Google Kubernetes Engine Reviews

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.

In our company, the microservices are deployed into the containers in Google Kubernetes Engine.

Google Kubernetes Engine itself is quite a revolutionary concept. The scalability and reliability features of the product are fascinating, and they help a lot. With Google Kubernetes Engine, the area around deployment and the CI/CD implementation is easy.

The monitoring part requires some serious improvements in Google Kubernetes Engine, as it does not have very good monitoring consoles. If Google Kubernetes Engine comes up with monitoring consoles, that would be helpful.

During deployment, if the product provides users with an interactive kind of proper dashboard, which gives status or feedback on deployment, it will be helpful.

I have been using Google Kubernetes Engine for around two years.

I am satisfied with the stability offered by the solution. Stability-wise, I rate the solution a nine out of ten.

Google Kubernetes Engine is an industry leader. Scalability-wise, I rate the solution a nine out of ten.

More than 200 people in my company use the solution.

The use of the solution is increasing day by day in my company as Google Kubernetes Engine is the main platform we use for development. Our organization is rapidly growing, and new teams are being introduced.

Though I have not contacted the solution's technical support, through our company's regular channels, we keep watch for regular updates. If there is any escalation or ticket that was raised, our company gets good support from Google. I rate the technical support an eight out of ten.

Positive

I rate the product's initial setup phase a seven out of ten.

The solution is deployed on a hybrid cloud.

The time required to deploy the product depends on how many services you want to be deployed at a time in your environment. In general, deploying the product doesn't take much time since it can usually be done in ten to fifteen minutes. If everything goes smoothly during the deployment process, it won't take much time, and if something gets stuck, then it may require time for a person to deploy.

Initially, Google Kubernetes Engine was a little bit cheaper, but now its prices have been increased compared to the pricing model and the features that are made available by its competitors.

I recommend others try the solution and see if it suits and meets your budget. I recommend the solution to others because of the scalability, reliability, and ease of use it offers.

I rate the overall tool 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. 

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 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. 

We used to deploy microservices on the Java platform. It took a lot of work to manage these skill sets. With Kubernetes, it is easy to use and deploy. It was very convenient, mostly for scalability purposes.

The product could be cheaper.

I have been using Google Kubernetes Engine for one year.

The product is stable.

We started with 20 people who were migrating. Later, we migrated to other teams.

In the initial stage, It'll depend on our understanding level and how we've undergone training because we have a dedicated Engine. We were more inclined to learn something new. It is easy to use.

Google Kubernetes Engine integrates with your existing CI/CD pipelines. It's quite straightforward. Someone knowledgeable in DevOps helped develop a script for us. This script facilitated the integration of both pipelines with Kubernetes and streamlined the process for us.

The solution was pricey. If you have options, consider OpenShift as another platform that does the same.

We had a training session where they covered the features of Google Kubernetes Engine, allowing us to become familiar with all the commands that can be used in Kubernetes.

You won't need to have its feature to encrypt the data. Instead, a config map and secret feature are available. There's ample data recovery capability, particularly during operations like saving and performing other tasks. It's pretty impressive.

Google Kubernetes Engine provides certain features for free, especially for self-learning purposes. We install it on my local machine and deploy applications without incurring costs.

Overall, I rate the solution an eight or nine 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.