It's not 100% free, but with my 1 node setup, you can pay as low as ~$6USD/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!).
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.
- 1 free zonal GKE cluster
- e2-medium @ $27USD/mth (or $8USD/mth for preemptible)
- $0.10/hr per EKS cluster @ 730hrs/mth (or $73USD/mth)
- t3.medium @ $29USD/mth (Spot instances available at up to 90% savings)
- free cluster management
- B2S @ $34USD/mth (Spot instances available at up to 90% savings)
Azure's AKS combined with Spot instances are actually incredibly competitive in pricing vs preemptibles, but in my mind, preemptibles have the edge due to ease of use -- no price bidding and a generably more reliable/predictable uptime (in my use don't think I've had any node get terminated before 22hrs).
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.
You can do this via console or...
gcloud services enable --async \
container.googleapis.com- You will need to set an environment variable to provide credentials to Terraform in order to deploy these blueprints (typically one of
GOOGLE_CREDENTIALS,GOOGLE_APPLICATION_CREDENTIALSorGOOGLE_OAUTH_ACCESS_TOKEN) - While
e2-microis a viable option formachine_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 ane2-small(2CPUs/2GB memory) - Leaving
release_channelasUNSPECIFIEDmeans 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 - 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
- If
confidential_nodes_enabledis set to true, themachine_typeneeds to be from the N2D family where the smallest node size starts atn2d-standard-2(2CPUs/8GB memory) and it must also NOT be a preemptible node (which effectively nullifies one of the cost-saving components of this free-tier GKE)
I've been learning a lot about eBPF and experimenting with Cilium in particular. New in v0.4.0, you will have the option of enabling GKE Dataplane V2 which leverages the power of eBPF and Cilium to provide enhanced security and observability in your GKE cluster.
When Dataplane V2 is enabled, one of the things you may notice is the absence of kube-proxy in the cluster. That's becuase it has been replaced by Cilium CNI! It replaces iptables as component that controls connections between pods (and between nodes). Iptables is an old-school (albeit, extensive and powerful) program that allows the configuration of (mainly static) IP packet filter rules in a Linux kernel firewall and was never meant for something as dynamic as a Kubernetes environment. The sheer number of iptables rules in very large clusters makes scaling difficult and hence a kube-proxy replacement such as Cilium would be very welcomed in such a scenario.
If you would like to learn more about Cilium and how to get started, I wrote a short Medium article about it here.
As of v0.8.0, you will have the option of provisioning a private GKE nodes. Doing so will also provision a Cloud NAT router in order for your nodes to get internet -- but this, of course will incur extra costs.
If you decide to go the full private GKE cluster route (private GKE endpoint/control-plane AND private GKE nodes) then it will provision an additional /29 subnet that will house a VM running tinyproxy that will act as a forwarding proxy to the private GKE endpoint.
See this Medium article if you want to see how the network traffic flows in this setup.
To use the IAP tunnel, your user needs to have the IAP-secured Tunnel User (roles/iap.tunnelResourceAccessor) -- even if you're the Owner of the project, you will need to add this role!!
You will need to create an IAP tunnel from your local machine/laptop to the IAP proxy VM (command will be in the Terraform output) and you will also have to export HTTPS_PROXY=localhost:8888 (just remember to unset the env var when you're done). Alternatively you can set an alias which prepends the env var (e.g. alias k='HTTPS_PROXY=localhost:8888 kubectl ').