Amazon EKS Reviews

We have a service hosting platform, and we use Amazon EKS for hosting web services.

The most valuable features of Amazon EKS are its auto scaling ability and the ability for service discovery.

Amazon EKS's vulnerability management of data could be improved. Also, concerning vulnerability and version upgrades, many more things could be pulled into the control plane rather than keeping it in the data plane and having the users figure out how to upgrade their portions.

I have been using Amazon EKS for five years.

Amazon EKS has good stability.

Amazon EKS's scalability is very low. Quite a few people are using the solution in our organization.

We had no issues contacting Amazon EKS's technical support and usually got the necessary support. However, as with any Kubernetes installation, only the control plane is covered by Amazon, not the data plane. So if there are issues on the data plane side, Amazon cannot be of much help.

Amazon EKS’s initial setup was moderately easy. Amazon EKS's deployment was fast. We took the help of an automated deployment system, and the deployment was done in a couple of minutes.

We have seen a return on investment with Amazon EKS.

Amazon EKS’s pricing is ok compared to its competitors.

Amazon EKS is a good platform to use, but scale up before you start deploying in industrial quantities.

Overall, I rate Amazon EKS an eight out of ten.

The value is for me that it's a community product. We don't have to rely on the ECS services. 

What is new right now is Fargate. Fargate has the ability to abstract from the clusters. Amazon said that using the cluster is too complicated for people. Therefore, what they do right now is they have a service that sits on top of the cluster that doesn't even know it's a cluster. It abstracts it for you. Fargate is the ability to deploy it into the cluster, which specifies what you want to deploy and it takes care of the cluster provisioning and deployment for you. The tool just abstracts you away from the cluster, so you don't even know that you're using a cluster, which is good for people that don't want to learn the technology, the infrastructure.

The solution is easy to use. You don't have to care about the servers or the cluster. You really just say, I want to deploy this application A. You just find the application, click a button, and Fargate deploys it to a cluster for you.

They really want to get away from the idea that you made your own cluster. They really want to push you a bit higher up the layer, more of an abstraction layer.

Due to Kubernetes, it's easy to move between the clouds, to move those jobs, especially in multi-cloud systems right now. So one of the disadvantages of EKS is because of the technology they use for their machine learning right now and we prefer to have options, like CPU and Google.

When we switched to EKS, historically it wasn't good. There were issues with bugs in it. They didn't have managed pools, which means small subsections of the clusters that you divided into pools like a mini-cluster in your cluster. However, now they have managed pools.

For the last several versions, the issue was with their kind of networking plug-in, the security plug-ins, and things like that. That EKS layer on top of the Kubernetes, they add themselves to each cloud, however, only with fewer standards and a little more issues. They need to work on the Amazon plugins on the Kubernetes cluster.

We just updated to a cluster 1.18, but we were on that cluster 1.13 which had many bugs and issues. Moving up to 1.19 in the middle of last year, we had some issues which they had to fix.

One thing that is probably not the greatest in Amazon is the ideology. They really want you to stick to cloud tools. They want you to use the managed version of the databases and our preference is to use the Kubernetes-managed databases. This doesn't fit well with the AWS philosophy, which is then passed on to the AWS engineers and they push that, push ideology on us as well, saying "You know what, we want you to use this database."

We're not dogmatic. If they want us to use a specific database, we use it, as the cluster is very dynamic. We don't need to deploy a database within a cluster, we can use the cloud database. To us, it's just a connection string, so it's not inefficient for us. It's just based on the client. However, you can see there's a little bit of an ideology dogma baked into the AWS philosophy just to keep you in the cloud. 

We've used the solution since it began. It's been a while.

Older versions haven't been so stable, however, they have been working on improving the latest solutions and it's getting much better.

The Kubernetes cluster is developed outside of the cloud, the core of it. The core of it has gotten much better and all the plugins that Amazon did, also have gotten better as well. One kind-of drives the other. It's a revolving, iterative process. You just have to be proactive and keep on updating your versions and manage your cluster a little bit better every time.

We don't have to deal with technical support at all. We haven't used them in the last six or seven years. There's nothing fundamentally wrong that we've found over the years that we have to call support. Almost everything is self-explanatory on the website. There really isn't a need to talk to them directly.

I also have some experience with Kubernetes and Google Cloud. Google Cloud has something called Google Cloud Run, which is very similar to Fargate. Both are trying to make your life much simpler so you don't have to look at the bare-bones infrastructure. It's easy.

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.

We're not on the latest version. We are three or so versions back.

However, we're almost on the latest version, which may be 1.19. The version's no longer an issue. For us, the issue was that Amazon started with the ECS, the Elastic Container Services. Therefore, while we were using Kubernetes and then Google Cloud, for example, for a while and we had developed all the tools when a client came to us and said they wanted to cluster within the Amazon development cluster. That was the ECS. After that, Amazon added the EKS. Our first deployment in Amazon was on our own deployment of the cluster, not on any services. We didn't want to use the ECS, we wanted to use a cluster. We wanted a managed version, so we don't have to manage it ourselves, due to the fact that it's a little bit of a mess if you manage it.

I would advise new users to make sure that your cluster's secure. Make sure you're using a good networking configuration in your EKS. You need to get the NAT and the router going just on the subnet. You might have to pay for that. There are open-source tools to use, however, you can also pay for their monitoring.

When you have a development pipeline, we suggest having multiple clusters, not just one. Then you can really isolate your production cluster and make it really secure and maybe relax a little bit for your DEV and then QA, as you might want to have more things in there. You just need to make sure you remove those tools from your production box.

It's easier to have multiple clusters and really partition the cluster per environment, development, QA, testing environment, integration testing, whatever, and then you have your production environment, which is really kind of locked down so that nobody has access to it except specific people.

In general, I would rate this solution at an eight out of ten.

Our primary use case for Amazon EKS is for running production workloads. The microservices-based modules are broken down and then hosted using EKS.

The most valuable feature to us is orchestration when it comes to deploying. The main ideology behind Kubernetes is that you are breaking down your application into smaller modules. Instead of having a monolithic application, you make it a microservices-based module. Using an e-commerce website as an example, we would break down login's as a single module, registration as a single module and then in the event of an issue one does not impact the other. 

With your small modules, in most cases, they share a backend, and that's how at the service layer you connect them by using an API gateway or a service mesh. This ensures scalability and they don't have any dependencies when it comes to failures. This means you can easily scale and optimize. 

For now I can't suggest any improvements or additional features, the features we currently use we are very happy with. 

We have been using Amazon EKS for 8-9 months.

The Kubernetes solution has been very stable for us, there are no issues to report on that aspect. 

The beauty of Kubernetes is that you can scale it. You just have to define the workload and the scalability capability is managed itself. We just need to define the number of bots or services we want to run and it manages to scale up when needed. 

We have found Amazon to be quite responsive. We have experienced some minor issues and the response time was good. 

With regards to setup, you would need to have a basic understanding of Kubernetes and how it works before you start deploying your production workloads. Once we started working with Kubernetes and got hands-on experience there was not much hassle to roll out to production workloads.

There is no up front licensing cost, as this is a cloud based solution you pay-as-you-go.

The advice I would offer on Kubernetes is with the configuration of your applications. Some are compute-extensive, some are compute-optimized. If you don't configure this correctly it can lead to deadlocks. The compute power has to be enough when it comes to your applications. Some applications need more memory, for example, e-commerce sites need a good response time. In this example, you would want to configure a memory-based application. 

Amazon EKS is for storage, services, and infrastructure. We have about 100 to 120 users on Amazon EKS right now. We'll likely expand by 10 or 15 percent in the next year depending on our customer needs. 

AWS cloud services are flexible and have thorough documentation. AWS also has data centers all over the world.

I would like Amazon EKS to be easier to configure on various environments like Windows or Linux installations

I've been using Amazon EKS for five years.

We use AWS in parallel with other cloud service providers from different vendors, like Azure and GCP. We are not the end customer. It's something we use to deliver services to our customers. We work with a wide range of technologies at the company, which employs around 500 plus people. They work with all major technologies currently available.

Installing Amazon EKS is straightforward. It depends on what needs to be done, but it's not something that takes weeks to deploy. You can do it in a few hours or days. I would need to have a specific scenario to be precise because the steps vary in each installation.

I would like Amazon EKS to be cheaper. Otherwise, it's fine.  

I rate Amazon EKS eight out of 10. 

The product helps to create a new environment fast. 

The tool helps us to provide greater access and also scale applications faster.

The product helps us to configure the new environment. 

The tool's setup is complex. 

I have been working with the product for three years. 

I would rate the product's stability a ten out of ten.

I would rate the product's scalability a ten out of ten. My company has around 1000 users of the tool. We have plans to increase the usage in future. 

The tool's setup is difficult and requires experienced people. 

I would rate the tool a ten out of ten. The product is very easy to use since it is based on Kubernetes. It is a great tool for complex environments with multiple applications. 

We are using EKS for crypto exchange. 

We chose this solution because with crypto exchange you need high performance and easy manageability. It offers highly available architecture, and with two or three Linux servers it distributes all containers through the Linux systems. If that's what you're looking for, it's a really good solution. It also allows for integrating their infrastructure as service capabilities. 

The only important thing that needs to be improved is the EKS dashboard. It could be more user-friendly. 

We've been using this solution for about six months. 

The solution is stable. 

The solution is very scalable. 

We're very happy with the customer support. 

The initial setup was average in terms of complexity. We have one very good system admin person in our company, he has a lot of experience and carried out the deployment. We are still in a testing environment, so there are only around 15 users. We have around a million users and eventually they'll all be using it. 

We pay a monthly licensing fee which is quite high but they do provide good service.

I rate this solution eight out of 10. 

Amazon EKS is basically a model provided by Amazon that allows you to create and deploy multiple microservices and manage containers. Once the Kubernetes is installed, we can directly create the container, set up ports, and set up new services. We currently have Java containers running.

We have more than 500 people using this solution. We are on version 21.

I like the scalability they're currently providing. Integration was very easy. It was a good experience. 

The connectivity could be better.

I have been using Amazon EKS for more than two years.

It's stable enough. We have had network connectivity issues twice. We weren't able to figure out what went wrong there, and it caused a problem initially. When we restarted it, it began working again.

The scalability is good.

We have called technical support a couple of times. We had an issue with integration with Lambda. But we took the help of AWS and it was resolved.

We have also used the IBM private cloud, but it wasn't user friendly.

Setup can take half an hour to an hour.

For deployment, our technical team was made up of 15 people.

My company paid for the license. 

I would rate this solution 8 out of 10.

I would definitely recommend this solution. It's a very good application, and the scalability is very good.

Our primary case is using Amazon EKS with all of our data in our MapReduce, map clusters, and our data clusters. And from there, we just input the information using Python and do our analysis using that.

We have problems with setting up virtual environments and installing the right packages. I believe the initial setup could be a better experience and faster customer support.

I have been using Amazon EKS for the past one and half years now.

The stability is excellent.

The scalability is not a problem we currently have five to six thousand employees using Amazon EKS.

Technical support or customer support is good but could be faster.

For us in our company, it is a very straightforward process because they had built a layer that is very useful for the users with just a couple of clicks. But normally, if you ask me in person, I think it is a bit cumbersome.

It can be done in-house but they have to read a lot of documentation from the AWS provider in order for it to make sense.

When you compare it to on-premises it is much better because, with on-premise, we have to look into data security and all these items. Amazon EKS eliminates that and becomes a cost-benefit for us.

On a scale of one to ten, I would give it a six. With the correct technical support individual, you can do well with implementation. If you are not sure about the technical support side for implementation I would wait until you are ready.