Google Kubernetes Engine Reviews

My main use case for Google Kubernetes Engine is that it best suits web-based applications, and it can have its own ingress controller when we go with the enterprise.

Google Kubernetes Engine can manage web applications such as APIs, front end, back end, and multiple workloads. When we have the managed Google Kubernetes Engine, the need for updating the cluster or the ingress controller is not needed, which eliminates the hectic process of managing updates that could cause downtime at the cluster level. It is advantageous to have the managed Google Kubernetes Engine with their specialist team managing it.

Google Kubernetes Engine has positively impacted my organization in migration-based projects such as on-prem to GCP and AWS to GCP, as we migrated more than thousands, even 10,000s workload in Google Kubernetes Engine. The clients notice that post cutover or after one month once their workload is stable in the Google environment, the costing is lesser compared to managing it in the on-prem instances. Additionally, Google frequently provides discounts for specific machine types, which clients appreciate.

Managing is also easier; unlike on-premises setups that require a large team of separate server engineers and hardware managers, GCP saves nearly 100 to 200 people's efforts.

The best feature Google Kubernetes Engine offers is its monitoring feature, which is valuable because whenever I spin up the cluster, the workload may or may not run as expected. Their logging feature is fantastic and helps me debug those applications quickly.

When I deploy the workload, by default the logs are enabled, allowing me to catch the error logs in the UI itself. If I indicate STDOUT or have an error snippet or code change, then I don't need to worry about anything.

Google Kubernetes Engine can be improved by enabling the in-place upgrade of the machine type of an existing node pool since I currently need to destroy and recreate it. There is no feature present where I can upgrade directly, and having more than 1,000 to 2,000 workloads in one node pool makes changing the node pool name difficult for all those workloads.

I choose eight out of ten mainly because of the node pool upgrade challenge I mentioned, but also because of the existence of Anthos service mesh, which is the ingress controller available only for the enterprise Kubernetes Engine. It would be beneficial if it could be offered in the normal Kubernetes Engine with any limitations.

I have been working in my current field for more than four years.

Google Kubernetes Engine is stable in my experience, but stability is dependent on the machine type I choose. If I have 100 replicas of containers and the traffic suddenly pushes to 1,000 within a fraction of a second, the selected machine type must be fast. A suitable option would be N2D or beyond, which can onboard all the workloads quickly; however, using lower-cost machine types such as E2 may cause downtime.

My experience with pricing, setup cost, and licensing for Google Kubernetes Engine is straightforward, as I previously indicated. The need for humans is reduced with GCP since there is no need for large teams to manage physical on-premises. Instead, we only pay for the hardware we use, which results in cost-cutting.

My advice for others looking into using Google Kubernetes Engine is that if you have tons of workloads and want to orchestrate them with fewer DevOps, consider Google Kubernetes Engine.

I am not a partner; I am simply a DevOps professional working for a client who wants to migrate their on-premises to GCP.

On a scale of one to ten, I rate Google Kubernetes Engine an eight.

I use Google Kubernetes Engine for many purposes, most notably for websites, but the websites are microservices. However, some mega projects have many machines that needed to be containerized, so we are using Google Kubernetes Engine for autoscaling and managing. We are providing Google Kubernetes Engine services, making autoscaling, deployment with many replicas of the containers.

Google Kubernetes Engine is helping tremendously to make the high availability of the service. The best advantage of Google Kubernetes Engine is that it manages Kubernetes for you, making everything related to the master nodes managed for you with automatic updating and upgrading. It is easy to integrate services related to compliance, network things, and network compliance.

The price could be a bit cheaper. I don't see anything with Google Kubernetes Engine that needs to be improved. I think they are already implementing Kubernetes itself, so they are the owners. However, they are not perfect, which is why I gave it a nine. There are very small things, but they are not marked as problems that need to be improved.

I have been working with Google Kubernetes Engine for one year and a half.

The current business contact is amr.ehab@solutionsbystc.com. I changed the company. I am still a Solution Architect in the company.

Google Kubernetes Engine has built-in services. I worked with Ingress and API gateways. We manage all the clusters, create all the services, and manage the load balancers and Ingress. We also integrate the load balancer in the cloud with Google Kubernetes Engine cloud load balancer. At the same time, we can use some CRIs from the services themselves, integrating or installing Ingress or installing Gateway API to handle the traffic. We can do both. We also make the rules and network policies inside the cluster, manage service accounts. I don't see a disadvantage related to Google Kubernetes Engine. Most of the developers wouldn't remember any disadvantages. I don't use any integrations, but I think Google Kubernetes Engine is open to integrate anything, though I haven't tried integrating anything with Kubernetes. My overall rating for this product is 9.

Negative

We use Google Cloud's storage because our solution needs persistent storage. The application itself runs in Google Kubernetes Engine (GKE) where we run containers that require some form of storage to store their data, such as databases and uploaded files.

It allows for cost optimization. Our product must handle varying loads effectively, and the auto-scaling feature ensures that we only use resources when needed, aiding both in performance and cost savings.

The most valuable aspect of Google Kubernetes Engine (GKE) is its managed nature, which significantly reduces the burden on our platform team. 

Features like auto-scaling are highly beneficial for both handling traffic spikes and optimizing costs. 

Furthermore, Google's promise of good SLA availability, with the service being available in different locations, adds to its robustness.

The management UI could be improved. When looking at the web interface, it feels kind of slow due to the many features involved. The interface could definitely be faster.

I have been working with Google Cloud Storage for about two years.

I would rate the stability as ten out of ten.

Scalability is great since hyperscalers like Google Kubernetes Engine must be good at scaling to stay in business.

Initial setup is very straightforward and simple.

For pricing, Google is considered cheaper compared to AWS, making it suitable for smaller to medium companies concerning cost. I would rate the cost around four out of ten, where ten is the higher price.

AWS and Microsoft Azure are the main competitors, with AWS being more expensive. Other smaller competitors include DigitalOcean, Hetzner, and Tencent.

I rate Google Kubernetes Engine a nine out of ten. 

I highly recommend it to others due to its comprehensive features and cost-effectiveness for various company sizes.

I use it for my company, Enterprise Solutions, to host our applications and gateway.

GCP is easy to use, which benefits newcomers, even those without a technological background.

GKE is easier to understand and use than Elastic Kubernetes Service. Its functionalities, such as computing the namespaces, clusters, pods, and restart logs, are easy to use.

Log observability could be made easier so someone from high school can use it without having technological expertise.

I have worked for four years.

The engine is stable, and we didn't find any issues.

I earlier worked on Elastic Kubernetes Engine. I switched to GKE.

I'd rate the solution seven out of ten.

We use the solution for running microservices for different healthcare workflows like authorization claims and adjudication with no downtime and to do continuous deployments.

Earlier, we used an on-premise solution, so we faced downtimes. It was a monolithic architecture. Google Kubernetes Engine provides no downtime deployments. Everything is scaled well.

The product has no downtime. Automated scaling is a valuable feature. During peak hours, the datasets are on a higher volume. We need scaling in place. Otherwise, there's a degradation in the performance. We might sometimes miss data, or there will be no data sync between systems. Auto-scaling helps deal with performance needs during peak hours. There's no lag time for processing data.

The notifications are not informative. It's a little confusing at times.

We have been using the solution for more than a year now.

The tool’s stability is good.

The tool has good scalability. I rate the scalability a nine out of ten. We have more than 150 users in our organization.

The support is not that great. I rate the support a three or four out of ten. Amazon provides better customer support through chats and emails. Google must improve its support.

Negative

The initial setup is straightforward. We can manage the maintenance.

Pricing is always a concern. We keep running the service, and we need to pay for it. I rate the pricing a seven or eight out of ten.

We also use Google’s storage solution. It is similar to Amazon S3. I recommend the product because Google has good infrastructure and service management overall. It is a big brand but has fewer services than AWS. The vendor is fighting hard to reach a status where they get more services and get more involved with AI. We have a partnership with Google. We resell the solution. Overall, I rate the product a seven 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.

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.