GKE consulting for teams on Google Cloud.
Senior help with Google Kubernetes Engine, from choosing Autopilot or Standard to VPC-native cluster design, migration, autoscaling and cost control. We build clusters your team can own, secured with Workload Identity and delivered as infrastructure-as-code with GitOps, so deploys stay boring and the bill stays defensible.
Built for scale-ups and established teams on Google Cloud.
GKE consulting earns its place when Google Kubernetes Engine is either already central to your platform or about to be, and you want it designed properly rather than accreted by trial and error. These are the situations where senior help pays for itself, whether you are a fast-growing product company or an established business modernising its platform.
Standing up GKE from scratch
You are committing to Google Cloud and want VPC-native clusters, Workload Identity, node pools and release channels designed right the first time, not retrofitted.
Autopilot versus Standard
You cannot decide between Autopilot and Standard, or you are on one and suspect the other fits better. You want a reasoned recommendation per workload.
Inherited a messy cluster
An existing GKE estate grew organically: flat networking, over-privileged pods, ballooning node cost. You want it hardened and right-sized without a rebuild.
Migrating onto GKE
You are moving from self-managed Kubernetes, EKS, ECS or virtual machines and want the landing zone and cutover planned by people who have done it before.
Preparing for AI and GPU workloads
You are heading towards model serving and GPU scheduling on GKE, and want the cluster and node strategy ready before the AI roadmap lands on it.
Reliability and cost under pressure
The GKE bill and the incident count are both climbing. You want autoscaling, observability and Spot capacity tuned so the platform is defensible on both.
Recommendations follow your workloads, not a partner quota.
Google Kubernetes Engine gives you a lot of managed surface, and the hard part is deciding which of it to lean on. We start from your workloads and constraints, choose Autopilot or Standard on the merits, and design the cluster so security and cost are built in from the first namespace rather than bolted on after launch. Every recommendation is defensible, and everything lands in your repositories as code.
Principles we hold to on every GKE engagement.
- Autopilot or Standard is chosen per cluster on the merits, and many estates run both rather than forcing one model everywhere.
- Clusters are VPC-native and private by default, with Workload Identity so pods hold no static keys, and authorised networks limiting the control plane.
- Everything is code in your repositories: Terraform or OpenTofu for clusters, and Argo CD or Flux for delivery, reviewed like any other change.
- Cost is a design input, not an afterthought: right-sized requests, Spot Pods for fault-tolerant work, and node auto-provisioning tuned to real demand.
- Your engineers work alongside us the whole way, so the cluster is understood and owned, not just delivered.
What GKE consulting covers.
Cluster design
VPC-native clusters, Workload Identity, node pool strategy and release channels, with Autopilot or Standard chosen per workload and defined as code.
Autoscaling
Cluster Autoscaler, node auto-provisioning and the Horizontal Pod Autoscaler, with right-sized requests so scaling is predictable and cost-aware.
Networking & ingress
Gateway API and GKE Ingress, network policy for pod-to-pod isolation, and private cluster connectivity designed for your traffic.
GitOps delivery
Argo CD or Flux, Helm or Kustomize, and CI wiring so deploys to GKE become boring, auditable and self-service for your teams.
Observability
Cloud Operations or Managed Service for Prometheus, Grafana and OpenTelemetry for metrics, logs and traces, with SLOs and alerts wired in.
Security & cost
CIS GKE benchmark hardening, Binary Authorization, network policy and least-privilege, plus Spot Pods and right-sizing to keep spend defensible.
GKE consulting sits within our broader Kubernetes consulting practice, and often follows a Kubernetes migration or a wider GCP cloud transformation. Once the cluster is live, teams build self-service on top with platform engineering.
Our GKE consulting process.
Four phases, planned backwards from a cluster your team can run. The architecture review confirms exact scope and the Autopilot-or-Standard decision before anything is built.
Assess
Workloads, traffic, statefulness, GPU needs and compliance constraints, ending in an Autopilot-versus-Standard recommendation and a cluster design.
Build foundations
VPC-native clusters, Workload Identity, node pools, networking and secrets stood up as Terraform or OpenTofu, with GitOps wired in.
Deploy & harden
Workloads onto GKE with autoscaling, Gateway API, observability and CIS-aligned security including Binary Authorization and network policy.
Operate & own
Handover with runbooks and GitOps, or an agreed managed operating model, with cost dashboards so the platform stays defensible.
The GKE building blocks we actually use.
No reseller agreements and no partner quota. Recommendations follow your workloads. Typical building blocks on a Google Kubernetes Engine engagement:
Cluster modes
GKE Autopilot for hands-off, managed nodes, and Standard where custom node pools, GPUs or node-level agents demand more control. Often both.
Cluster foundations
VPC-native networking, Workload Identity, private clusters with authorised networks, node pools and release channels for controlled upgrades.
Infrastructure as code
Terraform or OpenTofu for projects, networking and clusters, with modular, reviewed, version-controlled definitions in your repositories.
Autoscaling
Cluster Autoscaler, node auto-provisioning and the Horizontal Pod Autoscaler, plus Spot Pods for fault-tolerant and batch workloads.
Networking & delivery
Gateway API and GKE Ingress for traffic, Argo CD or Flux for GitOps, and Helm or Kustomize for packaging and progressive rollouts.
Observability & security
Cloud Operations or Managed Prometheus, Grafana and OpenTelemetry, with network policy, Binary Authorization and CIS GKE benchmark hardening.
GKE consulting FAQ.
What does GKE consulting include?
GKE consulting covers the full lifecycle of running workloads on Google Kubernetes Engine: choosing between Autopilot and Standard, designing VPC-native clusters with Workload Identity, node pools and release channels, wiring autoscaling and Gateway API networking, and building GitOps delivery with Argo CD or Flux. We also handle migration onto GKE, observability with Cloud Operations or Managed Prometheus, security hardening against the CIS GKE benchmark, and cost control. Everything ships as Terraform or OpenTofu and GitOps definitions in your own repositories.
Should we use GKE Autopilot or Standard?
Autopilot is the default we reach for when a team wants Google to manage nodes, capacity and much of the security posture, and is happy to run within its supported surface. Standard is the right choice when you need custom node pools, GPUs or specialised hardware, DaemonSets and node-level agents, or fine-grained control over machine types and kernel settings. Many estates run both: Autopilot for stateless application workloads and Standard for specialised or GPU-bound ones. We assess your workloads and recommend per cluster rather than applying a blanket rule.
How do you secure a GKE cluster?
Security starts with Workload Identity so pods assume least-privilege Google service accounts instead of holding static keys. We add Kubernetes network policy to restrict pod-to-pod traffic, private clusters with authorised networks to limit the control-plane surface, and Binary Authorization so only signed, attested images can be deployed. On top of that we align the cluster to the CIS GKE benchmark, enforce RBAC, manage secrets properly, and scan images in the pipeline. Every control is defined as code and reviewed like any other change.
Can you migrate us to GKE from another platform?
Yes. Common starting points are self-managed Kubernetes, Amazon EKS or ECS, virtual machines on GCE or elsewhere, and legacy PaaS. We inventory the workloads, containerise anything that is not already, and move them onto GKE in planned waves behind a blue-green or canary strategy, with a tested rollback at each step. Stateful services and data stores are handled explicitly rather than lifted blindly, so most services move with no user-visible downtime.
How do you control GKE costs?
Cost control on GKE comes from right-sizing requests and limits so nodes are not paying for headroom you never use, running fault-tolerant and batch workloads on Spot Pods, and letting node auto-provisioning and the Cluster Autoscaler scale capacity to real demand. On Autopilot you pay for requested pod resources, which makes disciplined requests the main lever. We wire cost and utilisation dashboards so the platform stays defensible over time. We do not resell compute or take a margin on your bill.
Building on Google Kubernetes Engine?
Start with the readiness scorecard, or book a free 30-minute architecture call. A senior engineer reviews your workloads and returns a clear GKE design with an Autopilot-versus-Standard recommendation.