Google Kubernetes Engine Reviews

Mainly, we target SMEs for Kubernetes services. Currently, we have a few customers, and they expect more customized experiences.

The features are typical Kubernetes, but Google One offers a better GUI-based deployment. It's more sophisticated and integrates well with other services, providing a better customer experience.

There is room for improvement in this solution. For example, auto-scaling can be complex. We expect it to be easier to set up and manage, even for our customers.

We started recently. I have around one and a half months of experience with it.

The stability is good. I would rate it a nine out of ten.

It is a scalable solution. I would rate the scalability a nine out of ten.

The customer service and support are good. 

Positive

The initial setup is pretty simple; we can deploy a cluster within a minute. Just choose a region and the required parameters.

One person can handle it unless they're inexperienced.

Pricing is a bit expensive compared to some other products, but it's acceptable.

I would rate the pricing an eight out of ten, where one is a low price, and ten is a high price. 

The auto-scaling can be complicated, so they should be prepared for that. It is difficult to hand over the solution to customers because of scaling up and scaling down issues. If Google improves that aspect, it'll be easier to manage.

Overall, I would rate the solution a nine out of ten.

We have everything in Kubernetes. We're basically moving everything from the cloud into Kubernetes - inverting the cloud. We have all that built for the CIT pipeline and have our tools within the cluster.

This is to support application development. The application side is always within the cluster. We have a security cluster. So everything is there. We have a database within the cluster as well. We don't need a managed database. There's a cloud database due to the fact that we use Kubernetes database. Everything goes into the cluster.

It makes it easy for us to be consistent across different environments, including development environments or in Oracle environments, as everything runs within the cluster.

The solution allows you to work on and from multiple clouds. You can use Google's cloud, or mix and match clouds across suppliers.

You can split into regions within your own cloud.

The deployment of the cluster is very easy. You just click a button and it's deployed, or just run a simple command and it deploys itself. You don't have to go through the steps of installing the cluster yourself. It's already deployed and managed.

The master of the cluster is also managed by Google. If there are any updates, they are responsible to handle that. It just takes a little bit of a load from our task load. You don't have to manage the master, or the version of the cluster yourself. 

You don't have to think about the installation process. They take care of the underlying infrastructure deployment and managing the versioning of the cluster. When we need to update, it's simple. They'll help us to easily, smoothly update those cluster nodes. You don't have to deal with that either.

When it comes to the Google Cloud, the Kubernetes advantage that's there for machine learning is that they have a CPU, which is a central processing unit, which is much faster than GPU. If the clients are willing to pay for it, we'll run the machine learning jobs within the Kubernetes cluster, then connect to Google CPU, which gives us the ability to finish the job much, much faster.

It's maybe a controversial topic, as Kubernetes itself should be just your bottom layer. However, within your own engine, you expect to do more with time. Since we're putting so much into the cluster, it would be nice if some of this stuff was already done, baked into the cluster.

Our critique is that we have to do too much work to get the cluster production-ready. Most people just start it and think that's production. That's not really production. That's just bootstrapping the cluster, with all the tools that you need.

A lot of people rely on cloud tools, or a cloud-built system, to get going. We would like to have that baked into the cluster. Due to our usage pattern of the cluster and how heavily we use it, our expectation is to have more tools baked into the cluster. There should be more emphasis on tools developed immediately from the cluster to support application development versus relying on third-party vendors, like Jenkins. 

The third-party vendors have to adapt to Kubernetes, and that creates a problem, as there's always a delay. Third parties don't have much incentive to do anything right away. That means we have to wait for these guys to catch up. We don't have a big enough team to actually change every open source code, as there's so much of it.

We started using Kubernetes in 2015, around the time it started. Whenever Google launched their tools is about the time we started. Before that, we used Kubernetes, however, we were deploying it ourselves.

The solution is stable.

The solution is cloud-native and every cloud is using basically the same version. That's what makes it easy for us to move between clouds. Google wants users to integrate with their own cloud storage and security, however, which is where issues can arise. 

It allows you to create private clusters. There's no competitive advantage for cluster clouds right now, which is good for us, as it's a uniform-looking ecosystem that allows us to move between clouds easily.

Kubernetes is designed to scale horizontally and vertically as well. It scales quite well.

We have unlimited scaling through horizontal scaling. We can add more Kubernetes nodes. When applications do grow, we need to maybe horizontally scale applications or our databases. We just kick off another node when we need however much memory CPU and just keep on scaling it. Obviously, you pay for it, however, scaling is extremely easy.

A lot of time we automate the scaling as well. Based a little bit on AI and cloud automation processing, we detect the CPU usage or the GPU usage and if we exceed a certain threshold, the cluster automatically adds another data node, so it's self-serving. So scaling is almost automated around cases that we do.

The system knows by itself how to scale dynamically. The dynamic elastic scaling is baked into our systems as well. When people do use, for example, the machine learning special cluster, that one goes automatically from zero to 23 nodes depending on the users. A lot of times, we do shut it down, with just no usage. When people kick off a job, it automatically spins up on a new cluster node and deploys the job, gets another job, and spins up another one. It dynamically grows. We have to allocate pools that we have of an increased cluster in Google Cloud and it works well.

We basically are able to solve our own problems on our end. We don't need the assistance of technical support. We might have used it once in the past six or seven years and that is it.

The initial setup is very straightforward. Everything is basically done for you. It's nice and easy.

We can do the cluster management. There are cluster administrators who take a more serious role. They are responsible for the disks backing some of these applications, the databases, deployment of these tools, the infrastructure tools, et cetera.

We have application developers which work with the administrators to actually deploy the application, as you still have to just be meaningful in the cluster, and add some sort of business logic. Those have to work with our administrators so we get that done and we do support, and a lot of different tools, monitoring tools for visibility. It's important to us, because if we do, we follow a microservice architecture across applications. They're like little black boxes and we have to be able to see inside of them.

Multi-cloud is a sort of an expensive endeavor as the tools are overpriced. We're looking at options that aren't based on Anthos, which is Google's multi-cloud solution. 

While you pay money to Google, they also take a piece of the action as well.

CPU is very cheap, however, GPU is very expensive. If you want to iterate on your data client's tasks within a Kubernetes cluster, it will cost you.

There is no licensing cost. You pay for the cloud and you pay for what you use based on the CPU and RAM usage based on the VM, the virtual machines. The cluster is still made up of computers, so you pay for the computers that are backing the clusters. If you kick off a Kubernetes node, which has three nodes in your cluster, you have to pay for each of these nodes, these computers, these virtual machines that you get bootstrapped with. You just use the machine time as with any cloud and you get a price in Google for the machine type and your machine type is defined based on your CPU and RAM usage. If you want to have 60 GBs of RAM, you pay for that RAM or for CPUs.

The same thing is true if you ask for a GPU computer, as most of the virtual machines don't come with a video card unless you ask for it. Then you have to pay for that the computer and the video card

We're actually certified as well Kubernetes vendor.

We're using version 1.19. The most up-to-date is 1.20. We're never on the latest version, we're always like a version behind, or even two versions behind, to give them time to sort through their issues. We're using 1.19 in both Azure's, Google Cloud's, and EKS, however, EKS might be two versions behind, maybe.

Most of the time we're deploying in, as a private cluster within the cloud. It's isolated from public infrastructure. That's for security reasons. We don't want our cluster to be exposed to the public internet.

We also have a hybrid deployment Azure on-premises. This is just to make things easier for integration purposes. On-premise, it's connected to the cloud and then we can just use the same tools to be Kube-Native source. We develop the same tools for Kubernetes and then we can just deploy Kubernetes on-premises or in the cloud, it doesn't matter.

We also are doing multi-cloud as well, and we're deploying from Google Cloud into AWS.

With Azure, we have one giant cloud right now. That way, we can partition a cluster and see multiple clouds and multiple visions. If Google Cloud goes down for whatever reason, as it happened, two years ago, due to bad configurations, too many clusters in a cloud, we're covered. We do multi-cloud as the solution is critical and we can't afford to have it go down.

We are basically are a full-service company. We do everything for our clients - including application development and everything that entails.

I'd advise users to take security seriously. Don't just deploy things on the internet. Make sure your cluster's secure. You want to be able to tell your clients that you have a secure implementation of a cluster. That requires a little bit of cloud set up with every cloud to create a private network, private subnetwork, manage the ingress and egress, so input and outputs of the requests coming into your cluster.

These are things you have to think about when you deploy, just initially before you get started. All the clouds support it, you just have to know how to set up your VPC, virtual private network connection tier with every cloud and how to set up subnets to isolate your cluster to specific subnets, so it's not exposed on the internet, it's private and then any requests coming from the internet have to go to your load balancer directly to your cluster.

However, if you manage that and some peoples' requests going out of your cluster, you won't be able to manage those as well, since they're on NAT and a cloud router as well. So you know what's coming in, what's going out. You can monitor your traffic coming in and within your cluster.

These days a lot of people just use the containers directly from third-party sources or public repositories in the docker containers in which the Kubernetes cluster runs and those could come with malware. You want basically, in the main cluster, to have security policies implemented for every cluster. You don't get that from your cluster loggers. You have to get that from third-party vendors. This is where the competition comes with the Kubernetes. 

In general, I would rate the solution at an eight out of ten.

We use it for all applications.

The logs are important for detecting problems in our clusters. I use the fragment with this property, which is valuable.

The current version is quite good. However, a new version, 1.26, is not yet stable. GKE offers regular, stable, and rapid versions, but this one is being used for the rapid version and hasn't been thoroughly tested yet. I think DCP may be quicker in releasing more stable versions.

I have been using Google Kubernetes Engine for two years. For SunTrust, we use version 1.21, which is old. But on ComMaster, we use version 1.24.

It is not very stable, I would rate the stability an eight out of ten. 

I would rate the scalability a ten out of ten. Around 30 users are using Google Kubernetes Engine in your organization.

The customer service and support team is quite good. 

Positive

We can't say the initial setup was easy, but it wasn't difficult either. The deployment process was pretty fast. 

The pricing is average. For example, Tanzu Build Service is very expensive, but generally, it's okay. However, I understand that the on-to service is very expensive.

Overall, I would rate the solution a seven out of ten. 

Google Kubernetes Engine's most valuable features are microservices and its acquisition rate, which is very useful for scaling perspective.

The user interface could be improved. In the next release, I'd like to see better notifications.

I've been using Google Kubernetes Engine for a few years. 

Kubernetes is stable because it has scalable architecture, so if any part goes down, it starts from the other parts. 

Kubernetes is scalable.

The initial setup was simple.

I would rate Kubernetes' pricing four out of five.

I also evaluated VMware, which is more expensive than Kubernetes.

I would give Kubernetes a rating of eight out of ten.

Our primary use case is to arrange the correct CICD (Continuous Integration / Continous Deployment) conveyor to provide for continuous changes in production.

The improvement is mainly connected to the speed of change implementation. In the case of the automatic convenor, we spent less time due to the automation of the process. Before using this solution, it was a lot of manual tasks and a lot of people participated in the process.

The most valuable feature is the horizontal staging of applications. Other important features include isolation of applications and more effective usage of infrastructure due to less consumption of resources by containers.

I think that security is an important point, and there should be additional features for the evaluation of data in containers that will create a more secure environment for usage in multi-parent models.

We have not seen any signs of instability, and have an optimistic view of the solution.

Scalability is an important feature of this solution, and we are happy with it.

We have approximately one hundred people using the solution. It is mostly developers and quality assurance people who are working on the preparation for CICD.

Once we move to production, next year, our usage will increase.

I think technical support is good enough.

We did not use an integrated solution prior to this. Rather, it was done in a more traditional way. This included virtual machine creation, installation of additional software, and connection to an external CICD conveyor, etc. We are switching because we are interested in more widely using continuous technology.

Our motivation for switching is to simplify creating a CICD process. We have a lot of small changes and after testing, we will be using an automated process for product delivery.

We are currently in a test phase and are estimating the feasibility of moving this to production. Our plan is to finalize testing by the end of this year and move the solution to production at the beginning of next year.

We have two people working the maintenance of this solution, but frankly speaking, it is not enough. We are planning to improve our skills and capacity and expand these resources.

We communicate directly with somebody who is part of OpenShift. They are top guys and have enough experience to help us build our system. It's no problem.

We do not have a local team in Russia, but at some point, that may change and we will use a local integrator. 

We are planning to reach a positive ROI using this solution.

We are planning to use external support, and hire a commercial partner for it. Usually, this is about twenty percent of the solution.

We have been watching what is happening the market, and for some time it has been obvious that Kubernetes has the most followers and most potential. This is why we are starting with Kubernetes from the beginning.

My advice is not to implement this solution unless there is a genuine demand for it from the business side. It can be useful to start from the bottom of the infrastructure and take it to the highest level because it requires changes in the development and business levels to work with this technology.

I think that there is enough documentation available to start to work with this product. The technology provides a very good opportunity to grow and improve.

I would rate this solution a six out of ten.

I use the tool to host a SaaS application.We also provision the clusters to help students learn how to use Kubernetes.

The solution is more user-friendly than AWS or Azure. I can also easily scale out the service in the future when the number of customers grows. GKE is the leader of Kubernetes service and it can be easily updated. I love the tool's user interfaces. 

I use the Firebase tool with GKE and it would be helpful if the solution can give notifications when we reach the budget limit. 

I have been working with the solution for more than 8 years. 

The product is stable. 

The tool is scalable and we have around 3000 users for the product. 

The tech support's service is good. 

Neutral

The solution's setup is very easy. 

The solution's ROI is good. 

The solution's price is reasonable. 

I would rate the product a nine out of ten. 

The Google Kubernetes Engine is used for managing data workloads. As a part of the platform engineering team, we are uncertain about the specific data workloads running. Primarily, the application teams wanted to run specialized workflows, while the data engineering workflow preferred to opt for GKE. After a thorough evaluation of the different versions of the Kubernetes platform over a week, we informed the application teams about the available options. In case of confusion, we clarify the advantages and disadvantages of each feature. However, when the application teams express their preference for GKE, we support their decision, as they comprehensively understand the use cases for both EKS and GKE. This invariably leads to a smoother onboarding process, as we are well-equipped to cater to their specific requirements.

We are working and monitoring HTTPS on GKE. It has a great manager for license management. The controller policy item for that particular feature doesn't seem visible anywhere, which might be a bonus, but we are unfamiliar with it. As we explore the multiple flavors of Kubernetes clusters, we aim to enforce similar policies across all clusters. Overall, everything looks good, and hand tools are one of the unique features that set us apart from other vendors.

The solution's support is terrible. Even the GCP engineers have been tracing some concerns. The dedicated GKE support engineer often tries to find solutions when raised tickets. Support engineers don't know about it. Regarding AWS or Azure support, GKE support needs a lot of improvement. The documentation is another issue. Even though the support engineers keep referring to the documentation, it's not current because the cloud keeps evolving. They need to update the documentation frequently. The controller policy will be compatible with version 1.26 of Kubernetes. We assumed we needed to upgrade to 1.26 fast, as we were running 1.25. However, after spending two and a half months, we realized it works with 1.25 as well. Even the GCP engineers were not aware that it was compatible with 1.25. 

Google Kubernetes Engine has a limited layer of support, and not many workloads are feasible. Some application teams need the Kubernetes platform. However, we can't recommend GKE, even though the cost would be lower than EKS and OpenShift. We can't trust GCP support when major issues arise.

I have been using Google Kubernetes Engine as an integrator for one year.

The initial setup is relatively easy, but unfortunately, some issues repeat. For instance, we always try to showcase our capabilities during working sessions with vendors, like when they come for a demo or even with AWS. However, when we require assistance, the installation seems straightforward, yet when an issue arises, we struggle to resolve it. We're trying to schedule a working session with GCP, but they are unavailable. Despite multiple follow-ups, when they finally arrive, the experience is not very productive, as the person often searches for solutions. Overall, the installation process is smooth. If we stick to the application, it seems to be working fine.

We use Terraform. It will take some time, but the cluster installation on the Google Kubernetes Engine will be completed within four or five hours.

I'm familiar with Kubernetes, but not many customers are using GKE. For instance, I've been involved in one project using GKE for about six to seven months; before that, another project used GKE for a year. However, I can only recall one project that used GKE for four months, about one and a half years ago. My work revolves around Kubernetes, but it's not exclusively focused on GKE.

The maintenance of the solution is at an intermediate level, as there are some issues. We encounter problems with the Ingress controller and the GKE clusters. We have some open cases with GKE and have been trying to find solutions. Some aspects seem easy, while others are challenging to troubleshoot. Moreover, the documentation lacks comprehensive recommendations for GKE; we often have to rely solely on Google's official documentation.

Overall, I rate the solution a five out of ten.

We use this solution to run the AI models that we have developed for anomaly detection, cloud workload prediction, as well as setting up clusters.

We appreciate that it is quite easy to set up a Kubernetes cluster in Google Cloud, using the managed services within this solution.

We would like to see some improvement in the ease of integration with this solution.

We would also like to see the addition of some automated realization of data pipelines.

We have been using this solution for nearly three years.

We have found the stability of this solution to be good during our time working with it.

This is an easily scalable solution.

The setup for this solution is reasonably simple.

We implemented this solution using our team of in-house, GCP-certified architects and operators.

We would rate this solution an eight out of ten.