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.
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).
Pricing is a bit expensive compared to some other products, but it's acceptable. I would rate the pricing an eight out of ten, where one is a low price, and ten is a high price.
Consultant at a manufacturing company with 10,001+ employees
Real User
Top 5
2023-07-18T08:33:50Z
Jul 18, 2023
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.
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.
The pricing is average. For example, Tanzu Build Service is very expensive, but generally, it's okay. However, I understand that the on-to service is very expensive.
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.
Solutions Architect at a tech services company with 11-50 employees
Real User
Top 20
2022-08-15T13:03:44Z
Aug 15, 2022
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.
Multi-cloud is a sort of an expensive endeavor as the tools are overpriced. We're looking at options that aren't based on Anthos, which is Google's multi-cloud solution. While you pay money to Google, they also take a piece of the action as well. CPU is very cheap, however, GPU is very expensive. If you want to iterate on your data client's tasks within a Kubernetes cluster, it will cost you. There is no licensing cost. You pay for the cloud and you pay for what you use based on the CPU and RAM usage based on the VM, the virtual machines. The cluster is still made up of computers, so you pay for the computers that are backing the clusters. If you kick off a Kubernetes node, which has three nodes in your cluster, you have to pay for each of these nodes, these computers, these virtual machines that you get bootstrapped with. You just use the machine time as with any cloud and you get a price in Google for the machine type and your machine type is defined based on your CPU and RAM usage. If you want to have 60 GBs of RAM, you pay for that RAM or for CPUs. The same thing is true if you ask for a GPU computer, as most of the virtual machines don't come with a video card unless you ask for it. Then you have to pay for that the computer and the video card
Kubernetes Engine is a managed, production-ready environment for deploying containerized applications. It brings our latest innovations in developer productivity, resource efficiency, automated operations, and open source flexibility to accelerate your time to market.
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.
The solution was pricey. If you have options, consider OpenShift as another platform that does the same.
The tool's licensing costs are yearly.
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 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.
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).
Pricing is a bit expensive compared to some other products, but it's acceptable. I would rate the pricing an eight out of ten, where one is a low price, and ten is a high price.
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.
The solution's price is reasonable.
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.
The product is a little bit expensive.
The pricing is average. For example, Tanzu Build Service is very expensive, but generally, it's okay. However, I understand that the on-to service is very expensive.
Its pricing is good. They bill us only per user. That's nice.
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.
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.
The price for Google Kubernetes Engine could be lower - I'd rate its pricing at three out of five.
I do not know specific details about the price, but I believe Google Kubernetes Engine is cheaper than Pivotal Cloud Foundry.
I would rate Kubernetes' pricing four out of five.
Google offers yearly and monthly subscriptions.
Multi-cloud is a sort of an expensive endeavor as the tools are overpriced. We're looking at options that aren't based on Anthos, which is Google's multi-cloud solution. While you pay money to Google, they also take a piece of the action as well. CPU is very cheap, however, GPU is very expensive. If you want to iterate on your data client's tasks within a Kubernetes cluster, it will cost you. There is no licensing cost. You pay for the cloud and you pay for what you use based on the CPU and RAM usage based on the VM, the virtual machines. The cluster is still made up of computers, so you pay for the computers that are backing the clusters. If you kick off a Kubernetes node, which has three nodes in your cluster, you have to pay for each of these nodes, these computers, these virtual machines that you get bootstrapped with. You just use the machine time as with any cloud and you get a price in Google for the machine type and your machine type is defined based on your CPU and RAM usage. If you want to have 60 GBs of RAM, you pay for that RAM or for CPUs. The same thing is true if you ask for a GPU computer, as most of the virtual machines don't come with a video card unless you ask for it. Then you have to pay for that the computer and the video card
Currently, it costs around $1000 per month which sorted our deployment. So once we get more clients, having a huge suffix, costs can go up.
This is an open source solution, so there is no pricing or licensing.
We are planning to use external support, and hire a commercial partner for it. Usually, this is about twenty percent of the solution.