Google Kubernetes Engine Reviews

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.

It is good for testing and training purposes. 

In terms of integration with other Google Cloud services, I use specific patterns like event-driven architectures or middleware solutions such as Kafka for system integration.

In my experience, GKE is a good platform for managing microservices on the cloud. It allows you to easily deploy and manage containers with databases.

The auto-scaling feature in Google Kubernetes Engine is very effective. When there's increased traffic on a microservice, it automatically duplicates pods to handle the load, enabling seamless scaling. This capability ensures zero downtime, significantly enhancing workflow management.

The pricing could be more competitive. It should be cheaper. 

I have been using it for a year. 

Stability is perfect for me. 

It supports both horizontal and vertical scaling seamlessly across various cloud platforms like AWS, Azure, and Google Cloud, making it a reliable choice for scalable applications.

The customer service and support are good.

There is also the AI support. There were instances when I needed help with configurations, and the AI support was instrumental in validating the correctness of my setups. It also provided good information on pricing and even helped estimate costs through AI. 

My experiences with Google Cloud Platform's AI support have been highly successful and satisfactory.

Positive

Before using Google Kubernetes Engine, I explored OpenShift and Cloud Insight Engine.  

The setup is straightforward, I've encountered challenges when attempting to integrate certain services directly. However, creating clusters in Google Cloud has generally been a smooth process for me.

I did it myself. I am a developer. Sometimes, I give consultation to other people as well. 

Deployment's complexity and time can vary, but for me, it involved setting up the necessary environments and deploying Kubernetes clusters to manage microservices. The process duration can depend on the specific requirements of the deployment.

The value of investment in Google Kubernetes Engine can be substantial, especially for startups. However, the costs associated with databases, Kafka, microservices, API gateways, and programming can add up, posing a challenge for startups without significant funding because they are big money. 

Although GKE Autopilot can offer some relief in costs, startups need to carefully consider their options and potential expenses.

The pricing can be high for small deployments. For example, it costs $90 per month for a small instance.

Another option available is GKE Autopilot, which can be more cost-effective for certain use cases, though the pricing is private and may vary.

The pricing could be cheaper.

In Google Cloud, there are different licensing options, but the specifics of payment aren't clear to me. 

There are two primary models: one is more cost-effective but offers limited control over nodes, and the other allows more comprehensive control over nodes but at a higher cost. The standard cluster model tends to be less affordable.

For me, it is a good solution. 

For new users, I recommend starting with GKE Autopilot because of its cost-effectiveness compared to standard clusters. It's a more affordable option for those beginning with Google Kubernetes Engine.

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

We use Google Kubernetes Engine, usually for containerizing.

Google Kubernetes Engine has helped our organization to deploy classes faster.

The most valuable feature of Google Kubernetes Engine is how you can automatically scale and load balance.

Google Kubernetes Engine's cost should be improved because it is high.

It would help if Google Kubernetes Engine could be made more customizable.

I have been using Google Kubernetes Engine for three years.

Google Kubernetes Engine is a pretty stable solution.

I rate Google Kubernetes Engine an eight or nine out of ten for stability.

Google Kubernetes Engine's scalability is pretty high.

I rate Google Kubernetes Engine a nine out of ten for scalability.

The solution’s initial deployment is pretty easy.

Our clients have seen a return on investment with Google Kubernetes Engine, which is diminishing because competitors now have better products.

I am using the latest version of Google Kubernetes Engine.

Google Kubernetes Engine is deployed on-cloud in our organization.

I would advise users to pay attention to the vendor lock-in. Sometimes, they reduce access to infrastructure. I would pay much attention to implementing the solution to have better visibility of the performance issues, especially concerning direct access to virtual machines.

Google Kubernetes Engine is pretty simple to use. It is pretty good as long as you use the solution within Google Cloud.

The solution's ease of use comes with the cost. Whoever wants to use the solution must weigh what's more important to them: the cost they'll pay to Google or the cost they'll pay their staff to make it work as well as they need.

Overall, I rate Google Kubernetes Engine an eight out of ten.

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.

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. 

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 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.