Google Kubernetes Engine Reviews

The primary purpose for utilizing Google Kubernetes Engine (GKE) is for application deployment. This managed cloud service eliminates the need for operating our own Kubernetes cluster. Our applications are designed in a microservice architecture, meaning they are comprised of numerous smaller components, each running on its own container. GKE acts as an orchestration engine for these containers, managing and organizing them. In essence, GKE serves as a platform for both application development and deployment within a Kubernetes cluster. This is our main use case for GKE.

In addition to deploying applications, we also utilize GKE for deploying our machine learning models. By containerizing these models, we are able to deploy them within the Kubernetes cluster, making it easier for our applications to communicate with them. As the application and the model are co-located within GKE, it is simpler for us to manage this process and make predictions in a timely manner. This is an advantageous use case for us.

Machine learning is not a use case that we utilize GKE. Instead, we have our own platform in place to deal with GKE. Although GKE is an open engine, it still requires compliance, security, and observability. Thus, we provide these elements to our clients. This includes observability through the collection of metrics and logs from all containers within GKE, which we aggregate and display through dashboards and dashboarding tools. Additionally, we have built our own security system within GKE, which includes hosting security tools to manage passwords, secrets, and certificates. These tools are also deployed within GKE.

In addition to application deployment, our continuous integration and continuous deployment (CI/CD) pipeline are housed within GKE. Our pipeline includes tools, such as Jenkins and Slack, which aid in the building, containerization, and deployment of software. This comprehensive pipeline within GKE streamlines the development process and allows for the efficient and effective release of our software. Currently, GKE serves all of our use cases related to software development and deployment.

In our organization, GKE is utilized to orchestrate containers that hold microservices, which combine to form an application. Furthermore, we also utilize GKE to host self-hosted databases and build our own data pipelines. As a result, GKE acts as the foundation for our data platform, supporting multiple different types of databases within the cluster. The solution has been helpful for our organization.

The main advantage of GKE is that it is a managed service. This means that Google is responsible for managing the master node in the Kubernetes cluster system. As a result, we can focus on deploying applications to the slaves, while Google handles any updates and security patches. The fact that GKE is fully integrated into the Google ecosystem, including solutions such as BigQuery and VertexAI. This makes it easier for us to integrate these tools into our process. This integration ultimately speeds up our time to market and reduces the time and effort spent on managing infrastructure. The managed aspect of GKE allows us to simply deploy and utilize it without having to worry about the technicalities of infrastructure management. 

Recently, Google has introduced new features to GKE. One of the latest additions includes a managed backup service, which backs up the disks attached to the containers within the platform. This service is a valuable asset provided by Google. Furthermore, they also offer configuration management, providing all the necessary infrastructure and services to accompany the use of Kubernetes. This saves time and reduces the effort needed to manage the cluster, allowing for a more focused approach toward business-critical tasks, such as containers, building pipelines, and more. GKE provides the necessary support and resources to allow for rapid deployment and efficient management.

While the GKE cluster is secure, application-level security is an essential aspect that needs to be addressed. The security provided by GKE includes the security of communication between nodes within the cluster and the basic features of Kubernetes security. However, these features may not be sufficient for the security needs of an enterprise. Additional security measures must be added to ensure adequate protection. It has become a common practice to deploy security tools within a Kubernetes cluster. It would be ideal if these tools were included as part of the package, as this is a standard requirement in the industry. Thus, application-level security should be integrated into GKE for improved security measures.

Additionally, a crucial aspect that was previously lacking was a reliable backup system. Although Google has recently released a beta version of GKE backups, it still requires improvement. Within a cluster, many components, such as databases, have a state and a disk attached to them. Hence, it is essential to have both physical snapshots of the disk and logical backups of the data. However, the backup system offered by GKE is not yet fully developed and requires more work to become a robust enterprise feature. For enterprise applications, it is imperative to manage state and take regular backups due to the Service Level Agreements (SLAs) signed with clients, which often require multiple backups per day. Thus, further development and improvement of the backup system are necessary.

I have been using Google Kubernetes Engine for approximately six years.

GKE is extremely stable, with very few issues related to stability. This is due to frequent and continuous updates to the system. In the world of Kubernetes, it is common to maintain one version behind and two versions ahead, allowing for a clear understanding of upcoming releases and the ability to subscribe to the latest versions. Google is always at the forefront of updates and releases, and users have the option to either use the latest and most cutting-edge versions or stick with the stable and tried-and-true versions. There are no problems or concerns with stability in GKE.

GKE was designed with scalability as its core feature, offering both flexibility and scalability in its functionality. It is easily adaptable for scaling both horizontally and vertically, making it ideal for our machine-learning tasks as well. The ability to attach a GPU to a node in the Kubernetes cluster is a straightforward process, providing us with the option to deploy a Kubernetes cluster with or without video cards, based on our specific use case requirements. The horizontal scalability of GKE is instantaneous, as the solution was specifically engineered to excel in this aspect. The scalability of GKE is one of its most valuable features, making it a prime selling point.

Regarding support from GKE, I have limited knowledge. Our team is highly skilled in the field and would not require support from Google. In fact, I have communicated to Google that we do not require certification from them, as we are already Kubernetes certified and feel no need to be Google certified. I believe there is no return on investment for us in obtaining this certification. Despite Google's efforts to encourage us, we have informed them that they should focus on getting certified themselves rather than having us certified. Our team has a vast amount of experience and knowledge in the field, having been involved in the beta project even before Google knew the ins and outs of the technology. Therefore, we are capable of resolving any issues that arise on our own, without the need for assistance from Google.

This solution is better than Amazon and Azure.

Deploying GKE is a swift and seamless process, accomplished by running scripts. Our approach to infrastructure is based on the principle of infrastructure as code, utilizing Terraform for all operations. Google offers Terraform integration, further simplifying the process. Instead of manual intervention through the console or script writing, we choose to automate every aspect of our deployment, including GKE deployment, through Terraform. The cloud engineering provided by Google encompasses all the necessary tools to rapidly deploy and manage GKE, freeing us from the tedious task of managing individual components of the cluster.

Getting started with GKE is relatively simple, but ensuring proper deployment can be challenging. The ease of use, with just a click of the mouse button, does not guarantee secure and compliant deployment. Google should do more to educate users on the proper way to deploy GKE and provide resources such as recipes or integrate these best practices into the standard offering. For example, making the GKE public should be avoided as it poses a security risk, as each node in the cluster is publicly facing the internet, making it vulnerable to attacks by hackers who could target any of the nodes and potentially access a piece of the application and data.

The requirement of a private deployment in GKE comes with the need for extra configuration and networking setup, which can pose a challenge for developers and companies who are not familiar with the process. Although Google provides guidance and best practices, it is still necessary to have a good understanding of network engineering in order to successfully deploy Kubernetes. The complexity of the process can result in incorrect or insecure versions of Kubernetes being deployed, as seen with the recent hack on Tesla's GKE due to their improper deployment. Ideally, these configurations and setup steps should be integrated into the solution itself, eliminating the need for excessive technical expertise.

I rate the setup of Google Kubernetes Engine a seven out of ten.

The pricing for GKE is dependent on the type of machine or virtual machine (VM) that is selected for the nodes in the cluster. There is a degree of flexibility in choosing the specifications of the machine, such as the number of CPUs, GPUs, and so on. Google provides a variety of options, allowing the user to create the desired cluster composition. However, the cost can be quite steep when it comes to regional clusters, which are necessary for high availability and failover. This redundancy is crucial for businesses and is required to handle an increase in requests in case of any issues in one region, such as jumping to a different region in case of a failure in the Toronto region. While it may be tempting to choose the cheapest type of machines, this may result in a limited capacity and user numbers, requiring over-provisioning to handle additional requests, such as those for a web application.

The cost of using GKE, which includes having a redundant system and failover capacity, appears to be overly high. The requirement of having this extra capacity in case of disk failure or other issues means paying for the extra provision, which contributes to the elevated cost. This pricing model seems to be an unfair practice on Google's part as redundancy is a fundamental aspect of any business and must be paid for regardless of whether it is used or not. When it comes to general pricing, the choice of what is best for the specific use case is left to the user.

I rate Google Kubernetes Engine an eight out of ten.

We use Kubernetes for our data pipelines. For everything else, we use the standard version of GKE, and we manage the whole stack. It's a bit higher than infrastructures as a service. It's more of a platform as a service. Kubernetes is the platform we use to build our staff products.

We're a Kubernetes certified company. Everything in the cloud, we push toward Kubernetes. You can move Kubernetes between systems, and we're deploying the system into Kubernetes securely. We're running a lot of fintech applications and healthcare applications, so both financial data and patient healthcare is essential. That's one of the reasons we moved to Google Cloud, because the implementation is a little more secure.

Kubernetes is the number one tool used by every company in the last five years because it's a dynamic container runtime engine, and we ship software in containers.

We've been shipping software containers since 2014. We switched to Kubernetes around 2016 or 2017. We switched completely to Kubernetes as our container runtime engine. That's how we ship software and maintain managed software. Kubernetes is the primary tool for all our work that we do in DevOps.

There are multiple flavors of GKE. That's why we deploy it for different use cases. There are different issues with each of the use cases. When it comes to using Kubernetes as a commodity, which means allowing Google to manage your virtual machines, they still don't have all the features baked in. Our issue is that you have no ability to change it because Google manages it. Our biggest issue right now is that it just requires a little bit more control over some of these Google managed versions of virtual machines.

One of the biggest issues right now is the Kubernetes backup system. That's being handled right now by Google, but it's in beta. There are no fundamental issues like we have in EKS or Azure with a private cluster. They nickel and dime you with features or they're pushing you to their own observability tools. We want to use our own observability tools.

The whole thing is integrated with Google monitoring and logging all that stuff in the cloud, so it's not necessarily bad. It's just that we want to use our tools. Service mesh management is an issue as well. There's only one service management, so we would like to use console service mesh so we can use the managed project there. We don't like the way Google deploys things. We like to deploy things ourselves, and that can cause friction in terms of how we deploy things. We have to spend a little bit of extra time on coding. Fundamentally, I don't see any issues with it right now.

The stability is good.

The scalability is good.

In terms of deploying multi-regional clusters or multi-zonal clusters, and multi-cloud, we can do that. Most of the time for development, we use a single zone just to save money, but for our staging environment, we use multi-zonal in a single region. Then when we go to production, we use multi-regional. We can scale it all the way from a single zone to multiple regions around the world. 

Now with Anthos, which is the multi-cloud version of Kubernetes and Google Cloud, it allows us to deploy Google Cloud Kubernetes into AWS or Azure. That means that we don't have to use EKS; we can just deploy Kubernetes and Amazon through Google Cloud, so we have one portal to manage everything.

We haven't needed to use technical support yet.

We use Amazon EKS for some clients. When we write software, a lot of times we try to keep the deployment in-house. But if it's just the client, they have their own deployment DevOps team.

It's straightforward. We did everything in Terraform. All of our infrastructure is codified. We basically codify our whole infrastructure. We have a deployment platform to plan this and to be able to deploy all the tools in Google Cloud. It's easy for us to manage. If there are any issues, we can always change that in our code, and manage it through just code. The accessibility is nice.

Auditing those cloud resources is very easy for us. It gets quite expensive as well. If you don't keep your tabs on it from a business point of view, you're always going to check the billing to make sure that there are no services that aren't being used efficiently. Google is fully committed to Terraform scripts. 

I think every part of the communication has been updated from just basic CLI to Terraform scripts, which is nice. The Terraform, Google Cloud module is almost fully baked. It's basically a mature tool as well. It makes things a little bit easier for us to manage.

I would rate this solution 8 out of 10.

We have somewhere around 20 microservices that we need to deploy for our product. We are using Kubernetes Engine to deploy those 20 microservices.

The feature that I like the most is the ease of use as compared to AWS. Its ease of use is very high, and I can quickly deploy clusters with a simple template.

Their documentation is a little here and there. Sometimes, the information is not clear or updated. Their documentation should be a little bit better.

They have a good marketplace, but it is still evolving and is not as mature for some services.

I've been using this solution for about a year.

It is pretty stable. I did not encounter any problems.

In the last 10 months, scaling was easy. There is an auto-scaling feature where I can just provide them with how many nodes I require, or I can create custom node pools where, for particular applications, I can deploy certain nodes. I haven't had any issues with scalability.

We have four or five users. They are DevOps engineers. Only two users are the owners. They have deployed Kubernetes Engine, and they manipulate the workload. Its usage would be twice or thrice a week.

I haven't encountered any issues so far, and I haven't raised any doubt with them. So, I haven't interacted with them. We are currently managing four or five projects with Kubernetes Engine. We haven't had any issues with any of them.

It is easy to set up. In AWS and other clouds, the support for Kubernetes is minimal, but in Google Cloud, it is much easier to set up. I would rate it a five out of five in terms of ease of setup.

It was set up in-house.

It is competitive, and it is not expensive. It is almost competitive with AWS and the rest of the cloud solutions.

We are spending around 3K USD per month. There are four projects that are currently running, and each one is incurring a cost of around 3K USD.

For Kubernetes, the components have to be broken down into machine cost, storage cost, and application cost, if you are using any private applications or any images. If you look at the compute instances for GCP, there are hundred different tiers. For example, you have general-purpose E2 machines, then you have high-compute machines, and then you have the GPU machines for T4. Those are pretty standard compute instances. Kubernetes Engine is just a layer on top of these compute instances to control your entire microservices architecture.

I would rate it a four out of five in terms of pricing.

I am yet to explore all of its features. I would rate it an eight out of ten.

The autoscaling feature is the most important feature in Google Kubernetes Engine. It also supports containerization. The solution is available across AWS, GCP and Azure and is seamless.

It does not require much investment if you move from AWS to GCP or Azure. The entire cloud process remains the same, with only minor changes.

The console for this solution could be improved because it is very limited. In addition, features like a desktop for the Docker image, drag and drop into a node could be added.

In terms of visualization, the solution could also provide some integration with other tools. We have heard about Grafana and AppDynamics offering these features, but they still require better tools. Google is going in the right direction because they believe in open source integration and give opportunities to other players to work with them. However, they should provide packages where they integrate more features to demonstrate more encouragement in the marketplace.

Training, tutorials, tooling, samples, and more case studies should be included in the next release.

We used this solution for one year.

The solution is stable, and I think even more stability will occur over time. Google Kubernetes Engine is moving in the right direction, especially when you consider concepts of Docker image, pods, nodes, clusters, control planes, data planes, gateway, and the ingress. Many projects run in production on Google Kubernetes Engine, and companies partner with AWS, GCP, and Azure to provide training and assist in completing modules regarding portals.

Google Kubernetes Engine is a scalable solution.

The setup was confusing. We were able to install it locally, but it was slow.

Because I worked in production, I did not use technical support directly. It was required but most likely very minimal.

I do not know specific details about the price, but I believe Google Kubernetes Engine is cheaper than Pivotal Cloud Foundry.

We compared Google Kubernetes Engine with Pivotal Cloud Foundry and found that Google was better.

I rate this solution seven out of ten. I believe Google Kubernetes Engine is the best solution in the market.

All of our clients are using GKE lightly. The companies are big, but the usage is small.

GKE's plugin management and configuration sync are excellent features. The amount of data it provides is good, and I've been able to integrate it with the things I need.

The user interface is a bit confusing sometimes. You need to navigate between the various consoles we have. It's hard to figure out where things are.  It's frustrating. The documentation could be a bit better.

I have only been using GKE for a couple of months, but I have been working with Kubernetes for about six years and EKS for a year. 

GKE is scalable.

We were previously using basic Kubernetes that we set up and managed ourselves. I've also used EKS.

Setting up GKE is somewhat complex. There are lots of concepts that are involved and many options to consider. At the same time, you can easily automate it. It's easy to interact with the client and the APIs, and the documentation is good. Sometimes it is confusing, but you can find most of the stuff that you need.

I think EKS and AWS are very similar usages. I don't know if one is superior to the other. Google might be better at working with identities for workloads and resource integration. I think it's a bit better and easier than AWS's IM.

I rate Google Kubernetes Engine eight out of 10.

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.

I am just starting with it. I am testing different platforms. I've done some deployments, and with some samples, I've tried to install the Kubernetes application.

I am using its latest version.

It's easy to manage and deploy. It's the best. 

I can have some controls through some parameters, and it's very good. 

There is a limitation for our infrastructure. It's very complex to see in one dashboard all the components and all the behavior on performance. I am looking for some additional tools for that. If I want to check the disk or file storage, it gets complex. There should be an integrated dashboard so that we can manage everything through a single pane.

I've been using this solution for two or three months.

It's stable.

I haven't yet used their support.

Its pricing is good. They bill us only per user. That's nice.

I'd rate it an eight out of ten.

Google Kubernetes Engine is used for orchestrating Docker containers. We have 30 or 40 customers working with this solution now. We'll probably see 10 to 15 percent growth in the number of customers using Google Kubernetes Engine in the future. 

We've been using Kubernetes Engine for five years.

Google Kubernetes Engine is less stable in some highly complex deployments with many nodes. However, those are probably edge cases.

Google Kubernetes Engine is scalable 

The ease of deployment for Google Kubernetes Engine is about average.  

Google offers yearly and monthly subscriptions. 

We don't prefer Google over Amazon or any other Kubernetes solution. It depends on the technology stack that our customers choose based on project needs. It's not better than the others.

I rate Google Kubernetes Engine eight out of 10.