Google Kubernetes Engine Reviews

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 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.

We use this solution to handle big data workloads in that specific NGK.

The solution is the latest and greatest. It has everything in it. You can have service and everything that you need.

There is room for improvement in terms of visibility and observability of solutions. One of the things I missed a bit is the visibility and availability of solutions. If I compare it to a different solution, it is a bit behind.

I have been using this solution for three years. We use the tool’s latest version; it was Kubernetes V1.24

The solution is quite stable.

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

It is a scalable solution. We are using this solution for small enterprises.

I rate the solution’s scalability a ten out of ten.

The customer service and support are not too good.  Usually, the vendor acts as the middleman for support on GK, and it's not good. 

They should be more professional.

Neutral

We used automation for the initial setup. It was okay. So it wasn't too complex.

With automation, the deployment took about a week. But if you do it manually, it can take even just minutes or hours.

The deployment was done in-house with the help of one DevOps engineer. 

Maintenance is required for the solution. Moreover, maintenance is a bit of an issue because we don't have good observability for those issues, so I'm less positive about that. It was hard to maintain. Only one person is enough for the maintenance.

The pricing is the same, as far as I recall. So it's the same pricing. It depends on the compute pricing, so the pricing is relative. 

It's a monthly solution for the Control Plane, and pay-as-you-go for the workers, and the compute — it depends on how you define it. I don't think there are any extra costs. It is only pay for the Control Plane, and that's it.

I worked with AWS, specifically EKS (Elastic Kubernetes Service) and Postgres.

In brief, I would say that Amazon's support is better. The solutions are quite equal, but there is better integration for EKS with AWS services.

My advice to others looking to use Google Kubernetes Engine is to pay attention to maintenance and utilize automation and platform tools to build and manage it. Do not do it manually. 

Many maintenance errors should be managed automatically, especially by Google. Otherwise, you need to be very attentive to the cluster due to maintenance issues. I suggest automating most of the manual tasks. There is a distribution of Google called Autopilot that handles it, although I haven't personally worked with it. I recommend considering its usage rather than doing everything manually. Maybe it's related to the specific solution we chose, but ultimately, it's about pricing.

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

I am using Google Kubernetes Engine as a customer primarily for test cases and self-research. I mainly use their VMs with the free credits they have provided, and I am learning more about DevOps technology with DevOps tools integration with the Kubernetes clusters they provide with their free trial credits.

Google Kubernetes Engine is a cloud platform where I can host any applications for containers. It is a reliable cloud platform for hosting VMs and containers on the Kubernetes platform.

In terms of scalability, Google Kubernetes Engine is very convenient. Going through their portal to scale based on machine resource requirements is straightforward. It is convenient to add nodes.

The installation process for Google Kubernetes Engine is not stressful at all.

Regarding deployment in the cloud platform, it is simple because there are pre-configured configurations. All I have to do is select how powerful my machine needs to be, and they provide the business end. The entire process is stress-free.

I have no comment about the learning curve of Google Kubernetes Engine.

Regarding AI integration and features in Google Kubernetes Engine, there are currently none available.

I would appreciate seeing AI features added to Google Kubernetes Engine in the future. One potential feature could be AI scanning for configuration errors. This could help inexperienced users who might have trouble configuring their platform, acting as a guidance system.

I have been working with Google Kubernetes Engine for two years, though not very frequently.

Regarding stability with Google Kubernetes Engine, it is based on regions. While it can be region-dependent, I have not experienced any stability issues.

The most challenging aspect of Google Kubernetes Engine depends on one's understanding of the infrastructure side, particularly how to tune a particular machine to specific needs. It should not be problematic beyond one's experience level.

I have not used any other cloud-providing Kubernetes Engine solutions for comparison.

There are definite savings from Google Kubernetes Engine, particularly in terms of time management. I would estimate the savings to be around 40 percent.

Google Kubernetes Engine solution is expensive, as are all cloud solutions in general. On a scale of one to ten for pricing, I would rate it between seven and eight.

I have not used any other cloud-providing Kubernetes Engine solutions for comparison.

I currently have no specific disadvantages to report about Google Kubernetes Engine. I prefer to be called JD, which stands for Jedidiah. On a scale of one to ten, I rate Google Kubernetes Engine an 8.

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.