Capability · Kubernetes & platform

Kubernetes & platform engineering

Container platforms that scale under real load — and stay affordable.

Assess platform readiness or view the fixed-scope entry

120,000+

concurrent-user load-test capacity

42%

faster average API response

65%

fewer manual production interventions

99.97%

peak-event availability

Measured on one engagement — CricRadio, verified with the owner.

Sound familiar?

Two or more of these means this page is for you.

1Latency climbs the moment traffic spikes — and stays high until someone intervenes
2The cluster bill grows faster than traffic does
3Scaling still means resizing nodes by hand, usually mid-incident
4One noisy workload starves the others sharing its node
5Every team deploys to Kubernetes a slightly different way
6Nobody can say what a given pod is actually allowed to consume

The transformation

How this discipline behaves when it's done right

The target is a platform where capacity follows demand automatically and predictably: workloads separated by responsibility, horizontal autoscaling calibrated against real load rather than defaults, node autoscaling and bin-packing to keep utilisation honest, health and disruption policies treated as production requirements, and a paved path so product teams ship without re-inventing deployment each time.

1
Workload separation
Split high-demand APIs from background and low-volume services so each scales on its own signal instead of dragging the others with it.
2
Autoscaling calibration
Configure horizontal scaling on CPU, memory and selected application metrics, with thresholds tuned through staged load tests — not defaults.
3
Node efficiency
Node autoscaling, right-sized requests and limits, and bin-packing keep utilisation high and the bill proportional to traffic.
4
Production-readiness
Health probes, resource governance, disruption budgets and rolling deployment with rollback — made mandatory, not optional configuration.
5
Paved path
A standard golden deployment workflow so product teams ship to the platform safely without bespoke pipelines.

Decisions

The calls we make — and why

Kubernetes, or just bigger machines?

Bigger machines add capacity but can't let components scale independently. Kubernetes gives workload-level elasticity, standardised deployment and automatic replacement of unhealthy services — the leverage is in the operating model, not the box size.

How aggressive should autoscaling be?

Calibrated, not maximal. Aggressive default scaling amplifies abnormal behaviour and cost; we tune thresholds against measured load so capacity tracks real demand.

Where does cost control live?

In the platform, not a spreadsheet. Right-sized requests, node autoscaling and bin-packing keep utilisation honest continuously, so spend stays proportional to traffic.

Artifacts

What you hold at the end

Report
Production-readiness assessment with prioritised remediation
Code
Autoscaling and resource-governance configuration
Path
Golden deployment path for product teams
Dashboard
Latency, saturation, scaling and cost dashboards
Runbook
Scaling and recovery runbooks

Evidence

What it did on a real system

Situation

A real-time platform with sharp, event-driven traffic spikes around live moments.

Intervention

Workload separation on Kubernetes, calibrated autoscaling and a cache layer protecting the database.

Measured result

Validated through a 120,000+ concurrent-user load scenario; average API response improved 42% and manual production intervention fell 65%.

Verified with the engagement owner · CricRadio.

Read the full engagement

Start here

Most start as a fixed-scope Kubernetes Production-Readiness Review or a platform build, then continue as managed operations where we run and improve the cluster as an extension of your team.

View scope: Kubernetes Production-Readiness Review

Delivery & ongoing

GKE, EKS and AKS architecture
Autoscaling and resource governance
Bin-packing and cost-aware scheduling
Internal developer platforms and golden paths

Delivered as code with handover — or run ongoing as managed operations.

Before you engage

Do we have to re-architect our applications?

Usually not. Most workloads gain the operational benefits of containers and autoscaling without redesigning business logic; we sequence the few that genuinely need state externalised.

Which cloud's Kubernetes?

We work across GKE, EKS and AKS. The operating model — separation, calibrated scaling, governance, a paved path — is the same; the managed-service specifics differ and we handle them.

Can you cut our Kubernetes bill without hurting reliability?

Yes. Most savings come from right-sized requests, node autoscaling and bin-packing, which raise utilisation without touching the headroom real demand needs.

Not in scope