The product pricing depends on the specific requirements. For instance, clients between $3000-$4000 per month might find the pricing reasonable, with clusters priced around $70 to $80 plus additional costs. However, the actual pricing can vary based on the number of services utilized.
The product is available at such a huge scale in the market since the resources that are offered under the tool are competitively priced and available at a much cheaper rate compared to other solutions.
The solution is cheaper than AKS. Its license costs $73. The customers have to pay extra depending on the instances they want to deploy for hosting the application. I rate its pricing as a four.
The solution is quite costly and developers will start exploring other solutions or moving their workloads to other clouds if costs aren't reduced. Our client pays the licensing fees so I don't have specifics about its cost, but I hear that the solution is expensive so I am rating it a five out of ten.
Cloud Architect & Devops engineer at KdmConsulting
Real User
Top 5
2022-07-09T03:27:16Z
Jul 9, 2022
The calculation of the pricing is dependent upon instance type. So when we make a cluster without defining any instance type, it will default enter a large instance type. So as per our requirement, we can create our node group and define our instance types per our workload.
Practice Director, Global Infrastructure Services at Wipro Limited
Real User
2022-01-19T11:20:19Z
Jan 19, 2022
We already have costs built into the service given by the particular vendor. If it is on-premise, we buy the software, and we pay some support costs and license costs. However, if it is on the cloud, it is a pay-per-use model.
The solution offers different pricing models. They charge in different ways - either per CPU hour or usage based on a machine type. When it comes to pricing, Google may be two cents cheaper, whoever, the difference makes it a bit of a wash. It might mean an extra five dollars or 20 dollars a month. The difference isn't enough to be too noticeable. All of the main competitors charge very competitive pricing. That said, when it comes to the CPUs, that's a Google proprietary technology. When we do machine learning, we do prefer working in Google Cloud, as we have the option to expand all the way to CPU and AWS doesn't have that option. It's a GPU-only system. Amazon's also pushing you towards their own machine learning tool, SageMaker, which we don't want to use. We want to use our own tool.
Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed Kubernetes service. Customers such as Intel, Snap, Intuit, GoDaddy, and Autodesk trust EKS to run their most sensitive and mission critical applications because of its security, reliability, and scalability.
EKS is the best place to run Kubernetes for several reasons. First, you can choose to run your EKS clusters using AWS Fargate, which is serverless compute for containers. Fargate removes the need to provision and manage...
The product pricing depends on the specific requirements. For instance, clients between $3000-$4000 per month might find the pricing reasonable, with clusters priced around $70 to $80 plus additional costs. However, the actual pricing can vary based on the number of services utilized.
The product is available at such a huge scale in the market since the resources that are offered under the tool are competitively priced and available at a much cheaper rate compared to other solutions.
Amazon EKS is not a cheap solution.
Amazon EKS is expensive.
The tool's pricing is good.
The solution's pricing is fair enough and a little less costly.
I rate EKS seven out of 10 for affordability. Amazon EKS costs us $730 a year.
The solution is pricey. The tool's pricing is monthly.
The solution is cheaper than AKS. Its license costs $73. The customers have to pay extra depending on the instances they want to deploy for hosting the application. I rate its pricing as a four.
Amazon EKS is very cost-effective. I rate the pricing a ten on a scale of one to ten.
Amazon EKS has fair pricing. It's better in terms of pricing than other platforms.
The price could be cheaper. I would rate it as seven out of ten. There are no extra costs in addition to the standard license.
It's a subscription and depends on the cluster nodes and other effects. There are many calculation criteria for licensing.
The solution is quite costly and developers will start exploring other solutions or moving their workloads to other clouds if costs aren't reduced. Our client pays the licensing fees so I don't have specifics about its cost, but I hear that the solution is expensive so I am rating it a five out of ten.
The calculation of the pricing is dependent upon instance type. So when we make a cluster without defining any instance type, it will default enter a large instance type. So as per our requirement, we can create our node group and define our instance types per our workload.
My company paid for the license.
We already have costs built into the service given by the particular vendor. If it is on-premise, we buy the software, and we pay some support costs and license costs. However, if it is on the cloud, it is a pay-per-use model.
The solution is more expensive than other competitors and does not require a license.
The solution offers different pricing models. They charge in different ways - either per CPU hour or usage based on a machine type. When it comes to pricing, Google may be two cents cheaper, whoever, the difference makes it a bit of a wash. It might mean an extra five dollars or 20 dollars a month. The difference isn't enough to be too noticeable. All of the main competitors charge very competitive pricing. That said, when it comes to the CPUs, that's a Google proprietary technology. When we do machine learning, we do prefer working in Google Cloud, as we have the option to expand all the way to CPU and AWS doesn't have that option. It's a GPU-only system. Amazon's also pushing you towards their own machine learning tool, SageMaker, which we don't want to use. We want to use our own tool.