EKS vs GKE for Payment Processing. Architecture Decisions for Regulated Workloads.

Choosing between Amazon EKS and Google GKE is rarely about Kubernetes itself - the API is the same. The choice is about everything around the cluster: network architecture, identity integration, compliance tooling, operational complexity, and cost structure.

For fintech teams, the wrong choice means months of rework to meet auditor requirements. The right choice means your platform engineering team spends time on product differentiation, not fighting infrastructure limitations.

The compliance context: PCI DSS on Kubernetes

Before comparing platforms, a quick framing. PCI DSS (Payment Card Industry Data Security Standard) requires:

• Network segmentation between in-scope and out-of-scope systems
• Encryption of cardholder data in transit and at rest
• Strict access control with audit trails
• Vulnerability management and regular patching
• Logging and monitoring of all access to cardholder data

Both EKS and GKE can meet these requirements. The question is: which makes it easier and cheaper to demonstrate compliance during your QSA audit?

Network isolation and segmentation

Network architecture is where the platforms diverge most significantly for regulated workloads.

EKS network model

EKS uses the Amazon VPC CNI plugin by default. Every pod gets a real VPC IP address from your subnet. This means:

• Security Groups per pod - you can attach AWS Security Groups directly to pods, giving you L4 network policy enforcement at the VPC level. Your auditor sees familiar firewall rules, not Kubernetes-native NetworkPolicies they may not understand.
• VPC isolation - your cluster lives in a VPC with standard AWS constructs: private subnets, NACLs, Transit Gateway for cross-VPC connectivity. If your organisation already has a well-architected AWS landing zone, EKS slots in without new networking concepts.
• PrivateLink - services that need to communicate with payment processors or bank APIs can use VPC PrivateLink endpoints. Traffic never touches the public internet.

The downside: the VPC CNI consumes IP addresses rapidly. A cluster with 200 pods on m5.large nodes (which support ~29 IPs each) needs careful CIDR planning. Secondary CIDR blocks or prefix delegation mitigate this, but it is additional operational complexity.

GKE network model

GKE uses a VPC-native cluster model with alias IP ranges. Pods get IPs from a secondary range on the node’s subnet. Key differences:

• GKE Dataplane V2 (Cilium-based) - enabled by default on new clusters. Gives you kernel-level network policy enforcement with eBPF. Network policies are not optional or “soft” - they are enforced at the data plane. Auditors appreciate that policies cannot be bypassed by misconfigured kube-proxy rules.
• VPC Service Controls - a perimeter around your GCP resources that prevents data exfiltration. Even if a pod is compromised, VPC Service Controls can prevent API calls to resources outside the defined perimeter. This is GCP-specific and has no direct EKS equivalent.
• Private Service Connect - GCP’s equivalent to PrivateLink. Connectivity to payment processors and banking APIs stays within Google’s network.
• Hierarchical Firewalls - organisation-level firewall policies that cascade down to projects. Your security team can enforce baseline rules that individual project owners cannot override.

The downside: GKE’s networking model is less familiar to teams coming from AWS. The abstraction is different - you think in terms of VPC, subnets, and firewall rules, but the mapping to pods is handled differently than the “one pod = one VPC IP” model of EKS.

Verdict: network isolation

For PCI DSS environments, GKE has a slight edge due to VPC Service Controls (data exfiltration prevention) and Dataplane V2’s kernel-level enforcement. However, if your team already operates AWS infrastructure and your auditor is familiar with Security Groups - EKS is easier to audit because the constructs are well-understood.

Identity and access management

EKS + AWS IAM

EKS uses IAM Roles for Service Accounts (IRSA) or the newer EKS Pod Identity to assign AWS IAM roles to Kubernetes pods. This means:

• Pod A (payment processing) gets an IAM role that can access the card data KMS key
• Pod B (reporting) gets a different role with read-only access to anonymised data
• Access is audited in CloudTrail at the IAM level

Integration with existing AWS IAM policies is seamless if you already manage hundreds of roles. Your security team continues using their existing IAM review processes.

GKE + Google Cloud IAM

GKE uses Workload Identity to bind Kubernetes service accounts to Google Cloud IAM service accounts. The model is similar to IRSA:

• Each Kubernetes service account maps to a GCP service account
• The GCP service account has IAM bindings to specific resources
• Access is audited in Cloud Audit Logs

GKE adds Binary Authorization - a policy that enforces only signed, verified container images can run on your cluster. For payment processing, this means you can prove to auditors that only images that passed your security scanning pipeline reached production. EKS has equivalent functionality via AWS Signer and admission controllers, but Binary Authorization is built-in and requires less setup.

Verdict: identity

Roughly equivalent. Choose based on your existing IAM ecosystem. If your organisation already has mature AWS IAM governance - EKS. If you are greenfield or already on Google Cloud - GKE’s Workload Identity + Binary Authorization gives you a slightly faster path to compliance evidence.

Encryption and key management

EKS

• Secrets encryption: EKS supports envelope encryption of Kubernetes secrets using AWS KMS. You provide a KMS key, EKS encrypts the etcd data at rest.
• Storage encryption: EBS volumes (used for PVCs) support KMS encryption by default.
• In-transit encryption: Service mesh (Istio, Linkerd) or application-level TLS. Not enforced by the platform.

GKE

• Secrets encryption: Application-layer secrets encryption with Cloud KMS. Same envelope encryption model.
• Storage encryption: Persistent Disks are encrypted by default with Google-managed or customer-managed keys (CMEK).
• In-transit encryption: GKE encrypts all intra-node traffic by default using WireGuard (Dataplane V2). Pod-to-pod traffic within the cluster is encrypted without a service mesh.

The intra-node encryption by default in GKE is a significant differentiator for PCI DSS. With EKS, you need a service mesh or mTLS at the application layer to achieve the same. GKE does it at the network layer transparently.

Verdict: encryption

GKE wins on default encryption posture. Intra-cluster traffic encryption without a service mesh simplifies your architecture and reduces the attack surface you need to document for auditors.

Operational complexity and managed features

EKS: more control, more responsibility

• Control plane: Managed by AWS. You manage the data plane (node groups, networking, add-ons).
• Upgrades: You schedule and execute upgrades. AWS provides managed node group rolling updates, but you own the timing and testing.
• Add-ons: CoreDNS, kube-proxy, VPC CNI - managed as EKS add-ons but you choose versions and update schedules.
• Logging: You configure Fluent Bit or CloudWatch agent to ship logs to CloudWatch or your SIEM.

EKS gives you flexibility at the cost of operational decisions. A well-staffed platform team will appreciate the control. A lean team may find it burdensome.

GKE Autopilot: less control, less burden

• Control plane + data plane: Both managed. You define workloads, Google manages nodes, scaling, security patching, and OS updates.
• Upgrades: Automatic. Release channels (Rapid, Regular, Stable) let you choose the aggressiveness. Google handles the rollout.
• Built-in security: Shielded GKE nodes, Secure Boot, Container-Optimized OS, automatic node repair.
• Logging: Cloud Logging is integrated by default. No agent configuration needed.

GKE Autopilot is particularly attractive for fintech startups that want to focus engineering effort on the payment product rather than Kubernetes operations. The trade-off: less customisation of node configuration, no DaemonSets (on Autopilot), and some workload restrictions.

For teams that need full node control (custom kernel modules, GPU workloads, specific machine types), GKE Standard offers the same level of control as EKS.

Verdict: operations

GKE Autopilot is the better choice for lean fintech teams (5-20 engineers). EKS is better for organisations with a dedicated platform engineering team (20+ engineers) that values control and has existing AWS operational expertise.

Cost comparison for a typical fintech cluster

A representative payment processing cluster: 3 node pools, 12-20 nodes, running a mix of API gateways, payment orchestration services, fraud detection, and batch reconciliation jobs.

The cost differential is not dramatic. GKE tends to be 5-10% cheaper on compute, slightly more expensive on storage. Egress costs are nearly identical. The real cost difference is operational: how many engineers do you need to keep the cluster healthy and compliant?

For a fintech running PCI DSS workloads, the hidden cost is compliance evidence generation. GKE’s built-in defaults (encrypted intra-node traffic, Binary Authorization, VPC Service Controls) reduce the effort to produce evidence during annual QSA assessments.

When to choose EKS for payment processing

• Your organisation is already on AWS with mature VPC architecture and IAM governance
• Your payment processors and banking partners offer AWS PrivateLink connectivity
• You have a platform engineering team experienced with EKS operations
• Your QSA auditor is familiar with AWS Security Groups and CloudTrail
• You need specific node configurations (custom AMIs, GPU for fraud ML models, Graviton for cost optimisation)

When to choose GKE for payment processing

• You are building greenfield and want maximum security defaults out of the box
• Your team is lean (5-15 engineers) and you cannot dedicate headcount to K8s operations
• You value intra-cluster encryption without managing a service mesh
• You process data that requires exfiltration prevention (VPC Service Controls)
• You use BigQuery for financial analytics and want native GCP integration
• Your ML/fraud detection team uses Vertex AI or BigQuery ML

Our experience

At Devopsity, we have built and operated payment processing infrastructure on both platforms. Our GCP fintech projects typically involve:

• GKE clusters with Workload Identity and Binary Authorization
• Cloud KMS with customer-managed keys for cardholder data encryption
• VPC Service Controls perimeters around PCI-scoped resources
• Cloud Build pipelines with vulnerability scanning gates
• BigQuery-based transaction analytics with column-level access controls

On the AWS side, we operate EKS clusters for payment platforms with:

• IRSA-based pod identity with fine-grained KMS access
• Security Groups for Pods for network segmentation
• AWS Config rules for continuous compliance monitoring
• CodePipeline with ECR image scanning

Both approaches pass PCI DSS audits. The choice comes down to your team’s expertise, your existing cloud investments, and how much operational burden you want to absorb.

Building payment infrastructure on Kubernetes?

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Frequently asked questions

Is GKE or EKS better for PCI DSS compliance?

Both can achieve PCI DSS compliance. GKE has more security features enabled by default (intra-node encryption, Dataplane V2, VPC Service Controls), which reduces the effort to generate compliance evidence. EKS gives you more control but requires explicit configuration of each security layer.

Can I run PCI DSS workloads on GKE Autopilot?

Yes. GKE Autopilot provides a hardened environment with Shielded Nodes, Secure Boot, and automatic patching. However, you cannot run DaemonSets or privileged containers, which may limit some monitoring and security agent deployments. Verify that your runtime security tooling (Falco, Sysdig) supports Autopilot before committing.

How much does a PCI-compliant Kubernetes cluster cost?

For a typical mid-size payment platform (12-20 nodes), expect $3,000-$5,000/month in infrastructure costs on either platform. The real cost difference is engineering time: GKE typically requires 0.5-1 fewer FTE for cluster operations compared to EKS for the same compliance posture.

What about Azure AKS for payment processing?

AKS is viable, particularly for organisations in the Microsoft ecosystem. It sits between EKS and GKE in terms of managed features - free control plane, good default security, but lacking equivalents to VPC Service Controls and Dataplane V2’s built-in encryption. We cover AKS in a separate comparison.

Do I need a service mesh for PCI DSS on Kubernetes?

On EKS: likely yes, for mTLS between services handling card data. On GKE: not necessarily - Dataplane V2 provides encryption at the network layer. A service mesh still adds value for observability and traffic management, but it is not strictly required for encryption compliance on GKE.