Google Kubernetes Engine supplies containerized application management powered by Kubernetes which includes Google Cloud services including load balancing, automatic scaling and upgrade, and other Google Cloud services.
Kubernetes is by far the best choice. More reliable and better developer experience. Mesos is prone to sporadic failures and not really designed to handle CI/CD-based deployments. Docker Cloud once shut down our entire cluster for "upgrades" without giving us any warning.
Kubernetes is really great and their community is growing really fast (Google influence). We evaluated it in the beginning and it would fit for our web applications workload. We decided to proceed with Mesos because it has more potential. You may use a different framework for …
We have a CICD pipeline, which we wrote using the Gitlab CI file. This is connected directly to our GKE cluster. So, any change in our code will directly start the CICD pipeline. The pipeline first tests the deployment on testing environments. We are also using Helm charts to …
We had to move several products to Google Cloud, and the Google Kubernetes Engine was the option recommended to us, so we investigated it and ran with it. Back then (2019), we were not aware of Cloud Run-provisioned K8s clusters, so our other option was a completely …
GKE spins up new nodes a LOT faster than AKS. GKE's auto scaler runs a lot smoother than AKS. GKE has a lot more Kubernetes features baked in natively.
In comparison to functionality with EKS and AKS, it has a better upgrade path and the price is lower. Not sure why flannel is the primary overlay network provider but network policies are supported as well.
Google Kubernetes Engine has better upgrades and auto-scale management. Google Kubernetes Engine is also the cheapest option for managed Kubernetes, and Google is the principal contributor to the Kubernetes project.
Our organization went with Google's Kubernetes Engine because we are already significantly invested in the Google Cloud Platform. In our evaluation of Amazon's Elastic Kubernetes Service we were turned off by recent concerns about Amazon becoming overly dominant in the cloud …
Mesos is really great when you have a big datacenter with many different applications and use cases. It will help you to optimize the resource usage, being a centralized API for your infrastructure. It will not suit well for small companies that just need to deploy a web app. In this case, I would recommend something smaller.
Google Kubernetes Engine is well suited for dynamic and large workloads since it can scale up with usage. It is easily configurable, which allows for flexibility. User interface is simple to navigate, which reduces roadblocks for a team with people unfamiliar with Kubernetes. Great if you are already using other GCP services as it integrates well with that.
Mesos may have many frameworks. If you have Mesos installed on your servers, you may use it for many kinds of tasks. Today we're running only web applications but the idea is to install a different framework for big data soon.
Unreliable deployments that would fail for no good reason. Sometimes our Docker container would be "restarting" forever because Mesos thought it didn't have enough resources to start the container.
Impossibly slow UI. Built in React under the hood with a lot of bloatware backed in, so loading the Mesos UI on a slow internet connection was painful.
No real logging solution - it would stream "console.log()" output to the UI, but searching for logs wasn't really possible without downloading a huge file.
No built-in support for redeploying containers from a CI. We had to create a service whose whole job was to expose an HTTP endpoint that restarted a container, and then made Circle CI ping the endpoint whenever we wanted to redeploy.
It's a great product if you learn it. It has flexibility and is very strong. Autoscaling and Resource management make running huge applications a breeze. Using Helm with Kubernetes and Terraform for infrastructure creation can totally automate your CICD pipeline. You also get easy access to CUDA cores for machine learning.
Kubernetes is by far the best choice. More reliable and better developer experience. Mesos is prone to sporadic failures and not really designed to handle CI/CD-based deployments. Docker Cloud once shut down our entire cluster for "upgrades" without giving us any warning.
We had to move several products to Google Cloud, and the Google Kubernetes Engine was the option recommended to us, so we investigated it and ran with it. Back then (2019), we were not aware of Cloud Run-provisioned K8s clusters, so our other option was a completely self-managed K8s cluster on Compute Engine VMs, which we did not have the knowledge of and capacity to handle.