The situation
What needs investigation
A team is considering Kubernetes, or already has a cluster that is becoming difficult to operate. The starting point is not the cluster configuration. It is the workload: deployment frequency, service count, scaling profile, stateful dependencies, team ownership, and what must be operated during an upgrade or incident.
Signals we look for
Workloads
- stateless and stateful boundaries
- resource requests and limits
- deployment and readiness behaviour
Cluster
- node groups and failure domains
- version and upgrade path
- namespace and tenancy boundaries
Traffic
- ingress and TLS termination
- service discovery
- autoscaling signals
Operations
- metrics and logs
- RBAC and admission controls
- on-call ownership
How we investigate
Each step is validated against the workload before the next change.
- 01
Map workloads
Identify which applications need orchestration and which do not.
- 02
Define boundaries
Set namespace, node, network, and access boundaries before deployment.
- 03
Size resources
Test requests, limits, probes, and autoscaling against actual load.
- 04
Design ingress
Review traffic routing, certificates, health checks, and failure handling.
- 05
Plan upgrades
Define cluster, add-on, and workload upgrade ownership.
- 06
Validate operations
Exercise deployment, scaling, and incident paths before production reliance.
Architecture / decision path
Example engineering decision
Illustrative platform decision — not customer data
Workloadsthree APIs and one scheduled job
Release frequencyweekly
Platform teamno dedicated cluster operator
Engineering interpretation
Kubernetes may be appropriate, but the cluster operating overhead must be justified against simpler managed runtime options. The review compares the application requirements with the ownership model, not only feature availability.
Possible next investigation
- Review ECS or managed compute alternatives.
- Define upgrade and node-patching ownership.
- Test resource limits with representative traffic.
- Document ingress and secrets boundaries.
Trade-offs we consider
Control
Kubernetes exposes useful controls but creates more operational surface.
Portability
Portable APIs do not remove cloud networking and identity decisions.
Scaling
Autoscaling depends on useful workload signals and available nodes.
Ownership
Cluster upgrades and add-ons need a named operating owner.
What the engagement can produce
Typical outputs for this type of work
Scope depends on the environment and the problem being investigated.
- workload and cluster boundary design
- resource request and limit guidance
- ingress and traffic plan
- upgrade and operational ownership plan
- observability baseline
- implementation backlog