Kubernetes Networking Comparison: AKS vs EKS vs GKE vs ARO

Kubernetes networking is not the same across cloud providers.

AKS, EKS, GKE, and ARO all implement different IP allocation models — and those differences directly affect:

Subnet sizing
Pod density
Autoscaling limits
Upgrade safety
Risk of IP exhaustion

This guide compares how Kubernetes networking works across the major managed platforms in 2026.

The Core Question: Where Do Pod IPs Come From?

The biggest architectural difference between providers is:

Do pods consume subnet IP addresses?

Here is the high-level comparison:

Provider	Pod IP Source	Subnet Pressure
AKS (Flat)	Azure subnet	High
AKS (Overlay)	Overlay CIDR	Low
EKS	VPC subnet	High
GKE	Secondary alias range	Moderate
ARO	OpenShift overlay	Low

This single difference drives most subnet planning mistakes.

Amazon EKS Networking

In EKS:

Pods receive real VPC IP addresses.
ENI limits affect maximum pod density.
Subnet CIDR sizing directly impacts cluster scalability.

This model provides clean AWS-native routing — but can quickly exhaust subnets.

→ Deep Dive: How EKS Networking Works
→ Planner: EKS Subnet Calculator

Azure Kubernetes Service (AKS)

AKS supports multiple networking models:

Azure CNI (Flat)

Pods consume Azure subnet IPs.
High subnet pressure.
Direct VNet reachability.

Azure CNI Overlay

Pods use overlay CIDR.
Nodes consume subnet IPs.
Each node receives a fixed /24 pod slice.

Overlay is now recommended for most new deployments.

→ Deep Dive: How AKS Networking Works
→ Planner: AKS Subnet Calculator

Google Kubernetes Engine (GKE)

GKE uses alias IP ranges.

Nodes use primary subnet IPs.
Pods use a secondary CIDR range.
Each node typically receives a /24 alias slice.
Services may use a GKE-managed default range.

This creates clean separation between node and pod scaling.

→ Deep Dive: How GKE Networking Works
→ Planner: GKE Subnet Calculator

Azure Red Hat OpenShift (ARO)

ARO combines Azure infrastructure with OpenShift overlay networking.

Master and worker nodes live in Azure subnets.
Pods use overlay cluster CIDR.
Each node receives a /23 hostPrefix allocation.
Worker subnet sizing is primarily node-driven.

→ Deep Dive: How ARO Networking Works
→ Planner: ARO Subnet Calculator

Subnet Sizing Complexity Comparison

Provider	Subnet Planning Difficulty	Common Failure Mode
AKS Flat	High	Subnet exhaustion from pods
AKS Overlay	Moderate	Pod CIDR too small
EKS	High	ENI + subnet exhaustion
GKE	Moderate	Secondary range too small
ARO	Moderate	Worker subnet too small

The most dangerous configurations are:

AKS Flat with small subnets
EKS with /24 worker subnets
ARO worker subnet sized for day-one only

Scaling Limits You Must Understand

Each provider has a different scaling constraint:

EKS: ENI and IP-per-instance limits
AKS Overlay: Fixed /24 per node
GKE: Secondary range divided into per-node slices
ARO: /23 per node hostPrefix from cluster network

Ignoring these leads to autoscaling failures.

Which Model Is Safest?

From an IP exhaustion perspective:

Overlay models (AKS Overlay, ARO) reduce subnet pressure.
GKE’s alias IP model provides predictable separation.
EKS and AKS Flat require the most careful subnet math.

But “safest” depends on architecture goals:

Need pod-level VNet routing? → AKS Flat
Want simpler subnet math? → AKS Overlay or ARO
Want predictable secondary ranges? → GKE
Need native AWS routing? → EKS

How to Plan Kubernetes Subnets Safely

Regardless of provider:

Add 25–30% growth buffer.
Avoid /24 production worker subnets.
Plan for autoscaler surge capacity.
Document CIDR allocation before deployment.
Understand per-node IP allocation rules.

If you skip step 5, you will eventually hit scaling limits.

Use the Kubernetes Subnet Planner

To compare sizing across providers:

→ Kubernetes Subnet Planner (Multi-Cloud)

It calculates:

Node subnet requirements
Pod CIDR sizing
Growth buffer impact
Azure/AWS reserved IP behavior

Final Thoughts

Kubernetes networking differences are subtle — but critical.

The question isn’t:

“How big is my subnet?”

The real question is:

“Where do my pod IPs come from?”

Once you understand that, subnet planning becomes predictable.

And predictable networking prevents production outages.