Google Kubernetes Engine Reviews

We use it for deploying our applications. All our applications are based on Kubernetes, so we create our products with Kubernetes.

I find Google's services very stable, and I appreciate some of the unique features it offers, like the ability to virtualize the database and access detailed analytics, which simplifies management.

Its main advantage is the technology itself, which allows our applications to scale easily. This scalability reduces downtime significantly.

I started using it when I joined the company. Initially, I was more familiar with things around Azure, but Google Kubernetes Engine was my first experience with Google’s cloud services when I joined MoniePoint. I contacted Google and learned about the competitive cloud market with AWS, Azure, and Google. 

I would rate the stability an eight out of ten.  We hardly have a breakdown. It's been very stable.

We, the developers, do experience some downtime occasionally, but we are relatively new to it.

I would rate the scalability a seven out of ten. 

I used Azure. The switch to Google Kubernetes Engine was due to a change in my employment. I started using Google when I joined this new place last year. It's a very efficient tool. 

The DevOps team takes care of this aspect.

Overall, I would rate the solution a seven out of ten. It's worth trying out.

I would recommend Google as a cloud service option. I wasn't aware of how good it was initially, but having tested it, I see that it's very efficient and good. We hardly have any issues; so, it's very efficient and good.

I'm using a different infrastructure-as-code engine, Terraform, to create Kubernetes clusters. I specify the machine type and memory requirements in my Terraform configuration, and Terraform sets up the network. With Google Kubernetes Engine (GKE), Google manages the Kubernetes control plane, so I only need to focus on creating and managing nodes. Currently, I'm creating pre-node Kubernetes clusters, including private clusters for security. Workloads can be deployed to GKE using YAML files or the Kubernetes CLI. To expose deployments to end users, I create load balancers. I use cluster autoscaling and HBA host port autoscaling to automatically maintain my workloads at the desired size. GKE also provides various options for load balancing, including ingress. QoS handles credentials using secret resources, and configuration is done using ConfigMaps. The main workflow is to create deployments, ports, services, secrets, and configuration maps.

Workloads are automatically manageable, and there's a cluster autoscaling option in Google Kubernetes Engine. It also supports HBA host port autoscaling, maintaining ports at the desired size. You can create a load balancer for different types of service access using ingress. QoS handles credentials with secret resources, and configuration is done through ConfigMaps.

So, autoscaling is the most valuable feature. 

I would like to see the ability to create multiple notebook configurations. In a cluster, we can create multiple notebooks, which means multiple machine configurations. This would be better because if we have a job that requires high CPU, then we can have a notebook available for that job with a high CPU machine type. 

And if we have a job that requires high memory, then we can have a notebook available for that job with a high memory machine type.

I have experience using this solution. It's been six to seven months now.

Google Kubernetes Engine is very stable.

There's no issue because if I face problems, I just Google it, and I find the solution.

I have previously worked with Docker. I have created and deployed containers using Docker and Docker Hub.

GKE is a managed Kubernetes service that runs on Google Cloud Platform (GCP). It makes it easy to deploy and manage containerized applications on GCP.

You can deploy workloads to GKE using YAML files or the Kubernetes CLI.

The initial setup is very easy. 

We can create our cluster using the command line or using our console.

First of all, you have to provide the name of your cluster. And you have to create your default notebook according to your workload. And if you have to provide, if the cluster is either private or public, and the other things that you need to add is like a cluster networking. The security section is also implemented. You have to create to mention if the cluster can be delectable. There's an option for specific, enable, and delete protection.

So, with all these configurations set up using the console or command line, you can either click to create or just hit the command, and your cluster will be deployed on your platform.

Google Kubernetes Engine requires some maintenance. However, most of the maintenance tasks are handled by Google Cloud. For example, Google Cloud will automatically patch the Kubernetes Engine nodes and apply security updates.

Kubernetes is an open-source project, so there is no licensing cost. However, there are costs associated with running Kubernetes in the cloud, such as the cost of the compute resources and the cost of the managed service (if you are using a managed Kubernetes service like GKE).

I have worked with App Engine and Cloud Functions. I recently learned about the Data Flow service, which allows you to move data from one source to another in real-time or batch mode. For example, you could use it to count the number of times each word appears in a textbook. You can save the results of your data flow to a Cloud Storage bucket.

Dataflow is a powerful tool for processing large amounts of data. You can also use Dataflow to save your results, such as text or documents, to a cloud storage bucket.

When you run a Dataflow job, Dataflow will process the data from your source, such as a Cloud Storage bucket, and store the results in a bucket that you specify. If you have a real-time data processing need, such as tracking the location of a taxi, you can also use Dataflow to create a real-time streaming pipeline.

Those who want to implement their workload in Kubernetes can create it. It's automatically scalable. So you don't have to maintain your service. It will be automatically adjusted based on your workload and needs.

The other thing is, when you are using microservice kind of development, like, now it is the programming language for microservices. So when we use microservices, it can be easily managed using Kubernetes. It makes it easy to find an error because the solution is really helpful. 

And if microservices, the whole application won't fail. Just the deployment notes, that may cause an error in our application. That's the only failure. The whole application won't fail. So it would be helpful. You have to use a microservice kind of development in your development environment and try to implement it as a container and delete the container workloads in Kubernetes. Using deployment or domain service, and our project will be automatically maintained.

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

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.

I have deployed the solution as a service into my private cloud, as well as into Azure infrastructure.

Google Kubernetes Engine is useful for cloud-native business applications, especially microservices-based architectures since business applications require scalability and resilience and must be highly available. Typically, Google Kubernetes Engine is used to deploy business applications and also to manage the integrations with the cloud services. There are a lot of SaaS solutions as service offerings provided by the Google Cloud Platform, so it helps with the integration to compile the solutions in the business space or, basically, the cloud-native space.

Google is the founder of Kubernetes. Google invented Kubernetes platform, and then later on, at some point in time, they had open-source scripts. Since Google started offering open-source scripts, a lot of other players in the market have adopted the same strategy for their products. Google Kubernetes Engine made a late entry into the market, but the good part that I like about it is that you have a seamless way of creating highly scalable and highly available solutions with it. On the tip of a command, you can scale in or scale out, and it offers every robust platform to implement DevOps processes for your automation solutions. The product fully supports the IaC concept.

The product's stability is an area of concern where improvements are required. Google Kubernetes Engine needs to mature more to be able to offer more stability compared to its competitors like Amazon and Microsoft, which have allowed for aggressive growth due to the area covered in terms of offering the complimentary services offered around Kubernetes. Google is behind even though it offers a robust platform. Google needs to offer more intensive services to fulfill the needs of customers and serve as a one-box or one-stop solution that covers everything enterprises need. Google needs to evolve more in terms of the richness of the services offered.

I haven't worked full-fledged with Google Kubernetes Engine. since I have used it from an experimentation perspective. It is mainly Kubernetes that I have worked for for more than three years. Google Kubernetes is basically a managed service, but Kubernetes 1.24 is what I have extensively used.

Stability-wise, I rate the solution a seven out of ten.

Scalability-wise, I rate the solution a six to seven out of ten.

My company's clients who use the solution are enterprise-sized businesses.

I have no experience with the solution's technical support because I have not done any production-related work revolving around Google Kubernetes Engine. I believe that Google provides better technical support, but I don't have the relevant background to speak about it.

I rate the product's initial setup phase a five or six on a scale of one to ten, where one is a difficult setup phase, and ten is an easy setup phase.

The concept revolving around Kubernetes is inherently complex when it comes to its setup phase. Kubernetes is not an easy domain to design and operate. The adoption of Kubernetes from a production perspective can be a bit of a complex task, not only for Google but for every other vendor in the world. The complexity is not much for a person who understands Kubernetes. With Google, considering its lack of maturity compared to the competitors, I will say the complexity during the setup phase may be a bit higher, based on which I rate the setup phase a six out of ten.

The solution is deployed on a public cloud by Google.

The solution can be deployed in a week since the major effort is in building the application, and getting the application to the production stage takes at least a week or so.

From an ROI perspective, the tool should be better, but since I have not done any production-grade application deployment on Google Kubernetes Engine, I won't be able to comment on the product's ROI part. As per my understanding, the overall Kubernetes ecosystem ensures a high ROI because it promotes the ability of a business, and that's what the platform is all about.

I rate the product's price a six on a scale of one to ten, where one is low price and ten is high price. The product is competitively priced.

I rate the overall product a seven 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. 

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.