Google Kubernetes Engine Reviews

We primarily use Google Kubernetes Engine for hosting applications. We use it for hosting our microservices-based applications.

Google Kubernetes Engine (GKE) takes care of managing Kubernetes, including the main control plane. It also offers solutions for monitoring resources and handling external traffic, which is essential for us. Being a cloud-native solution, it relieves us from worrying about these operational aspects. Specifically, I can use infrastructure-as-code with tools like Terraform to provision clusters efficiently. Additionally, GKE enables us to use GitOps for application deployment.

The most valuable feature is that GKE manages the control plane. Control Plane management is a good feature. 

There is room for improvement in the cluster updates process. Specifically, when managing both non-production and production clusters, we need a sequential functionality. This means being able to upgrade non-production clusters first and then the production clusters. Having this sequential upgrade capability would be beneficial.

Therefore, I am looking for a sequential functionality for cluster upgrades.

I have been working with this solution for two years. I am currently working with version 1.25.

I would rate the stability an eight out of ten.  Though it's highly stable, sometimes it takes time for Google to provide support.

It is a highly scalable solution. I would rate the scalability a nine out of ten. It doesn't have any limit to endpoints. 

We currently have around 300+ endpoints for the applications hosted on it. We use this solution extensively and 24/7.

The support takes longer to response. There is room for improvement in the response time.  

Positive

Before Google Kubernetes Engine, we used OpenShift. We switched to Google Kubernetes Engine because OpenShift's support and complexity were causing challenges in maintaining stable impressions. Kubernetes evolved from Google and provided better stability, leading to the switch.

I would rate my experience with the initial setup a nine out of ten, where one being difficult and ten being easy. It is very easy to set up. It took just a couple of minutes. 

I needed to deploy using infrastructure as code, specifically with Terraform. It was done in-house; we deployed using Argo CD and Flux CD. It's a self-service deployment.  

Developers can deploy the solution themselves. We don't require anyone in between. However, the number of people required for maintenance depends on how many clusters we have. As of now, we have around five DevOps practitioners responsible for maintenance.

As for the return on investment, I would not be able to conclusively determine that. However, from a cost-saving perspective and the efficiency it brings, we have likely saved money and required fewer employees to manage the solution.

It has been efficient in that regard. I would say the ROI has been around 60%.

I would rate the solution's pricing a six out of ten, with one being low price and ten being high price. 

There are no additional costs. It's based on usage and the number of nodes and applications deployed.

I have experience with Kubernetes, Jenkins, CI/CD solutions, and GitOps. We evaluated other cloud providers, but we ultimately finalized and switched to Google Kubernetes Engine.

My advice would be to go for Google Kubernetes Engine if they seek stability, a well-tested product, and a reliable solution. It's suitable for those who value those qualities.

Overall, I would rate the solution an eight out of ten.

The product helps us to manage Docker easily using automation. 

The solution simplified deployment, making it more automated. Previously, Docker required manual configuration, often done by developers on their computers. However, with Google Kubernetes Engine, automation extends to configuration, deployment, scalability, and viability, primarily originating from Docker rather than Kubernetes. Its most valuable feature is the ease of configuration. 

The tool's configuration features need improvement. 

I have been using the product for two years. 

We had some stability issues in the past. I rate the tool's stability a nine out of ten. 

I rate the solution's scalability a ten out of ten. Google Kubernetes Engine has around 100-200 users in my company. 

Google's support is good and fast. It's available 24/7. 

It will take some time for someone to get used to it, and there's a learning curve that shouldn't be skipped or neglected. But then, things will start to click, and you'll notice that the product is easy to deploy. The deployment setups are readily available from Google or Microsoft. You need to configure them, which can be done with these scripts and by automating your CI/CD processes. It's all interconnected with CI/CD.

Google Kubernetes Engine can be deployed in-house. 

The tool's licensing costs are yearly. 

The inter-system communication, including the ports used, is all described within Docker. The product manages these Docker pieces and builds the bigger picture. 

We integrate it as part of our DevOps script. It's all connected, with actions for the desktop, the CD Engine, and deployment on managed Kubernetes instances on Google Cloud. It's all automated and works well together.

I rate the overall product a nine out of ten. 

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.

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.

We use the platform for transforming our product from VM-based to container-based. It involves migrating old monolithic applications to containers, which takes years.

One valuable feature of the product is openness to global networks, which allows for integration and deployment across multiple regions, which is only sometimes possible with other cloud providers.

The product's integration with third-party vendors needs improvement. 

I have been working with Google Kubernetes Engine for approximately two to three years.

I rate the product stability an eight. 

I rate the product scalability an eight. 

The initial setup process is simpler and more user-friendly than other cloud providers.

Google Kubernetes Engine has made the deployment process easier than Amazon and Azure. It is a good product and often more cost-effective.

I recommend it to others and 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.

We use Google Kubernetes Engine primarily for our production clusters, running several microservices and main services. We have one main separate cluster for production testing, and for our actual production, we manage separate clusters.

Google Kubernetes Engine has helped us manage our infrastructure more securely, especially when separating projects and assigning permissions. This categorization enhances security as we streamline roles and permissions management.

The most beneficial feature is the ability to separate each project and manage permissions more effectively. This categorization is especially useful for security purposes. I find managing IAM roles in GCP to be better than AWS.

The primary area for improvement would be the complexity involved when working with Google Kubernetes Engine, especially when using Terraform. It can be more complex compared to AWS. 

Additionally, the process of managing IAM roles and integration with other Google services can be cumbersome and could use some simplification.

I have been using Google Kubernetes Engine for about one year to one and a half years.

We have not encountered any major stability issues with the Google Kubernetes Engine. Aside from the usual errors that occur day-to-day, such as image pull-back errors, we maintain a stable environment by using versions that are one or two versions behind the latest release.

The auto-scaling performance is really good in both GCP and AWS. I have not experienced any issues with auto-scaling capabilities, and they meet our demands efficiently.

Usually, our upper management takes care of any escalations to tech support, so I do not have direct experience with their customer service.

Neutral

The initial setup of Google Kubernetes Engine took me about two days. I primarily used Terraform scripts for deployment and testing.

Initially, when starting with Google Kubernetes Engine, I required some help, especially with configurations involving Helm charts and additional components such as the ingress controller. Once everything was set up, maintaining it became more manageable.

Google Kubernetes Engine has been cost-effective and has improved our operational productivity. However, GKE can be more expensive compared to AWS when it comes to certain services like Compute Engine. Integrating with multiple cloud providers is easier with GCP, making it a flexible solution for our diverse requirements.

I'm aware of the normal pricing, but it's not on top of my head. AWS is generally cheaper than GCP for most use cases. Costing fluctuates based on different purposes and sizes.

If you are using multiple cloud provider services, such as DNS management from DigitalOcean or S3 buckets from AWS, integrating with Google is simpler than AWS. For smaller functions, services like AWS Lambda can be more cost-effective than running them on GKE. It is important to utilize the proper tools for easy maintenance.

I'd rate the solution seven out of ten.