Platform engineering is no longer optional—it’s essential for any team operating Kubernetes at scale. The goal is not to wrap Kubernetes in marketing, but to build a platform that reduces cognitive load, enforces consistency, and lets developers focus on business logic rather than infra plumbing. In this post, I’ll share hardened lessons and recommended practices for using Kubernetes as your platform foundation.
Platform Engineering, Revisited
At its core, platform engineering is the discipline of building and operating an internal platform that development teams consume, rather than configure themselves. It’s about:
- Performing infrastructure, deployment, security, and observability work once and doing it well
- Enabling self-service for developers while safeguarding system reliability
- Maintaining clear boundaries of ownership between platform team vs. product teams
In practice, a good platform balances flexibility and control. If it’s too rigid, teams will bypass it; too loose, and you’ll end up with fragmentation.
Why Kubernetes as the Platform Foundation
Kubernetes isn’t perfect, but it's a powerful substrate. Its declarative APIs, scheduling, self-healing, and ecosystem are building blocks you can lean on rather than reinvent. Key reasons to use Kubernetes as the heart of your platform:
- Declarative control plane — Helps enforce consistency across environments
- Automated lifecycle management — Rollouts, rollbacks, scaling all baked in
- Extensibility — Custom controllers and CRDs let you extend behavior (e.g. operators)
- Rich ecosystem integration — Observability, policy, service mesh, and infra tooling all plug in
But with power comes complexity. Managing Kubernetes itself becomes a non-trivial challenge at scale. Platform engineering helps by abstracting that complexity behind domain-specific APIs.
Tooling & Patterns You Should Leverage
Below are core tools and patterns I consistently see in well-run platform stacks. You don’t need all of them—pick what's right for your domain.
- kubectl / Kubernetes API — Obvious, but don’t forget: everything on the platform should still be accessible via APIs and CLIs.
- Helm (or Kustomize) — For packaging and deploying applications. Helps teams avoid writing raw manifest sets.
- Operators / Custom Controllers — Good for domain-specific automation (e.g. database lifecycle, backup, scaling).
- GitOps (ArgoCD / FluxCD) — Makes your platform auditable, declarative, and self-healing by reconciling desired state from Git.
- Crossplane / Infrastructure as Code (IaC) — Let your platform control cloud/infrastructure resources using Kubernetes constructs.
- Observability stack — Prometheus + Grafana for metrics, ELK or Loki for logging, Jaeger or OpenTelemetry for tracing.
- Policy Enforcement tools — Kyverno or OPA/Gatekeeper to codify guardrails across namespaces, CRDs, etc.
- Cost / Resource controls — Enforce quotas, limits, and track cost attribution tools like Kubecost to avoid runaway resource consumption.
When building your stack, aim for incremental adoption—start with a few core patterns, validate them, then expand.
Observability That Doesn’t Leave You Blind
A platform is only as good as its visibility. Without metrics, logs, traces, and alerts, you’re flying blind:
- Collect metrics (Prometheus), visualize them (Grafana)
- Centralize logs (ELK / Loki) for fast troubleshooting
- Trace cross-service calls (Jaeger / OpenTelemetry) to find latency hotspots
- Define alerts with clear ownership and SLOs
- Use dashboards focused on platform health (e.g. cluster saturation, API errors) so you can catch issues before devs see them
If your platform can’t answer, “Why is my deployment slow?” or “Which namespace is saturating resources?”, it’s not mature yet.
Security: Guardrails, Not Barriers
Platform teams must balance convenience and safety. Some practices I strongly recommend:
- RBAC: Minimal scopes. Avoid “cluster-admin by convenience.”
- NetworkPolicies: Isolate workloads at namespace or service level.
- Secrets: Use encryption at rest, and consider external secret stores (Vault, External Secrets) for sensitive keys.
- Policies as Code: Use Kyverno or OPA/Gatekeeper to enforce naming standards, image scanning, resource constraints, etc.
- Admission Controls: Validate or mutate workloads at submit time—block anything that violates your platform rules.
These security measures ensure teams can move fast without opening dangerous holes.
Reality Checks & Trade-Offs
A few things you’ll run into—acknowledging them early helps:
- The platform team becomes a bottleneck if overloaded. Keep APIs lean and monitor support load.
- Divergent team needs will push for bypasses. You need flexibility, but with constraints.
- Cost drift is real. Kubernetes makes it easy to overprovision. Use quotas, show cost insights, and enforce limits.
- Skill gaps hurt adoption. The platform team needs deep Kubernetes expertise; invest in training.
- Upgrades and compatibility are non-trivial. CRDs, APIs, dependencies shift over Kubernetes versions, so you must manage version compatibility.
These challenges are among the most cited obstacles to operating production-grade Kubernetes platforms.
If you’re building a platform on Kubernetes, don’t try to bake all features at once. Start with the core abstractions that give the most leverage—self-service deployment, guardrails, observability, and resource control. Evolve based on feedback and usage. The platform doesn’t exist to lock teams in, but to keep them productive and safe. Do that well, and you’ll earn trust, not bureaucracy.