I'm trying to run open-source with minimal costs on the cloud and would love to run it on k8s without the hassle of managing it (managed k8s cluster). Is there a free tier option for a small-scale project in any cloud provider?
If there is one, which parameters should I choose to get the free tier?
You can use IBM cloud which provides a single worker node Kubernetes cluster along with container registry like other cloud providers. This is more than enough for a beginner to try the concepts of Kubernetes.
You can also use Tryk8s which provides a playground for trying Kubernetes for free. Play with Kubernetes is a labs site provided by Docker and created by Tutorius. Play with Kubernetes is a playground which allows users to run K8s clusters in a matter of seconds. It gives the experience of having a free Alpine Linux Virtual Machine in the browser. Under the hood Docker-in-Docker (DinD) is used to give the effect of multiple VMs/PCs.
If you want to use more services and resources, based on your use case you can try other cloud providers, they may not provide an indefinitely free trial but have no restriction on the resources.
For Example, Google Kubernetes engine(GKE) provides $300 credit to fully explore and conduct an assessment of Google Cloud. You won’t be charged until you upgrade which can be used for a 3 month period from the account creation. There is no restriction on the resources and the number of nodes for creating a cluster. You can add Istio and Try Cloud Run (Knative) also.
Refer Free Kubernetes which Lists the free Trials/Credit for Managed Kubernetes Services.
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I am now running two kubernetes clusters.
First Cluster is running on bare metal, and Second Cluster is running on EKS.
but since maintaining EKS costs a lot, so I am finding ways to change this service as Single Cluster that autoscales on AWS.
I did tried to consider several solutions such as RHACM, Rancher and Anthos.
But those solutions are for controlling multi cluster.
I just want to change this cluster as "onpremise based cluster that autoscales (on AWS) when lack of resources"
I could find "EKS anywhere" solution but since price is too high, I want to build similar architecture.
need advice for any use cases for ingress controller, or (physical) loadbalancer, or other architecture that could satisfies those conditions
Cluster API is probably what you need. It is a concept of creating Clusters with Machine objects. These Machine objects are then provisioned using a Provider. This provider can be Bare Metal Operator provider for your bare metal nodes and Cluster API Provider AWS for your AWS nodes. All resting in a single cluster (see the docs below for many other provider types).
You will run a local Kubernetes cluster which will have the Cluster API running in it. This will include components that will allow you to be able to create different Machine objects and tell Kubernetes also how to provision those machines.
Here is some more reading:
Cluster API Book: Excellent reading on the topic.
Documentation for CAPI Provider - AWS.
Documentation for the Bare Metal Operator I worked on this project for a couple of years and the community is pretty amazing. This GitHub repository hosts the CAPI Provider for bare metal nodes.
This should definitely get you going. You can start by running different providers individually to get a taste of how they work and then work with Cluster API and see it in function.
I've been reading the Google Cloud documentation about hybrid GKE cluster with Connect or completely on prem with GKE on-prem and VMWare.
However, I see that GKE with Connect you can manage the on-prem Kubernetes cluster from Google Cloud dashboard.
But, what I am trying to find, is, to mantain a hybrid cluster with GKE mixing on-prem and cloud nodes. Graphical example:
For the above solution, the master node is managed by GCloud, but the ideal solution is to manage multiple node masters (High availability) on cloud and nodes on prem. Graphical example:
Is it possible to apply some or both of the proposed solutions on Google Cloud with GKE?
If you want to maintain hybrid clusters, mixing on prem and cloud nodes, you need to use Anthos.
Anthos is a modern application management platform that provides a consistent development and operations experience for cloud and on-premises environments.
The primary computing environment for Anthos uses Anthos clusters, which extend GKE for use on Google Cloud, on-premises, or multicloud to manage Kubernetes installations in the environments where you intend to deploy your applications. These offerings bundle upstream Kubernetes releases and provide management capabilities for creating, scaling, and upgrading conformant Kubernetes clusters. With Kubernetes installed and running, you have access to a common orchestration layer that manages application deployment, configuration, upgrade, and scaling.
If you want to know more about Anthos in GCP please follow this link.
I'm trying to use the free tier (autopilot mode) to learn k8s on gcp. However I cam across the following Is it possible to have Google Cloud Kubernetes cluster in the free tier?. However when I checked the link given in the question I could not find the specified limitation f1-micro machines are not supported due to insufficient memory. Is this still valid ? can I use k8s on gcp in the free tier without incurring any cost?
There is no way to get a free GKE cluster on GCP, but you can get a very cheap one by following the instructions at
https://github.com/Neutrollized/free-tier-gke.
Using a combination of GKE's free management tier and a low cost machine type, the cost estimate is less than $5 per month: .
More details on what is available as part of the free tier can be found here: https://cloud.google.com/free.
Also, for your question regarding limitation of f1-micro to be used in GKE,if you follow the documentation limitation
It is written that- Minimum CPU platform cannot be used with shared core machine types. Now since f1-micro machines are shared core machine types. So it is valid and cannot be used.
As described in the documentation, there is no management cost for 1 Autopilot or 1 GKE standard zonal mode.
You don't pay for the control plane. But then you have to pay for your workload measured in seconds for autopilot (pod level) and seconds for GKE standard (node level (compute engine))
For study purpose, I believe its better to have classic (Standard) GKE cluster rather than autopilot, where you have less managing options.
When its come to pricing, using Preemptible nodes in GKE cluster is a better option where pricing is really low.
you can enable this selecting as below
Preemptible nodes
Hi If you want to learn the K8s on GCP you can try using qwiklabs where you get some initial credits by which you can access some of the labs where you can learn and practice your activities. https://go.qwiklabs.com/qwiklabs-free
I would like to use Google Kubernetes Engine for my Deep Learning project (google cloud storage, docker, tensorflow, etc.).
I found that Google VM Instances with GPUs are expensive for this initial phase of the project. I would like to use my 3 home computers with GPUs. Is possible to join these local computers to Google Kubernetes Engine and to create hybrid cluster?
Thank you for any feedback or comment.
I think an easier way could be to install a native K8s cluster on your 3 home computers. If you want to access data in GCP Persistent Disk or FileStore from the cluster, the CSI plugins should be also installed. These 2 repositories may help.
filestore
persistent disk
You can check out Anthos. It is a new product by Google made specifically for this
I was thinking of testing out Google's Cloud Run for a simple app when all of a sudden I got thinking as to whether Cloud Run is basically a managed K8s cluster. I really wanted to know as to when using Cloud Run would be preferred over traditional K8s clusters and why we should prefer it?
Thanks.
Technology wise, cloud Run is a managed Kubernetes cluster with Knative to run the containers on top of it.
However Cloud Run brings an additional advantages when you run fully managed: you only pay for used resources. In other words, Cloud Run can do scale down to zero cost, rather than bottoming out at the cost of keeping a minimum sized cluster running.