require a follow-up PR to reconcile.
Baseline conformance tests: small automated tests that try to deploy a non-compliant pod, create a forbidden LoadBalancer, or violate network policy – and assert that all clusters reject the same things.
One thing I learned the hard way is that “we meant to configure it the same everywhere” doesn’t count. Only what’s codified, enforced, and tested consistently becomes reality. Once you establish a unified baseline and policy layer for EKS, GKE, and AKS, the individual misconfigurations discussed earlier become much easier to spot – and much harder to reintroduce by accident. For a deeper blueprint on doing this at scale, look for a A Validated Pattern for Multi-Cloud GitOps – Red Hat.
Conclusion: Making EKS vs GKE vs AKS Differences Work For You, Not Against You
Running Kubernetes across clouds isn’t just about learning three different UIs—it’s about respecting three different sets of assumptions. In my experience, most EKS vs GKE vs AKS misconfigurations trace back to pretending they’re identical: networking glued to the wrong load balancer model, upgrades treated as uniform, security defaults trusted blindly, storage classes mapped 1:1, autoscaling copied without cost awareness, and no unified configuration baseline to tie everything together.
The good news is that the same differences that hurt you at first can become an advantage once you design for them. The pattern that’s worked best for me is simple but disciplined:
Define a portable baseline for security, networking, storage, and RBAC that applies everywhere.
Add cloud-specific overlays for things that truly differ (load balancers, IAM, storage types, autoscalers).
Enforce it all with policy as code, GitOps, and regular conformance tests so drift gets caught early.
If you pick one next step after reading this, I’d start by documenting your current “as-built” posture for EKS, GKE, and AKS side by side—then design a single baseline you wish they all followed. From there, you can iteratively close the gap, turn misconfigurations into lessons learned, and let each provider’s strengths work for you instead of against you.
Hi, I’m Cary Huang — a tech enthusiast based in Canada. I’ve spent years working with complex production systems and open-source software. Through TechBuddies.io, my team and I share practical engineering insights, curate relevant tech news, and recommend useful tools and products to help developers learn and work more effectively.
