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Container orchestration

Kubernetes reconciliation for workloads that need a platform

Model desired state, scheduling, and health probes deliberately—accept operational complexity only when the workload justifies it.

Primary intent: Container orchestration, workload scheduling and platform operations

DesiredManifests
RunPods
ExposeService/Ingress
OpsProbes / scale

Workload Reconciliation Loop

Choose rollout, scaling, or recovery to see conceptual reconciliation—no fake cluster telemetry.

Choose rollout, scaling, or recovery to see conceptual reconciliation—no fake cluster telemetry.

Static view: desired state, control plane, deployment, pods, service/ingress, probes, and autoscaling/observability form the loop.

Rollout

Progress desired replica state safely.

  1. Desired state
  2. Deployment
  3. Pods
  4. Probes
  5. Service

Responsibilities

  • Readiness gates
  • Rollback criteria
  • Change owner

Tradeoff: Skipping probes hides broken releases longer.

Rollout layer flowDesired stateDeploymentPodsProbesService

Problems this stack addresses

Kubernetes orchestrates containers. Docker packages them. Kubernetes is not required for every application, and autoscaling does not guarantee cost efficiency.

  • Many services needing shared scheduling and networking
  • Controlled rollouts with health-gated progression
  • Platform operations across multiple environments

What we build with Kubernetes

Illustrative delivery shapes—not a guaranteed catalog.

  • Workload platforms

    Deployments with services and ingress boundaries.

  • Rollout strategies

    Health probes and progressive delivery patterns.

  • Ops baselines

    Observability, quotas, and upgrade ownership.

Architecture and deployment model

Platform role

  • Orchestration and scheduling platform
  • Desired-state reconciliation loop
  • Not a replacement for application architecture

Architecture layers

  • Control plane and desired state
  • Nodes and scheduled pods
  • Services and ingress
  • Config, secrets, and autoscaling

Workload reconciliation loop

Desired state → scheduler/control plane → deployment → pods → service/ingress → probes → autoscaling/observability.

Operational complexity

Upgrades, networking, and RBAC require dedicated ownership—clusters are products themselves.

Docker relationship

Images remain the package unit; Kubernetes decides where and how many run.

Security and shared responsibility

Shared responsibility

  • RBAC and network policy are customer-owned
  • Secrets management needs a deliberate strategy
  • Supply chain of images remains critical

Identity and access

  • Least-privilege service accounts
  • Separate human cluster-admin access
  • Audit API access

Networking

  • Ingress TLS ownership
  • Network policies where multi-tenant risk exists
  • DNS and service discovery conventions

Reliability, scaling and operations

Observability

  • Metrics for cluster and workloads
  • Log aggregation with retention
  • Trace context across services

Reliability / recovery

  • Pod disruption budgets for critical services
  • Backup strategies for cluster state and app data separately
  • No zero-downtime guarantee wording

Scaling

  • HPA/VPA only with meaningful metrics
  • Node capacity planning remains required
  • Autoscaling ≠ automatic cost savings

Delivery, automation and cost governance

Delivery workflow

  • GitOps or CI-applied manifests with review
  • Image tags pinned for production
  • Canary/rollout owners defined

Automation / IaC

  • Cluster provisioning often via Terraform/cloud
  • Workload manifests versioned separately
  • Helm/Kustomize still need review discipline

Cost / governance

  • Idle clusters waste money quickly
  • Request/limit hygiene reduces waste
  • Multi-cluster only when isolation needs justify it

Integration patterns

  • Docker images
  • AWS / Azure / Google Cloud managed Kubernetes
  • GitHub Actions deploys
  • PostgreSQL/Redis as external data services

When to choose / when not to choose

Choose when

  • Many services need shared orchestration
  • You can staff platform operations
  • Rollout and scheduling complexity is justified

Reconsider when

  • A single service fits simpler compute
  • Team cannot own upgrades and networking
  • Managed app platforms already meet needs

Tradeoffs

  • Power vs operational burden
  • Portability vs cloud-managed specifics
  • Autoscaling complexity vs predictable capacity

Migration / modernization notes

  • Containerize and observe before full orchestration
  • Migrate one domain at a time
  • Keep rollback images and manifests ready

Proof and capability boundary

Capability-level orchestration guidance. No claim Kubernetes is required for every app or that autoscaling guarantees savings.

No zero-downtime guarantees; no “required for every app”; no fake telemetry.

Is Kubernetes required for every application?

No. Many workloads run well on simpler compute or managed app platforms. Use Kubernetes when orchestration complexity is justified.

Does autoscaling guarantee lower cost?

No. Autoscaling can reduce waste or increase spend depending on signals, limits, and idle capacity.

Discuss Kubernetes platforms

Share service count and ops staffing—we will assess whether orchestration is justified.

Begin stack consultation