Platform lab
Runtime for applications, workflows, and experimentation.
AI engineering · Kubernetes · GitOps
Platform lab
A polished, security-conscious view of my private Kubernetes platform lab — built like a compact platform for application workloads, experimentation, automation, operational feedback, and production-style GitOps delivery. The public page keeps sensitive operational details out of scope so the story can focus on the engineering model.
PlatformKubernetes
DeliveryGitOps
SecuritySealed secrets
SignalsOperational feedback
Platform LabK8s
EdgeControlled access layer
DeliveryGitOps
DataVolumes
SignalsObservability
platform status
desired_state: reconciled
secrets: encrypted
edge_policy: controlledDeclarative
Infrastructure and workloads are managed as reviewed code.
Observable
Operational signals support maintenance and reliability.
Architecture
A Kubernetes lab with an engineering aesthetic
This Kubernetes platform is built for learning, operating, and deploying real services. The design shows how compute, access, storage, delivery, and feedback loops work together as a compact engineering system.
Platform architectureSources → Edge / GitOps → Kubernetes → Workloads
GitOps, access, storage, and signals working together
Sources
Internet trafficRequests
GitHub sourceDesired state
Secure edge
IngressControlled routing
Access controls
Route policies
Kubernetes platform
K8sControl plane
GitOps
Secrets
Access
Storage
Signals
Data ops
Experiments
Apps
Metrics
Automation
State + signals
VolumesPersistent data
ObservabilityOperational feedback
Public entry Platform core State and signals
◈
Cluster foundation
Compute fabric
- Compact machines provide the container runtime layer.
- Workloads are scheduled by Kubernetes with a maintainable topology.
- Capacity planning keeps the cluster balanced for lab workloads.
⌁
Controlled access layer
Secure edge
- Services that require external access pass through a controlled access layer.
- Routing rules separate public entry points from internal services.
- The edge layer keeps ingress, certificates, and routing manageable.
◎
Persistent workloads
State layer
- Applications that need data use persistent volumes.
- Storage is declared and maintained with the rest of the platform.
- Data services are treated as first-class platform components.
GitOps mindset
The operating model looks like a production delivery loop
Design
Change is modeled as code.
Validate
Automation checks manifests before deployment.
Reconcile
GitOps applies the approved desired state.
Observe
Metrics, logs, and alerts close the feedback loop.
Repository changes become reviewed infrastructure changes. Pull requests, validation, and reconciliation make the platform workflow traceable from intent to runtime state.
Platform capabilities
Core services presented as clean building blocks
GitOps controller
Reconciles desired state from the repository.
Secret encryption
Keeps sensitive values encrypted before commit.
Access automation
Supports secure external-access workflows.
Traffic management
Controls how approved services are reached.
Storage controller
Provides persistent volumes for stateful workloads.
Database operator
Manages application databases declaratively.
Operational telemetry
Collects health and reliability signals.
Upgrade automation
Helps keep platform components maintained.
External access
Controlled external access for services that need to be reached
The platform uses a controlled access layer for services that require external reachability. Routing, certificates, and access policy are treated as part of the platform rather than ad-hoc service setup.
- Controlled routing
- Secure access patterns
- Certificate automation
- Route policies
Workloads
Applications, telemetry workflows, automation, and experimentation share one runtime
Workload class
Experimentation workloads
A runtime for testing services, workflows, and technical ideas.
- Designed like a compact platform engineering lab.
- Useful for testing deployment, monitoring, and reliability patterns.
- Configuration is managed through the same GitOps workflow.
Workload class
Application workloads
Custom applications deployed through the same GitOps platform.
- Built and released through a repeatable delivery workflow.
- Managed with environment-specific Kubernetes manifests.
- Versioned images and manifests move together through review.
Workload class
Telemetry workflows
Insight workflows for owned projects and operations.
- Combines application signals with platform-level operational feedback.
- Supports reporting, debugging, and operational awareness.
- Dashboards turn telemetry into practical maintenance context.
Workload class
Automation services
Services for delivery, maintenance, and support workflows.
- Includes supporting APIs and utility services.
- Access is limited to the required network paths.
- Implementation choices stay aligned with the platform operating model.
Observability
Operational signals support a platform-style workflow
Health signals and feedback loops
Detect issues quickly
Signals help understand workload health and guide operational decisions.
Traceable changes
Commits and pull requests document what changed and why.
Security
Public presentation with careful operational boundaries
The public version keeps credentials, internal network details, hostnames, and environment-specific values out of scope while still showing the engineering story and platform operating model.
No plaintext secrets in Git
Selected services exposed through a controlled edge
Configuration changes reviewed before deployment
Operational signals for reliability awareness
Quality gates
Validation keeps changes consistent before deployment
- YAML linting
- Kubernetes schema checks
- Manifest build checks
- Pull request validation
Automated checks reduce the risk of broken manifests reaching the platform and keep the GitOps workflow tidy.