Free-tier GKE Cluster

GKE Cluster

GKE Container Node Pool

It's not 100% free, but with my 1 node setup, I'm paying ~$5USD/mth for a fully managed Kubernetes cluster. This works by taking advantage of Google always free tier which waives the management fee of one zonal GKE cluster, so you only have to pay for your nodes. Combine this with using preemptible VMs as your nodes and you'll have some spectacular savings.

This is great if you're looking for a small k8s cluster that more closely resembles what you might see in the real world (not that Minikube or MicroK8s isn't good as a learning tool -- it's just not the same). Here, you can also scale in/out your cluster easily if you want test some features or add-ons (like service meshes!).

GKE vs EKS vs AKS

I'm going to use a single node (2CPUs/4GB memory) Kubernetes cluster as the basis for comparison between the 3 major cloud providers. The math is shown below, but it doesn't take an extreme couponer to figure out which is the best deal.

GKE

• 1 free zonal GKE cluster
• e2-medium @ $27USD/mth (or $8USD/mth for preemptible)

EKS

• $0.10/hr per EKS cluster @ 730hrs/mth (or $73USD/mth)
• t3.medium @ $29USD/mth

AKS

• free cluster management
• B2S @ $34USD/mth

IMPORTANT

The key to getting the savings here is to limit the amount of nodes in your cluster (until you need it). The 3 key settings to ensure this is location, node_locations and node_count (or initial_node_count).

location specifies where to place the cluster (masters). By specifying a zone, you have a free, zonal cluster. If you replaced it with a region instead, it becomes a regional cluster -- ideal for a production cluster, but not part of the free tier offering.

Leaving node_locations blank will default your node to be in the same zone as your GKE cluster's zone. Any zone you specify will be in addition to the the cluster's zone (i.e. node_locations = ["northamerica-northeast1-a",]), meaning your nodes will span more than one zone. This is referred to as a multi-zone cluster.

node_count specifies how many nodes per zone rather than the total node count in your cluster. Therefore, if you set 3 zones in node_locations with a node_count of 2, you're going to have 6 nodes in total.

Additional Notes

• While e2-micro is a viable option for machine_type, in practice it's not very useful as all the overhead that comes with GKE such as Stackdriver agent, kube-dns, kube-proxy, etc. consumes most of availble memory. I recommend starting with at least an e2-small (2CPUs/2GB memory)
• Leaving release_channel as UNSPECIFIED means that you will perform upgrades manually, where as if you subscribed to a channel, you will the get the regular updates that gets released to that channel
• At time of writing, enabling Istio on the GKE cluster is a Beta feature and thus I have specified provider = google-beta in the google_container_cluster resource block
• Enabling Istio will add an NLB to the deployment which will increase your costs so unless you want to do something with the service mesh, I recommend leaving it disabled to save yourself some money :)
• Depending on your workload/application that you're running, you definitely could run most (or all) of it on a preemptible node pool in GCP, but if you're going to run production, please provision a regional cluster rather than cheap out for the free zonal one
• If you deployed a private cluster, some of your k8s deployments may fail due to your pods not having outbound access to the public Internet...having said that, some of the more common images like the nginx one that I used in my examples folder may still work because you're pulling from a Docker Hub cache. Ideally, you should be pulling images from your private GCR in this case

Example terraform.tfvars

project_id            = "my-project"
credentials_file_path = "/path/to/my/credentials.json"
region                = "northamerica-northeast1"
zone                  = "northamerica-northeast1-c"

channel      = "REGULAR"
auto_upgrade = "true"

gke_cluster_name        = "playground"
enable_private_endpoint = "false"
master_ipv4_cidr_block  = "172.16.0.0/28"
#istio_disabled   = "false"

machine_type = "e2-small"
disk_size_gb = "40"
max_nodes    = "1"

Example Kubernetes Deployment

I've included an example deployment of nginx with LoadBalancer (GCP ALB) service. Please note that the deployment does provision an GCP load balancer so this will incur extra charges if you leave it running for too long.

To deploy: kubectl apply -f examples/nginx-deployment.yaml

To delete: kubectl delete -f examples/nginx-deployment.yaml

The pods should deploy fairly quickly, but the service might take a bit before you get the load balancer's public IP (you can do a watch kubectl get service if you're the impatient type)

(There are also other examples in there if you want try them out as well)