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Kubernetes Distributions - When to Use Which K8s Flavor

The question that's been asked in every architecture meeting I've been in: "Which Kubernetes should we use?" Spoiler: there's no one-size-fits-all answer, and choosing wrong can cost you months of painful migration.

Here's the thing about Kubernetes distributions—they all run containers, they all use kubectl, and they all look pretty similar in demos. But pick the wrong one, and you'll find yourself fighting against your infrastructure instead of building on top of it.

I've deployed K8s in enough different contexts (from Raspberry Pis to enterprise data centers) to know that the distribution you choose matters more than most architecture decisions. Let me save you from the mistakes I've made.

Bottom line: K3s for edge/IoT, Minikube for learning, EKS/GKE/AKS for cloud production, OpenShift when compliance teams are involved, and kubeadm when you need full control. The rest of this article explains why.

The Distribution Problem Nobody Talks About

Most Kubernetes guides act like vanilla K8s is a thing you just "install." It's not. Even the official install docs immediately punt you to kubeadm, kops, or a cloud provider.

Why? Because Kubernetes is deliberately incomplete. It's like buying a car engine—it'll power your vehicle, but you still need to figure out the transmission, brakes, steering, and everything else.

Every K8s cluster needs:

  • A container runtime (Docker? containerd? CRI-O?)
  • A networking plugin (Calico? Flannel? Cilium?)
  • Storage drivers (what happens when a pod asks for disk?)
  • Ingress controllers (how do requests get into your cluster?)
  • Certificate management, logging, monitoring...

Distributions are opinionated bundles that make these choices for you. The trick is picking one whose opinions match your constraints.

For the Edge: K3s

Remember when "running Kubernetes on a Raspberry Pi" sounded like a joke? K3s made it real.

I've run K3s on everything from $35 Pi boards to industrial IoT gateways, and honestly, it's the most "it just works" Kubernetes I've used. The entire thing is a single 60MB binary. No complex setup, no dependency hell, just a curl command and you're done.

When K3s Clicks

We deployed K3s to 50+ retail locations where the "server" was literally a mini PC behind the register. Each location runs its own cluster because network connectivity to HQ is unreliable. K3s uses SQLite instead of etcd by default, so there's no distributed database to babysit.

Installation literally looked like this:

curl -sfL https://get.k3s.io | sh -
# Wait 30 seconds
kubectl get nodes  # It just works

The built-in Traefik ingress and metrics server mean you don't need to set up a bunch of add-ons before you can actually run something useful. For edge deployments where you can't afford to have a platform team managing each location, this is gold.

When to Skip K3s

Don't use K3s for your main production cluster. I've seen teams try to "start simple" with K3s in the cloud, only to hit weird API compatibility issues down the line. It strips out some less-common K8s features to stay small—usually not a problem, until it is.

Also, if you need enterprise support contracts (looking at you, banks and healthcare), Rancher sells support for K3s but you might have an easier time with OpenShift or EKS.

MicroK8s

Canonical's low-ops, minimal production Kubernetes distribution.

Key Features

  • Snap-based: Easy installation via snap package manager
  • Add-ons system: Enable features on-demand (DNS, dashboard, storage, etc.)
  • Multi-node clustering: Simple cluster formation with microk8s add-node
  • Automatic updates: Tracks upstream Kubernetes releases
  • Strict confinement: Enhanced security through snap isolation

Best Use Cases

  • Local development: Quick K8s setup on workstations
  • Ubuntu environments: Native integration with Ubuntu/Canonical ecosystem
  • Small production clusters: Simpler than full Kubernetes deployment
  • Teaching and learning: Easy to set up and tear down
  • CI/CD testing: Disposable test clusters

When NOT to Use MicroK8s

  • Non-Ubuntu/Debian systems (limited support)
  • Large enterprise deployments
  • Environments where snap is unavailable or restricted
  • Organizations requiring commercial support
# Install MicroK8s
sudo snap install microk8s --classic

# Enable add-ons
microk8s enable dns dashboard storage

# Add node to cluster
microk8s add-node

# Access kubectl
microk8s kubectl get pods

kind (Kubernetes IN Docker)

Kubernetes clusters running in Docker containers, primarily for testing.

Key Features

  • Docker-based: Runs Kubernetes nodes as Docker containers
  • Multi-node support: Local multi-node clusters for testing
  • Fast startup: Cluster creation in seconds
  • CI/CD friendly: Originally designed for Kubernetes conformance tests
  • Configuration files: YAML-based cluster configuration

Best Use Cases

  • Kubernetes testing: Testing K8s features and versions
  • CI/CD pipelines: Automated testing with disposable clusters
  • Local development: Quick cluster creation and destruction
  • Multi-node testing: Testing distributed applications locally
  • Kubernetes contribution: Testing changes to Kubernetes itself

When NOT to Use kind

  • Production deployments (not designed for production)
  • Long-running development environments
  • Systems without Docker
  • Resource-constrained machines
# Install kind
go install sigs.k8s.io/kind@latest

# Create cluster
kind create cluster --name test-cluster

# Create multi-node cluster
cat <

Local Development Distributions

Minikube

The original local Kubernetes solution for learning and development.

Key Features

  • Multiple drivers: VirtualBox, Docker, Hyper-V, KVM, etc.
  • Add-ons ecosystem: Rich set of optional components
  • Multiple clusters: Run multiple clusters simultaneously
  • LoadBalancer support: Local LoadBalancer implementation
  • Cross-platform: Windows, macOS, and Linux support

Best Use Cases

  • Learning Kubernetes: Most documentation uses Minikube examples
  • Local development: Full-featured local K8s environment
  • Testing integrations: Testing with various Kubernetes versions
  • Add-on testing: Experimenting with different K8s components
  • Cross-platform development: Consistent experience across operating systems

When NOT to Use Minikube

  • Production environments
  • CI/CD pipelines (kind or K3s are faster)
  • Multi-node production-like testing
  • Resource-constrained systems
# Install Minikube
curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
sudo install minikube-linux-amd64 /usr/local/bin/minikube

# Start cluster
minikube start

# Enable add-ons
minikube addons enable ingress
minikube addons enable metrics-server

# Access dashboard
minikube dashboard

# Stop cluster
minikube stop

Docker Desktop

Built-in Kubernetes support in Docker Desktop.

Key Features

  • Integrated: One-click enable in Docker Desktop settings
  • Docker integration: Seamless Docker and Kubernetes workflow
  • Automatic updates: Updated with Docker Desktop
  • LoadBalancer support: localhost LoadBalancer for services
  • Context switching: Easy switching between clusters

Best Use Cases

  • Docker users: Already using Docker Desktop
  • Simple development: Basic Kubernetes development needs
  • Windows/Mac development: Native OS integration
  • Beginners: Easiest setup for new Kubernetes users

When NOT to Use Docker Desktop

  • Linux systems (limited Docker Desktop support)
  • Advanced Kubernetes features
  • Multi-node testing
  • Production-like environments

Managed Cloud Distributions

Amazon EKS (Elastic Kubernetes Service)

AWS's managed Kubernetes service.

Key Features

  • Managed control plane: AWS manages master nodes
  • AWS integration: IAM, VPC, ELB, EBS, CloudWatch integration
  • Fargate support: Serverless container execution
  • Multiple AMIs: Optimized, Bottlerocket, custom options
  • Add-ons: VPC CNI, CoreDNS, kube-proxy managed

Best Use Cases

  • AWS-centric organizations: Heavy AWS service usage
  • Enterprise production: Need for high availability and SLA
  • Compliance requirements: SOC, PCI, HIPAA certifications
  • Hybrid workloads: Mix of serverless (Fargate) and traditional
  • Multi-region deployments: Global application distribution

When NOT to Use EKS

  • Small projects (cost-prohibitive)
  • Multi-cloud strategy (vendor lock-in risk)
  • On-premises requirements
  • Very cost-sensitive projects (control plane costs add up)
# Create EKS cluster with eksctl
eksctl create cluster \
  --name production-cluster \
  --region us-west-2 \
  --nodegroup-name standard-workers \
  --node-type t3.medium \
  --nodes 3

# Update kubeconfig
aws eks update-kubeconfig --region us-west-2 --name production-cluster

Google GKE (Google Kubernetes Engine)

Google Cloud's managed Kubernetes service.

Key Features

  • Google expertise: From the creators of Kubernetes
  • Autopilot mode: Fully managed nodes and infrastructure
  • Fast updates: Quickest Kubernetes version updates
  • GCE integration: Deep integration with Google Cloud services
  • Binary authorization: Deploy-time security policy enforcement

Best Use Cases

  • Google Cloud users: GCP-centric infrastructure
  • Cutting-edge K8s: Latest Kubernetes features first
  • Hands-off operations: Autopilot for minimal management
  • Data analytics: Integration with BigQuery, Dataflow
  • Machine learning: AI Platform integration

When NOT to Use GKE

  • Non-GCP environments
  • Organizations with AWS/Azure commitments
  • On-premises requirements
  • Highly customized cluster configurations (Autopilot limitations)
# Create GKE cluster
gcloud container clusters create production-cluster \
  --zone us-central1-a \
  --num-nodes 3 \
  --machine-type n1-standard-2

# Create Autopilot cluster
gcloud container clusters create-auto autopilot-cluster \
  --region us-central1

# Get credentials
gcloud container clusters get-credentials production-cluster --zone us-central1-a

Azure AKS (Azure Kubernetes Service)

Microsoft Azure's managed Kubernetes service.

Key Features

  • Free control plane: No cost for Kubernetes masters
  • Azure integration: Active Directory, Key Vault, Monitor integration
  • Virtual nodes: ACI (Azure Container Instances) integration
  • Windows support: Windows Server containers alongside Linux
  • Dev Spaces: Fast iterative development and debugging

Best Use Cases

  • Microsoft ecosystem: Heavy Azure and Microsoft tool usage
  • Windows containers: Need for Windows workloads
  • Enterprise integration: Active Directory authentication
  • Cost-conscious: Free control plane attractive
  • Hybrid scenarios: Azure Arc integration

When NOT to Use AKS

  • Non-Azure environments
  • Organizations avoiding Microsoft ecosystem
  • Multi-cloud requirements
  • On-premises only deployments
# Create AKS cluster
az aks create \
  --resource-group myResourceGroup \
  --name myAKSCluster \
  --node-count 3 \
  --enable-addons monitoring \
  --generate-ssh-keys

# Get credentials
az aks get-credentials --resource-group myResourceGroup --name myAKSCluster

Enterprise Distributions

Red Hat OpenShift

Enterprise Kubernetes platform with developer and operational tools.

Key Features

  • Integrated CI/CD: Built-in Jenkins, Tekton pipelines
  • Developer console: User-friendly web interface
  • Source-to-Image (S2I): Build container images from source code
  • Enterprise support: Commercial support from Red Hat
  • Security: SELinux, RBAC, network policies by default
  • OperatorHub: Extensive operator ecosystem

Best Use Cases

  • Enterprise organizations: Need for vendor support and stability
  • Regulated industries: Banking, healthcare, government
  • Red Hat ecosystem: RHEL-based infrastructure
  • Developer platforms: Building internal PaaS
  • Hybrid cloud: Consistent platform across environments

When NOT to Use OpenShift

  • Cost-sensitive projects (licensing costs)
  • Simple Kubernetes needs (too heavy)
  • Organizations wanting pure upstream K8s
  • Small teams without enterprise requirements

Rancher

Multi-cluster Kubernetes management platform.

Key Features

  • Cluster management: Manage multiple Kubernetes clusters
  • Multi-cloud: Works with any Kubernetes distribution
  • User management: Centralized authentication and authorization
  • App catalog: Helm chart repository and management
  • Monitoring: Built-in Prometheus and Grafana

Best Use Cases

  • Multi-cluster management: Organizations with many clusters
  • Multi-cloud strategy: Kubernetes across different providers
  • Edge deployments: Managing distributed edge clusters
  • Team collaboration: Multiple teams sharing infrastructure
  • Kubernetes as a Service: Providing K8s to internal teams

When NOT to Use Rancher

  • Single cluster deployments
  • Cloud-managed K8s with native tools (EKS/GKE/AKS)
  • Very small operations teams
  • Organizations wanting minimal abstractions

VMware Tanzu

Enterprise-grade Kubernetes platform from VMware.

Key Features

  • vSphere integration: Deep VMware infrastructure integration
  • Application catalog: Curated and supported applications
  • Mission Control: Centralized multi-cluster management
  • Service Mesh: Built-in service mesh capabilities
  • Enterprise support: VMware support and SLAs

Best Use Cases

  • VMware shops: Existing VMware infrastructure
  • Enterprise applications: Traditional enterprise workloads
  • On-premises: Private data center deployments
  • Hybrid cloud: VMware Cloud on AWS, Azure VMware Solution
  • Developer productivity: Building internal platforms

When NOT to Use Tanzu

  • Non-VMware environments
  • Cloud-native startups
  • Cost-sensitive projects
  • Organizations preferring upstream Kubernetes

DIY Production Distributions

kubeadm

The official Kubernetes cluster creation tool.

Key Features

  • Official tool: Maintained by Kubernetes project
  • Best practices: Follows upstream Kubernetes recommendations
  • Flexibility: Customize every aspect of the cluster
  • Pure Kubernetes: No vendor-specific modifications
  • Upgrade path: Clear process for version upgrades

Best Use Cases

  • On-premises production: Full control over infrastructure
  • Custom requirements: Specific networking, storage, or security needs
  • Learning deep K8s: Understanding Kubernetes internals
  • Bare-metal deployments: Physical servers or VMs
  • Compliance: Specific security or compliance requirements

When NOT to Use kubeadm

  • Managed cloud environments (use EKS/GKE/AKS)
  • Limited operational expertise
  • Need for commercial support
  • Rapid deployment requirements
# Initialize control plane
sudo kubeadm init --pod-network-cidr=10.244.0.0/16

# Set up kubectl
mkdir -p $HOME/.kube
sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config

# Install network add-on (Calico example)
kubectl apply -f https://docs.projectcalico.org/manifests/calico.yaml

# Join worker nodes
sudo kubeadm join <control-plane-ip>:6443 --token <token> \
  --discovery-token-ca-cert-hash sha256:<hash>

Kubespray

Ansible-based Kubernetes cluster deployment.

Key Features

  • Ansible automation: Declarative cluster configuration
  • Production-ready: Battle-tested configurations
  • High availability: Multi-master setup support
  • Multiple CNIs: Calico, Cilium, Flannel, Weave support
  • Upgrades: Automated cluster upgrades

Best Use Cases

  • Ansible users: Teams familiar with Ansible
  • Complex deployments: Multi-master, multi-datacenter setups
  • Bare-metal: Physical infrastructure deployments
  • Automated operations: GitOps-style cluster management
  • Custom networking: Specific CNI or network requirements

When NOT to Use Kubespray

  • Cloud-managed Kubernetes (overkill)
  • Teams without Ansible experience
  • Simple single-node deployments
  • Need for commercial support

Specialized Distributions

k0s

Zero-friction Kubernetes distribution by Mirantis.

Key Features

  • Single binary: All-in-one distribution
  • Zero dependencies: No external dependencies required
  • Modular: Use only what you need
  • Controller-worker separation: Clean architecture
  • Autopilot: Automated cluster operations

Best Use Cases

  • Edge computing: Simplified edge deployments
  • Embedded systems: Appliances and IoT gateways
  • Easy operations: Reduced operational complexity
  • Quick provisioning: Rapid cluster creation

RKE2 (Rancher Kubernetes Engine 2)

Security-focused Kubernetes distribution.

Key Features

  • Security hardened: CIS benchmark compliance by default
  • FIPS 140-2: Federal compliance support
  • Embedded etcd: Simplified cluster setup
  • SELinux support: Enhanced security on RHEL/CentOS
  • Air-gap support: Offline installation support

Best Use Cases

  • Government: Federal and DoD compliance
  • Regulated industries: Banking, healthcare
  • Security-critical: High security requirements
  • Air-gapped: Disconnected environments

Decision Framework

By Environment

Cloud Production

  • AWS: EKS (managed) or kops (self-managed)
  • Google Cloud: GKE (Autopilot for simplicity, Standard for control)
  • Azure: AKS (especially for Windows workloads)
  • Multi-cloud: Rancher + multiple cloud K8s, or DIY with Kubespray

On-Premises Production

  • Enterprise with support needs: OpenShift, Tanzu
  • Full control: kubeadm, Kubespray
  • VMware infrastructure: Tanzu
  • Multi-cluster management: Rancher

Edge/IoT

  • Resource-constrained: K3s
  • ARM devices: K3s, MicroK8s
  • Simplified operations: k0s
  • Security focus: RKE2

Local Development

  • Learning: Minikube
  • Docker users: Docker Desktop
  • Ubuntu: MicroK8s
  • CI/CD: kind
  • Resource-limited: K3s

By Team Size

Individual / Small Teams (1-5 people)

  • Cloud: Managed K8s (EKS/GKE/AKS)
  • On-premises: K3s, MicroK8s
  • Development: Minikube, Docker Desktop

Medium Teams (5-20 people)

  • Cloud: Managed K8s with good tooling
  • On-premises: Rancher + RKE2, or managed OpenShift
  • Development: Kind for CI/CD, Minikube for local

Large Organizations (20+ people)

  • Cloud: Managed K8s with Rancher for multi-cluster
  • On-premises: OpenShift, Tanzu, or Rancher + custom
  • Multi-cluster: Rancher, OpenShift, or Tanzu Mission Control

By Expertise Level

Beginners

  • Learning: Minikube, Docker Desktop
  • Production: Managed cloud K8s (EKS/GKE/AKS)

Intermediate

  • Development: Kind, K3s
  • Production: Managed K8s or OpenShift

Advanced

  • Custom needs: kubeadm, Kubespray
  • Multi-cluster: Rancher, custom tooling
  • Any distribution: Can handle complexity

By Budget

Minimal Budget

  • On-premises: K3s, MicroK8s, kubeadm
  • Cloud: Consider control plane costs (AKS free control plane)

Moderate Budget

  • Cloud: Managed K8s (EKS/GKE/AKS)
  • On-premises: Community OpenShift (OKD), Rancher

Enterprise Budget

  • With support: OpenShift, Tanzu, Rancher Enterprise
  • Cloud: Managed K8s with premium support
  • Consulting: Custom solutions with vendor support

Comparison Matrix

Distribution Complexity Resource Usage Production Ready Support Available Best For
K3s Low Very Low Yes Community Edge, IoT, Small Clusters
MicroK8s Low Low Yes Commercial (Canonical) Ubuntu, Development
Minikube Low Medium No Community Learning, Local Dev
kind Low Low No Community CI/CD, Testing
Docker Desktop Very Low Medium No Community Beginners, Simple Dev
EKS Medium N/A (Managed) Yes Commercial (AWS) AWS Production
GKE Low-Medium N/A (Managed) Yes Commercial (Google) GCP Production
AKS Medium N/A (Managed) Yes Commercial (Microsoft) Azure Production
OpenShift High High Yes Commercial (Red Hat) Enterprise, Regulated
Rancher Medium Medium Yes Commercial (SUSE) Multi-cluster Management
Tanzu High High Yes Commercial (VMware) VMware Shops
kubeadm High Medium Yes Community Custom Production
Kubespray High Medium Yes Community Automated Deployments
k0s Low Low Yes Commercial (Mirantis) Simplified Operations
RKE2 Medium Medium Yes Commercial (Rancher) Security-Critical

Migration Considerations

From Minikube to Production

  • Next step: Managed cloud K8s (EKS/GKE/AKS) for simplicity
  • Challenges: LoadBalancer differences, storage classes, networking
  • Timeline: 1-2 weeks for basic workload migration

From Docker Swarm to Kubernetes

  • Best approach: Start with managed K8s or OpenShift (similar abstractions)
  • Challenges: Different deployment models, learning curve
  • Timeline: 1-3 months depending on complexity

From One Cloud to Another

  • Path: Use infrastructure-as-code (Terraform, Pulumi)
  • Considerations: Cloud-specific features, storage migration
  • Timeline: 2-4 weeks with good planning

From Self-Managed to Managed

  • Benefits: Reduced operational burden, better SLAs
  • Trade-offs: Less control, potential cost increase
  • Process: Parallel run, gradual migration

Common Pitfalls

Choosing Too Early

  • Problem: Selecting distribution before understanding requirements
  • Solution: Start with managed K8s or Minikube, evaluate later

Over-Engineering

  • Problem: Choosing OpenShift for a 3-person startup
  • Solution: Match complexity to team size and needs

Under-Estimating Operations

  • Problem: Choosing kubeadm without operational expertise
  • Solution: Be honest about team capabilities, prefer managed

Ignoring Costs

  • Problem: EKS control plane costs add up across many clusters
  • Solution: Calculate total cost of ownership, not just compute

Vendor Lock-In

  • Problem: Heavy use of cloud-specific Kubernetes features
  • Solution: Use standard K8s APIs, abstract cloud-specific parts

Serverless Kubernetes

  • AWS Fargate: Serverless pods on EKS
  • GKE Autopilot: Fully managed node infrastructure
  • Azure Container Apps: Serverless containers with K8s-like APIs

GitOps-Native Distributions

  • Distributions with built-in GitOps workflows
  • Declarative cluster management
  • Automated reconciliation and drift detection

WASM Support

  • Running WebAssembly workloads alongside containers
  • Lighter weight than containers
  • Better startup times and security

AI/ML Optimized

  • GPU scheduling and management
  • Model serving frameworks
  • Integration with ML platforms

Key Takeaways

Choosing Wisely

  1. Start simple: Use managed K8s or lightweight distributions initially
  2. Match to team: Distribution complexity should match team expertise
  3. Consider costs: Look at total cost of ownership, not just compute
  4. Evaluate support: Determine if commercial support is necessary
  5. Plan for change: Your needs will evolve, plan for migration

Golden Rules

  • Learning: Minikube or Docker Desktop
  • Cloud production: Use managed K8s (EKS/GKE/AKS)
  • Edge/IoT: K3s is the clear winner
  • Enterprise: OpenShift or Tanzu if budget allows
  • Multi-cluster: Rancher for management layer
  • Custom needs: kubeadm or Kubespray with expertise

Success Factors

  • Team skills: Most important factor in choice
  • Support needs: Commercial support worth the cost for critical systems
  • Ecosystem fit: Choose what integrates with your existing tools
  • Community: Active community means better resources and faster help
  • Future-proofing: Stick to standard Kubernetes APIs when possible

What I've Learned From Picking Wrong

I've been part of three painful Kubernetes migration projects. Two of them happened because we chose the wrong distribution upfront.

The first time, we went with kubeadm because "we want full control." What we actually got was six months of our team learning the hard way why managed services exist. Debugging certificate rotation at 2 AM builds character, but it doesn't ship features.

The second time, we picked EKS but then realized we needed the same setup to work on-premises for compliance reasons. Cue another six months migrating to OpenShift, rewriting all our AWS-specific integrations.

Here's what I wish someone had told me:

Start with Managed (Unless You Can't)

If you're on AWS, use EKS. On GCP, use GKE. On Azure, use AKS. Don't overthink it.

The time you save not managing control planes, etcd, and certificate rotation is time spent building actual products. Yes, you'll pay for the control plane (~$70/month), but compare that to the cost of hiring people to manage vanilla K8s.

The exception: you need on-premises, you have regulatory requirements that prevent cloud, or you're at sufficient scale where the math favors self-managed.

Learn on Minikube, Not Production

Every week I see developers trying to learn Kubernetes by deploying to a "dev" EKS cluster. Don't do this. Spin up Minikube on your laptop, break things locally, learn the basics where mistakes are free.

Once you understand pods, services, deployments, and ingress—then start thinking about production infrastructure.

Match the Distribution to Your Constraints, Not Your Wishes

Want to run cutting-edge K8s features? GKE ships the latest versions fastest.

Stuck with VMware everywhere? Tanzu is actually pretty good if you're already in that ecosystem.

Security/compliance people breathing down your neck? OpenShift or RKE2.

Three-person startup? Managed K8s or don't use Kubernetes at all.

The Decision Tree I Actually Use

When someone asks me which Kubernetes to use, here's my flowchart:

  1. Are you learning? → Minikube
  2. Is this for edge/IoT? → K3s
  3. Are you on AWS/GCP/Azure and can use their services? → EKS/GKE/AKS
  4. Do you need FedRAMP/FIPS/government compliance? → OpenShift or RKE2
  5. Are you managing 10+ clusters across clouds? → Rancher + whatever K8s fits each cloud
  6. Do you have very specific requirements and a team who can handle it? → kubeadm
  7. Everything else? → Start with managed, reassess in 6 months

Most importantly: the distribution matters less than you think, and more than you hope. Pick one that fits your constraints, get something running, and iterate. A "wrong" choice that ships is better than analysis paralysis.

The real expensive mistake isn't picking the "suboptimal" distribution—it's spending three months in architecture meetings trying to predict the future.

TL;DR: Use managed K8s in the cloud, K3s for edge, Minikube for learning. Ignore anyone who says you need to "start simple" with kubeadm—that's the opposite of simple. Make a decision, ship something, and adjust when you actually hit limitations instead of imagined ones.