package/gke-platform-engineering
GKE Platform & Scalability Engineering
Anyone can stand up a cluster. The question is whether it holds at four times current traffic, and the only honest way to answer that is to make it happen on purpose.
~$26,000
Starts with Discovery & Scoping
Floors reflect typical engagements. Larger, regulated, or multi-region estates are scoped and quoted after Discovery.
Where this usually starts
The cluster works. It has worked for a year. Then a marketing event doubles traffic for ninety minutes and the platform degrades in a way nobody predicted — not a clean failure, but rising latency, a queue building somewhere, and an autoscaler that added nodes eleven minutes after they would have helped.
Afterwards, the postmortem is unsatisfying. The autoscaler was configured. The pods had resource requests. Monitoring was on. Everything was in place and it still did not hold, because every individual piece was set to a default that was reasonable in isolation and wrong in combination.
What is usually missing is not a component. It is evidence. Nobody has ever driven the platform to the point of failure deliberately, so nobody knows where that point is, and every capacity conversation is a negotiation between guesses.
Load testing against a real SLO is the centre of this engagement
Most GKE consulting sells cluster setup. Cluster setup is the part your team can do, and increasingly the part that Autopilot does. The part that is hard to do internally is proving the platform holds at peak, because it requires defining what "holds" means, building a load profile that resembles real traffic, and having somewhere safe to break things.
It starts with a latency SLO expressed as a number: a percentile, a threshold, and a traffic level. Not "the site should be fast". Something like 95th percentile under 400 milliseconds at 3,000 requests per second. Without that, a load test produces a graph and no conclusion.
Then the platform gets driven to that level and past it. What comes back is a curve — where latency starts to degrade, what degrades first, and how far the headroom actually extends. That curve is the deliverable that changes capacity planning from argument to arithmetic.
Cluster and node pool architecture
- Node pool structure by workload class, so that a batch job cannot starve a latency-sensitive service, and so that machine types match what the workload actually consumes rather than what was picked at cluster creation.
- Regional versus zonal placement, and what each choice costs in both availability and inter-zone egress.
- Autopilot or Standard, decided on the workload rather than on preference. Autopilot removes real operational burden and takes away control that some workloads need.
- Workload Identity for pod-level authentication to Google Cloud services, replacing any remaining service account keys.
Autoscaling that reacts in time
Horizontal Pod Autoscaler configuration tuned on the metric that actually predicts saturation for your workload, which is often not CPU. Queue depth, in-flight requests, or a custom application metric usually correlates better, and reacting to the right signal is what turns an autoscaler from a lagging indicator into a working control.
Cluster autoscaler behaviour, node provisioning time, and the gap between "more pods are needed" and "a node exists to run them on". Where that gap is too long for the traffic pattern, over-provisioned placeholder capacity is configured to absorb the spike while real nodes come up.
Pod disruption budgets and graceful termination, so that scaling down and node upgrades do not produce the incident that scaling up was meant to prevent.
Quotas, limits, and observability
Resource requests and limits set from measured consumption rather than from copied defaults. Requests that are too high waste the majority of your cluster; requests that are too low make scheduling unpredictable and put every noisy neighbour in the same blast radius.
Namespace quotas and limit ranges so that one team cannot consume the cluster. Google Cloud project quotas reviewed too, because a scaling event that hits a CPU quota fails in a way that looks like a cluster problem and is not.
Prometheus and Grafana deployed with dashboards built around the SLO rather than around whatever the default dashboard shows. The load test output becomes the baseline those dashboards are read against.
Cost-aware node strategy
Machine type selection, Spot nodes for workloads that tolerate interruption, and bin-packing efficiency — how much of the CPU and memory you pay for is actually requested by running pods.
This is cost awareness inside the platform design, not a cost engagement. A full commitment and discount analysis across your Google Cloud spend is the Cost Optimization package.
Excluded, and where that work lives
Application containerization is out of scope. Getting an application into a container image that behaves correctly is application engineering, and it is work best done by the team that owns the code.
Service mesh is out of scope. Meshes solve real problems and introduce a substantial operational surface; adding one inside a platform engagement that is also changing autoscaling and node architecture makes it impossible to attribute a regression to a cause.
CI/CD pipeline authoring is out of scope here, and it is delivered by the Automation, IaC & DevSecOps package.
How the engagement runs
- Week 0 Discovery & Scoping Current cluster architecture, workload profile, traffic pattern, and what the business considers a failure. Produces the fixed quote.
- Week 1 SLO definition and baseline Latency SLO agreed as a number with your team. Current-state measurement taken so that later results have something to compare against.
- Week 2–3 Architecture and autoscaling Node pool structure, scaling metrics, requests and limits from measured consumption, disruption budgets, quotas.
- Week 4 Observability Prometheus and Grafana deployed, dashboards built around the SLO, alerting on leading indicators rather than on outage.
- Week 5 Load testing Load profile built from real traffic shape. Platform driven to the SLO threshold and beyond. Failure modes recorded in order of appearance.
- Week 6 Tuning and handover Findings from the load test applied, test re-run to confirm, and the results handed over as a documented headroom curve.
These figures are starting points, not quotes. Final pricing depends on the size and complexity of your estate and is fixed in writing at the end of Discovery & Scoping.
What this engagement does not cover
Named here rather than discovered later. This is the list that makes the fixed price hold when scope starts moving.
- application containerization
- service mesh
- CI/CD pipeline authoring
What this is based on
- Document AI pipeline operated on GKE at high page volume.
- Google Cloud Professional Cloud Architect and Professional Cloud Developer.
- Platform work delivered on foundations sized for estates in the thousands of VMs.
What buyers ask
We use Autopilot. Is this still relevant?
Yes, and the emphasis shifts. Autopilot removes node pool management, which is a genuine reduction in work, and it does not set your SLO, tune your scaling metric, size your requests, or tell you where your headroom ends.
On Autopilot the requests-and-limits work matters more, not less, because requests drive billing directly.
Can you load test against production?
Against a production-representative environment, yes. Against live production, only where you already run that practice and have the safeguards for it — that is a decision about your risk appetite, not one a consultant should make on your behalf.
Most engagements build a test environment that matches production topology at reduced node count and scale the results, which is less precise than testing production and considerably less exciting.
What if the load test shows we cannot hit the SLO?
That is a useful outcome, and it is more common than it sounds. The result is a curve showing where degradation starts and what degrades first, which usually points at one or two specific constraints rather than at a general shortfall.
Some of those constraints are platform-side and get fixed within the engagement. Some are in the application — a connection pool ceiling, a synchronous call to a slow dependency — and those are handed over as findings, because application changes are outside this scope.
Why exclude service mesh?
Because installing one during a platform engagement makes every subsequent measurement ambiguous. If latency changes after a mesh goes in alongside new autoscaling configuration and new resource limits, attribution is guesswork.
A mesh is a reasonable thing to adopt. It should be its own change, measured against the baseline this engagement establishes.
Questions about pricing, terms, and ownership across every engagement are on the FAQ.
Start with Discovery & Scoping
$4,500 fixed, 3–5 days. A current-state review, a gap analysis, a written scope, and a fixed quote for this engagement. Half the fee is credited against the work if you proceed within 60 days.