Cloud & Infrastructure
Kubernetes for platforms that must scale without drama
Declarative orchestration for containerised workloads: self-healing deployments, autoscaling and zero-downtime releases governed by version-controlled configuration.
0 downtime
Rolling and canary release strategy
Auto
Horizontal scaling on CPU, memory or queue depth
-30%
Infrastructure spend after right-sizing and bin-packing
Self-heal
Failed pods replaced without human intervention
Definition
What Kubernetes is
Kubernetes schedules and manages containers across a cluster of machines. You declare desired state — replicas, resources, networking — and the platform continuously reconciles reality to match it.
When to use it
Where Kubernetes is the right call
Recognise your situation before committing engineering budget.
Capabilities
How we work with Kubernetes
What is actually delivered, beyond the logo on a stack slide.
Cluster architecture
Node pools, namespaces, quotas and network policies designed per environment and tenant.
GitOps delivery
Declarative manifests or Helm charts reconciled from Git with full audit history.
Observability
Metrics, logs, traces and alerting with SLOs defined per service.
Resilience
Probes, pod disruption budgets, backups and tested disaster-recovery runbooks.
Strengths
- Handles scaling, healing and rollout mechanics for you
- Cloud-neutral — the same manifests run across providers
- Strong isolation and policy controls for multi-tenant platforms
- Rich ecosystem for ingress, secrets, service mesh and autoscaling
Trade-offs to plan for
- Real operational complexity — overkill for a single small service
- Needs disciplined cost governance or spend drifts upward
- Requires on-call maturity and runbooks, not just a cluster
Use cases
Kubernetes in production
Representative systems we build and support on this technology.
Multi-tenant SaaS
Namespace-isolated tenants with per-tenant resource limits and metering.
Seasonal retail load
Autoscaling for campaign peaks, scaling down again without manual work.
Regulated workloads
Private clusters with strict network policy and audited configuration changes.
Comparison
Kubernetes vs managed PaaS vs single-VM Docker
| Dimension | Kubernetes | Managed PaaS | Single-VM Docker |
|---|---|---|---|
| Scaling control | Fine-grained | Automatic, limited | Manual |
| Operational overhead | High | Low | Low |
| Portability | Excellent | Vendor-bound | Good |
| Best fit | Many services, real scale | Small teams, standard apps | Single service |
Engineering practices
Non-negotiables on every engagement
Decision guide
Which direction fits your situation?
Match your constraints to the recommended approach.
| If this sounds like you | We recommend |
|---|---|
| One or two services, steady traffic | Managed PaaS or Docker on a VM — skip Kubernetes |
| Many services and variable load | Managed Kubernetes with GitOps delivery |
| Strict data residency and isolation | Private cluster with network policy and audited access |
Delivery
Services built on Kubernetes
Pairs well with
Industries running this
FAQ
Kubernetes questions, answered
Related technologies
What we usually pair with it
Docker
Reproducible application images that remove "works on my machine" from the vocabulary and make deployments boring in the best possible way.
Python
The default language for AI, data and automation work — with a mature ecosystem for LLMs, document processing, forecasting and scientific computing.
Node.js
An event-driven runtime that handles thousands of concurrent I/O-bound requests efficiently — ideal for API gateways, integrations and real-time features.
Planning a build or migration on Kubernetes?
Share your requirements and current systems. We will return an architecture recommendation, trade-offs and a phased plan — under NDA.