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 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 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.

I am impressed with the product's auto-scaling. 

I would like the solution to integrate with another Kubernetes product. I would also like it to monitor other platforms. It needs to also include scale-up container in the tool's next release. 

I have been working with the product for four years. 

I would rate the solution's stability a nine out of ten. 

I would rate the product's scalability a seven out of ten. It can scale up automatically. My company has 5 users for the solution. 

We rely on technical support from USA or India since it is not available in the local area. It is very difficult to get support. 

Neutral

I have used VMware's virtual machine before. We switched to the product since it offered flexibility and the ability to auto-scale. 

The product's setup is easy and I would rate it an eight out of ten. The deployment got completed in three hours. We relied on a DevOps engineer to complete the deployment and maintenance. 

We did the solution's deployment in-house. 

The solution is worth its money.

I would rate the solution's pricing a nine out of ten. The tool costs around 3000 dollars per month. There are no additional costs apart from these.

I would rate the product an eight out of ten. 

I use the solution to orchestrate different containers that need microservice architecture.

The product’s dashboard is very intuitive. The solution is very useful for monitoring.

The solution does not have a visual interface. 

The solution could be improved by adding some visual drag-and-drop features.

I have been using the solution for the past three years.

The solution is very stable.

The solution is very scalable.

The initial setup is a little bit complex.

The deployment can be done in one to three months.

The product is a little bit expensive.

The solution is cloud-based. There are more tools available that are more visually intuitive. Overall, I rate the solution a nine 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. 

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.

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.