Amazon EKS Reviews

In my current project, I am handling a US customer who is completely focused on working with LLM and security. Their clients are expected to give us the data sources, and we provision from Greengrass, which works as an agent running on the end client to fetch the customer false alarms. It's primarily focused on the SOC 2 side, where data goes to the SOC dashboard or SOC data source, leveraging LLM to filter how many alarms are false positives versus true positives. In doing this, the NOC engineers observing the SOC 2 dashboard do not have to worry about how many are true positives; our LLM API or tool filters it out and indicates how many true positives and false positives are displayed on the dashboard. This significantly eases the burden on the NOC engineers.

We are utilizing Amazon EKS for our application because most components are LLM and require certain GPUs, so we have specific managed node groups and we also use Carpenter along with HPA in place. Whenever the need arises, process queues are listed in Redis for elastic cache, and all the lists are processed and read from Redis and handed over to the nodes dynamically using HPA. We leverage HPA and Carpenter within Amazon EKS for scaling or scaling out.

Amazon EKS is simple due to its support for diverse AWS tools and various integrations, significantly influencing my application development and management processes. There is the aspect of trust relationships and permissions. Every service we create involves setting a role, and all authentication processes link to a policy, deciding access. This seamless process extends across accounts thanks to ARN (Amazon Resource Names). With the security guardrails in place, services are accessed effortlessly while maintaining high security, giving me confidence in its management capabilities.

The HPA feature within Kubernetes is one aspect I appreciate about Amazon EKS; it's beneficial for scalability. The managed node groups or dedicated node groups with GPU capacity allow us to scale in or out depending on our capacity planning, and Carpenter, which we have provisioned for scaling, contributes to that. These features are not available by default in EC2s or ECS, where you lack control over the nodes. Many people attempt to adopt Lambda functions or serverless solutions, but that approach does not suffice for GDPR and HIPAA compliance as it demands a solid identity of the node and clarity on where it is being provisioned. Hence, we cannot depend on any of the Lambda or serverless components which may be unstable, and this often leads to increased billing due to the dynamic nature of scheduled procedures that require a standalone node rather than a transient one.

When it comes to the integration with IAM, I have two thoughts on the authentication process of Amazon EKS. Previously, when I began using AWS, there was something called the AWS auth config map within Amazon EKS. Initially, only the person who creates the cluster has access to it. Now, if you engage in enterprise roles—as I did while working within the UK for Santander—it presents challenges. Essentially, earlier only the creator had the master role for accessing the node and the onboarding process was rather manual, with companies relying on ServiceNow and other tools for onboarding new joiners or team members. Now, EKS API or EKS CTL has many default settings that are not enabled, you need to enable these. Most clusters are created using Terraform, and you need to create a role that can manage cross-account access, as many customers don't operate with a single account.

My previous employers, such as Fidelity Investment, Nokia, and Ford, have worked across multiple accounts, necessitating single sign-on. This setup allows for cross-account access to the cluster by employing EKS CTL APIs, which leverage single sign-on to onboard team members. As such, once the role has access to the cluster, it can onboard users as a dev user, admin, or tester, simplifying the onboarding process. This way, previously manual tasks can be automated, which is a significant improvement. Earlier, we had to make changes to the config map to onboard users, but with EKS CTL API, this integration between EKS, Kubernetes service, and the cloud side is improved tremendously, alleviating many worries.

Self-healing nodes assist in minimizing administrative burdens in my projects. Coming from a telecom background where I've worked over seven years, I'm familiar with a service called SON—self-organizing and self-healing functionality. At a logical level, these are the layers we interact with, but AWS handles the physical layer through their software components. For instance, if one node is not ready and you enable the auto mode feature, AWS manages that for you—IAM upgrades or any nodes malfunctioning. I've seen these features in the UI; I've enabled them, and every 10 or 15 days, patches roll out. I can check them via AWS Inspector to see if there are any node-level patches or AMI level patches necessary. AWS takes care of these issues automatically. I appreciate that I don't need to manually check the dashboard and apply upgrades one by one, which is a significant improvement.

I measure the impact of Amazon EKS on the organization's management of complex workloads in terms of effectiveness and efficiency through my background in development and systems. Initially, I spent five years as a Java developer before transitioning to DevOps. With my understanding of end-to-end application architecture, I assess workloads based on system and application planning. For example, when I worked on a data lake product in Fidelity Investment, I observed that the cloud onboarding process, including Amazon EKS, had roadmaps extending over five years—from 2019 to 2024. I understand the nuances of enterprise or legacy applications and any system-related complexities. It all boils down to two components: system planning and application planning. Initially, we identify the type of application—whether it is database-related or has high GPU demands. Most applications today involve GPUs, which tend to incur high costs, and often customers are unaware of how to handle dynamic workloads effectively. It's crucial to assess not just one part (system), but various elements CPU, memory, and IOPS since the underlying hardware interacts with those components regardless of domain. First, we need to evaluate the application's requirements—such as its dependency on node storage. With EKS, Kubernetes provides solutions CSI, CNI, and CRI. By understanding the application's demands, I can apply the right Kubernetes configurations for performance optimization, such as taking advantage of Amazon EKS's ability to adjust the container network interface settings to suit the client's workload requirements. This loose coupling allows us to optimize our resources irrespective of whether we're using on-prem or cloud environments.

It has been since 2019 that I started using Amazon EKS. At that time, it was completely new, and many people were not using it just yet; it started from version 1.21, and right now we are on 1.33. Recently, 1.34 has been launched, but it's not yet available in the service catalog; we can see only 1.33. A lot of improvements have been made.

We had numerous add-ons to install manually because Kubernetes is a completely different service than AWS cloud provider, and everyone has opted to use it. After opting, there is an identity that you have to maintain—one at Kubernetes level and one at the AWS provider level. You have to maintain one identity at IAM level and one within the cluster, Amazon EKS. A few things do not make sense within the add-ons, many of the secret providers that read the secret from Secrets Manager and then mount it as a volume. We use a service called EBS CSI driver, which reads the secrets or sensitive data from Secrets Manager and then mounts it as a volume to the pod at runtime. However, that doesn't have a dynamic feature where, if any changes happen in the secrets, it can read and populate in the environment.

Sometimes consider your RDS password or OpenSearch password rotates. Amazon EKS doesn't have that feature to read the dynamic one and consider that the password has changed overnight; there is no functionality from the provider to see the changes and then restart the pod or fetch the new value. This often leads to downtime of 12 or even 6 hours, depending on when you realize it, so that needs improvement.

Nonetheless, mostly on the add-on side, they have developed a lot; earlier we were installing them manually, but now with EKS auto mode, many things VPC CLI and pod identity service—around four plugins—are installed by default, which is a good thing. However, I believe there should be some solution that is self-contained, covering generic use cases.

With the 1.33 release, they have addressed most of my earlier concerns, but I am still looking for some improvements, particularly in CloudWatch monitoring. In IT, we manage two aspects: either the system or the application. Currently, the application logs and monitoring are not very robust in CloudWatch; you can only find things if you are familiar with them. Fortunately, we are familiar, as most of the monitoring involves two types of databases: one is a time series for monitoring data, and the other is an indexing solution for a streaming service. This means we need to get the logs from each node, index them, and populate them on a screen. That part remains a separate service, but if they managed it within Amazon EKS service, where the monitoring is consolidated in one place, you wouldn't need to rely on Prometheus, Grafana, or different services. It would be advantageous to have a consolidated platform for EKS, as Kubernetes is leveraged; monitoring and logging should also be integrated simply by enabling parameters or tags. This would create a self-contained platform where people can onboard and start using it. Currently, I still need to enable logging and monitoring among other things myself; that shouldn't be the case after six or seven years in the market.

On a scale from 1 to 10, I would rate Amazon EKS tech support an eight. Some individuals have a deep understanding of the services and can identify potential bottlenecks, especially with load balancer endpoints and certificate management. The shift from NGINX to AWS load balancers has diminished many previous issues. However, not every support engineer meets the same level of expertise, hence why I rate it a solid eight, which I consider decent.

I have been using Amazon EKS for seven years.

Amazon's customer support has its merits; it is good overall. However, when it comes to enterprise licenses, the quality declines significantly. Startups may not recognize this at first, engaging with support during their initial phase, but they soon discover the lack of expert guidance and the costs associated with it—it's quite expensive.

Positive

I have experience with other Kubernetes engines, such as those from Oracle, Azure, and Google. For instance, I find Red Hat OpenShift to be an excellent competitor. Many have transitioned to Azure due to the hosting incentives it offers for running open AI models. While people explore Amazon EKS or other Kubernetes solutions, the simplicity in moving between platforms remains an advantage since your manifests mostly stay the same, needing only minor adaptations to services. My experience with Red Hat OpenShift tells me it's a robust solution with competitive attributes against AWS.

In terms of ROI, the return on investment, I have clear examples. When we began, we utilized managed nodes for applications needing dynamic processes. Some applications simply required pod creation once data was queued for processing, allowing the node to remain free afterward. For a customer who was not cost-effective—we had provisioned a node that saw little CPU usage but substantial memory consumption—we implemented effective resource quotas and HPA. This setup enables the system to utilize resources dynamically based on the actual demand observed by reading metrics from the Prometheus adapter. Notably, the adapter isn't an out-of-the-box feature provided by Prometheus; you need to create your own adapter for it. Using tools HPA and Carpenter allowed us to scale resources based on requirements. Initially, not having them resulted in an unoptimized solution. However, with these tools in place, we witnessed a reduction of costs by approximately a third—if it was $100 beforehand, we brought costs down to $25.

Regarding the pricing aspect and the licensing cost of Amazon EKS, sometimes it is not clear. Most discussions revolve around the data transfer costs from one region to another, and there are certain concerns regarding GPU nodes. However, if you optimize your node usage, with tools such as Kubecost, you can analyze how effectively you utilize your nodes. If you manage to optimize usage, you won't face steep costs. Otherwise, the cloud provider will certainly benefit from inefficient usage. Ultimately, it's not out of the box—if you want to monitor costs effectively, applying separate tools and acting accordingly in advance is essential.

I notice key differences between Amazon EKS and its competitors, analyzing both pros and cons. The seamless integration is sometimes lacking in other offerings. When managing software in platforms Kubernetes—including EKS, AKS, GKE, Rancher Kubernetes, and Oracle's Kubernetes engine—I've faced specific challenges, particularly with user management in Oracle's solution, which isn't as seamless as it is in Amazon EKS. Comparatively, OpenShift from Red Hat has notable strengths. Oracle is making improvements, especially with its longstanding database solutions. For cloud providers, though, OS from Red Hat is a formidable competitor, offering robust out-of-the-box solutions around resource limits and dashboard configurations that do not require command-line interventions.

The review suggests that people considering Amazon EKS should heed some recommendations. They often attempt to enforce infrastructure as code with tools Terraform or HashiCorp to maintain workspace and all. I advise using services within a single environment, especially for LLM applications. It's prudent to have multiple LLM sources across various cloud providers while utilizing the same keys within your AWS environment.

My second piece of advice is to establish a separate CI/CD platform independent of AWS. This keeps things loosely coupled; with minimal tweaks in CI/CD pipelines, you can seamlessly migrate from one platform to another—say from EKS to AKS to GKE or OpenShift—thus keeping the focus on feature development rather than migration headaches. This leads to a modular approach in your code and infrastructure, ensuring that only the cloud provider specifics require adjustments.

Overall rating: 8 out of 10

Public Cloud

Amazon Web Services (AWS)

Amazon EKS's main use case is using compute in a highly containerized format. Building and running applications is my primary use case for Amazon EKS.

I currently use Amazon EKS to run a web application. I have my own website which I host on Amazon EKS.

Currently, I use Amazon EKS to host a web application, but Amazon EKS is so versatile. I have used it previously to host a whole host of different applications.

One of the features I really appreciate about Amazon EKS is its ability to integrate with other AWS tooling. That is very useful because it is part of the wider Amazon ecosystem. For example, if you wanted to grant access to a specific pod within Kubernetes, you could use Amazon EKS to provision a service account for that pod to interact directly with a bucket. You would not get this type of benefit if you used Kubernetes out of the box which was not running on an Amazon service. The ability to integrate with other Amazon tooling and do it at a very granular level is something I value.

From a security perspective, Amazon EKS is very strong. That granularity allows for specific pods to have access to specific buckets or specific EC2 instances. It allows for the principle of least privilege at a very granular level, which improves the security of using the tool.

Amazon EKS allows me to use applications and deploy them in a specified format and to scale that across huge numbers. Because you can deploy Amazon EKS via Terraform or other infrastructure as code tools, it allows for a repeatable architecture, which is brilliant because once it is developed, it is very simple to then deploy further versions for potentially different customers or different versions of the same application.

One limitation I have found with using Amazon EKS is that there is a very big learning curve. It is very complicated to use the tool. I have used Google's GKE which offers an easier framework because they offer managed clusters, which are called Autopilot clusters. With that, a lot of the management is abstracted away and managed by Google. Amazon does not have the equivalent of that, so you would have to manage everything yourself. In some scenarios you would want to do this because you might want that level of granular control. But if you are running a proof of concept or developing in a very quick way, it becomes very difficult and time-consuming to get it initially running.

Another limitation I have found is the management overhead of upgrading Amazon EKS. Because Kubernetes has frequent updates, you have to manually trigger those in AWS, which can be quite time-consuming, especially if you are managing many clusters. Amazon could potentially make it easier by automatically triggering upgrades, but it is definitely a high toil activity, especially for smaller teams.

I have been using Amazon EKS for around four years.

Amazon EKS is very stable. You can set up your clusters to have however many nodes you want, which allows for greater stability when users are using it. You are able to upgrade whenever a new upgrade comes out.

Amazon EKS's scalability is brilliant. You can easily scale from zero nodes all the way up to the tens of thousands, probably even higher. There would not really be a bottleneck in terms of scalability. You can have it however you wish.

Customer support for Amazon EKS is brilliant. It uses AWS's customer support which could be as quick as probably five-minute response time or 15-minute response time. Whenever I have contacted them, I get responses every single time, same day, and Amazon's expertise is immense. There are no complaints there.

I have previously used GKE from GCP. You would only really use that if your other workloads are running within Google Cloud as well. If your workloads are running in AWS, using Amazon EKS is better because it allows for better linkages between different products within that cloud platform.

In terms of deployment speed, if Terraform files are configured to deploy on Amazon EKS and they are written in a very efficient way, deployment could be as quick as five minutes. Compared to deploying on a traditional virtual machine in a data center, which would be hours, that is a big time saving. You can very easily save over 50% of the time if you are deploying on Amazon EKS compared to in a traditional manner.

Return on investment with Amazon EKS is positive, because it allows applications to be hosted. In terms of money saved, if a team was to go from not using autoscaling to using autoscaling in terms of how much user traffic is actually generated, teams could easily save over 30% of costs just from that one tool being enabled alone.

Pricing for Amazon EKS is quite good, because you can choose the instances which are running under the hood. If you wanted to use smaller machine types, you can control your cost quite well. You are also only paying when those machines are up. You could configure your nodes to scale down massively when you do not need them up all the time. It is very cost-efficient due to its flexibility.

I did not really evaluate other options before choosing Amazon EKS, because if workloads are run on AWS, you would only really use Amazon EKS if you wanted a managed Kubernetes service. Amazon does not offer another service which would compete with that.

Overall, Amazon EKS is a very good tool to use and it is commonly used in the industry. However, GKE is easier to use and some of the management is abstracted away, which is not the case with Amazon, so that is one drawback. The need to upgrade yourself as well is another limitation. I believe with GKE Autopilot, you do not have to do that because Google manages it under the hood. The fact that it is not as automated as similar Kubernetes products on the market is what takes off those last two points.

I did not purchase Amazon EKS through the AWS Marketplace. I bought it directly through Amazon on their AWS platform.

I would recommend using Amazon EKS if your workloads are running on AWS. If your workloads are running on Azure or GCP, you probably would not want to use it just because you would get better linkages if you were using the cloud platform's own Kubernetes service. I personally prefer GKE because of the Autopilot feature, but Amazon EKS is a brilliant tool if you are already on Amazon's platform. I have provided a review rating of 8 out of 10 for this product.

I used Amazon EKS throughout my internship while working on Docker-based deployments, Kubernetes orchestration, and cloud infrastructure tasks at Cognizant.

I used Amazon EKS when working as an intern at Cognizant, where it was used to deploy, manage, and scale containerized web applications. Our workflow started with building Docker images, storing them in Amazon ECR, and deploying them to Amazon EKS clusters running on EC2 worker nodes with CI/CD pipelines. We used many tools; for example, Jenkins was part of our CI/CD pipelines that automated the build and deployment process.

Additionally, I worked on application deployments, updating Kubernetes manifests, managing pods and services, and verifying application health. Amazon EKS acted as a central platform that connected Docker, AWS infrastructure, and DevOps automation into one consistent system.

I used Amazon EKS during my internship at Cognizant as part of a cloud and DevOps-focused environment, where it served as the core Kubernetes platform to run containerized applications built with Docker, deployed through CI/CD pipelines, and hosted on AWS infrastructure. We deployed numerous web applications, and we wanted to learn Amazon EKS through dummy projects with dummy web interfaces. Beyond dummy projects, we also deployed some client websites into the Kubernetes environment and managed traffic, although I cannot name the clients.

Amazon EKS is an excellent choice for organizations already invested in AWS. I recommend having a solid foundation in Docker, Kubernetes basics, and AWS core services before implementing Amazon EKS. Using infrastructure-as-code tools and following AWS best practices can significantly improve maintainability and security. Amazon EKS is particularly strong for enterprise environments and microservice-based architectures.

Amazon EKS is ideal for teams already using Docker, CI/CD, and AWS infrastructure, which our team was already utilizing. I strongly recommend learning Kubernetes fundamentals and AWS networking, containers, and security before using Amazon EKS in production, as it positively impacted our organization by making it easy to connect all our existing AWS services.

I deployed Docker applications to Amazon EKS using CI/CD pipelines, integrating with EC2, ECR, IAM, and automated workflows.

The most valuable feature of Amazon EKS is the managed Kubernetes control plane, which allows teams to focus on DevOps and application workflow instead of cluster maintenance. Another major benefit is how naturally Amazon EKS fits into the AWS ecosystem, integrating with EC2 for compute, IAM for access control, and Amazon ECR for container images. CloudWatch for monitoring creates a complete DevOps pipeline. The self-healing nature of Kubernetes combined with AWS scalability makes the environment reliable and suitable for real-world workloads.

The most promising feature, which I prefer the most, is its integration with all the AWS services, including EC2, IAM, VPC, ECR, and CloudWatch, making it a key part of my workflow.

Amazon EKS works very well with Docker-based container workflows; it is highly scalable and self-healing, complemented by its rolling update capabilities.

There are some drawbacks regarding Amazon EKS; pricing can increase as clusters and workloads scale, and there is an initial configuration learning curve. A beginner has to learn about IAM networking and cluster setup, plus improved built-in cost visibility and simplified monitoring tools could be beneficial.

Pricing can be improved, especially for small teams or landing projects, and the initial setup, as well as understanding IAM networking and cluster configuration, can be complex for beginners; improving this would enhance the experience. Troubleshooting sometimes requires deeper AWS and Kubernetes knowledge, which also could use improvement.

Amazon EKS is very stable; during my usage, the clusters remained consistently available and workloads ran reliably. The self-healing capabilities of Kubernetes, combined with AWS managed infrastructure, help ensure minimal disruption to running applications.

Amazon EKS is highly scalable; it supports horizontal scaling of pods and seamless scaling of worker nodes. It features AWS auto-scaling for vertical scaling, making it suitable for handling varying workloads and preparing the environment for real-world production demands.

Support and documentation from AWS are very strong, with extensively available official documentation, and AWS support channels are very kind and responsive. Most issues can be resolved through AWS knowledge bases, documentation, and our organizational support teams.

Positive

Previously, I used open-source Kubernetes as a normal Kubernetes solution. I wanted to switch to Amazon EKS because Amazon EKS connects with numerous AWS services, making it easier to deploy my applications than using open-source Kubernetes.

The initial setup is moderately complex; creating clusters, configuring IAM roles, setting up the network, and connecting with the worker nodes requires careful planning. However, once the environment is configured, ongoing operations such as deployment, updates, and scaling are smooth and efficient.

Time is definitely saved because Amazon EKS provides automation with CI/CD pipelines, allowing us to simply monitor it, and if there is any fault, we know immediately in the pipeline. Regarding the need for fewer employees, I do not think that will happen since only a few employees will know about Amazon EKS deployments in the cloud, so some knowledge is necessary. We can see a return on investment; we can save a lot of money by using Amazon EKS as it is directly connected with all AWS services and can be integrated with coding storage options such as GitHub and GitLab.

I did not evaluate many options; I evaluated some, specifically open-source Kubernetes, which I thought would be more difficult than Amazon EKS. Therefore, I chose Amazon EKS.

The benefits that I observed after adopting Amazon EKS are improved deployment speed and reliability, while resource usage remains the same, but we can handle a larger amount of traffic and application deployments without any issues. The user interface is very great, providing clarity on where the fault or error lies while deploying our application. I would rate this product nine out of ten.

Public Cloud

Amazon Web Services (AWS)

Amazon EKS is used for running any type of application, and it is one of the more stable platforms. We use everything in AWS, CloudFront, and we probably have a hundred applications in AWS.

Several of our platforms have transitioned to Kubernetes because it is more economical than some other methods, and we have lots of apps that use Kubernetes.

The deployment of Amazon EKS took time mainly because of the amount of stuff we had to move, such as old legacy applications and systems, which need changes every few years, and AWS is just better.

We have several applications that run in Amazon EKS.

The best features of Amazon EKS include Kubernetes, as several of our platforms have transitioned to Kubernetes because it is more economical than some other methods, and we have lots of apps that use Kubernetes.

The support for AWS tools integration with Amazon EKS has influenced our management process significantly. The cloud affects everything, with probably 80% of our stuff in the cloud, mainly in AWS.

The self-healing nodes with Amazon EKS help minimize administrative burdens, and while I'm not a system admin myself, I do disaster recovery testing and resiliency assessments for cloud applications.

Improvements could include better support and pricing, which is always important.

There are definitely areas for improvement with Amazon EKS.

My experience with AWS products spans the last six, seven years.

Amazon EKS is stable.

Amazon EKS scales effectively.

I would rank their support close to a 10; they are very responsive.

Positive

The initial setup with Amazon EKS is straightforward, and all of our teams are involved with AWS. Some use EC2 containers, and I've seen everything there is to see in AWS.

Amazon EKS is easy to use, and you don't experience the problems that you encounter with some solutions like EC2 containers or S3 buckets.

The support for AWS tools integration with Amazon EKS has influenced our management process significantly. The cloud affects everything, with probably 80% of our stuff in the cloud, mainly in AWS, though we have some in Google Cloud and Azure.

In my role, I don't set anything up; I set up test disaster scenarios and have teams practice against those scenarios. We have encountered challenges during that migration process, and we utilize our AWS dedicated person whenever something arises that we can't handle or don't understand, so we can seek their help.

Amazon EKS helps us manage complex workflows effectively.

On a scale of 1-10, I rate Amazon EKS a 10.

Public Cloud

Amazon Web Services (AWS)

My use cases for Amazon EKS involve different types of workloads, mostly web-related and mobile application related, to orchestrate deployments and make application release easier, smoother, and faster.

What I appreciate best about Amazon EKS is that it's a great solution, especially for teams who search for flexibility and who are in active development of microservices architecture. It gives flexibility not only for DevOps but also for developers to control infrastructure through code, such as through the GitOps approach, which releases a lot of time and effort for system engineers and DevOps engineers to do their business and not provision servers by their own. Scalability and flexibility are the most useful cases there.

The benefits I have seen using Amazon EKS are substantial. Kubernetes is a complex structure, and if you have it self-maintained on your bare metal devices, it's first a pretty big chunk of the infrastructure. Plus, it's very complex and hard to maintain. Not all engineers have the knowledge to control a Kubernetes cluster. Having it as a service reduces release time dramatically. Amazon controls the versions, upgrades, security components, and related elements. It's easy to do system upgrades, even release another cluster in a matter of a few hours and move all workloads to the latest release of Amazon EKS if needed or for development environment testing. This would be much more difficult if you control your Kubernetes cluster manually as self-hosted.

What could be improved in Amazon EKS is the documentation. It's pretty dynamic, moving too fast, and sometimes the documentation doesn't reflect the migration between changes, especially if you use some non-native vendor enterprise components and CRDM modules. If you use only AWS UI, it's pretty hard to debug something in Amazon EKS. You need to be aware about kubectl or another toolset which allows you to dive deeper into the Kubernetes details. Not always do software developers have such expertise. If they could improve their debug capabilities, toolset for debugging, or maybe integrate it with their AI, such as Amazon Q, to support the developers, it could be valuable for developers.

I have been using Amazon EKS practically from the day they implemented this solution, which has been many years already.

I can easily scale up or down with Amazon EKS. Currently, Kubernetes and Amazon EKS itself have many different vendor solutions, native solutions or vendor solutions for scaling. It's easy to control and very cost-efficient.

The customer support from Amazon could be better. I find it problematic that customers who don't have a big budget are not able to use the customer support. If you don't have your support subscription, you're only able to use forums, which is challenging because sometimes software can have issues or the setup is pretty tricky and not reflected in the documentation.

Support quality depends on which engineers you're talking with. For bigger clients and companies, AWS dedicates an agent or manager as a point of contact for the specific team, and you can easily reach them and discuss initial key points. It's great. It doesn't always do what you would expect, but at least it's a very good and fast starting point to solve issues.

I would rate their support a seven out of ten.

Positive

I have previously used different solutions before Amazon EKS. For simpler solutions, we used Amazon ECS, which is a native Docker orchestrator. It's much simpler than Kubernetes but does the job well for a simple setup. It's a bit more difficult in the provisioning and control of components, but it works fine for static infrastructure. We used various solutions, from self-hosted Docker containers for ECS, Fargate, to Lambda functions. The choice depends on what you actually expect from your application and infrastructure.

Regarding the pricing of Amazon EKS, if we discuss EKS itself, it's relatively cheap at $75 for deployment for the control plane. However, for running a Kubernetes cluster, you need more resources even for the simplest deployment of any modules. It could be very expensive, but it's mostly about how the architecture is built and how the team controls the resources. It's not about Amazon EKS directly, but related.

I have used Amazon EKS integration with IAM. It's basically one of the core parts of the configurations not only for access to the cluster but also for the services themselves to provide them access to other Amazon resources.

My experience with the deployment in Amazon EKS is that it's easy and has many options for how you can control your deployment in Kubernetes from your native kubectl to the GitOps approach. I strongly support the GitOps approach to control everything and give developers access to infrastructure through GitOps and full code-controlled infrastructure. It's easy when you integrate everything and it works fine.

I would recommend Amazon EKS to others if they're sure that Kubernetes is what they would need. On a scale of 1-10, I rate Amazon EKS an 8.

Public Cloud

Amazon Web Services (AWS)

The use case for Amazon EKS is that we have our multiple pods and services running on the particular microservices application, so we have to integrate and auto-scale the Amazon EKS cluster from the Amazon EKS cluster and pod management services.

If any traffic increases on the application, we have set the load balancer and auto-scaling via the Amazon EKS cluster, the managed cluster.

The value of Amazon EKS for us is due to our microservice-level architecture, where we need to automate and have a fast, scalable application, allowing us to directly configure the Amazon EKS cluster in the application, which will make it very easy to run our application smoothly and scalably.

Amazon EKS's self-healing nodes help minimize administrative burdens in my organization by automating through all of Amazon EKS. We have used multiple types of automation tools which we directly integrate for the deployment purpose of the Amazon EKS cluster. We have integrated them with the Docker side of Amazon EKS, allowing the container service to run over there, so it is directly deployed for the administrative level of the Amazon EKS cluster.

The benefits I have seen from Amazon EKS include a fully managed Kubernetes service for the control plane, the API server level, etcd scheduler, and controller. There's no need to worry about patching, scaling, and maintaining the master node. There is high availability over multiple availability zone control panels, and security compliance is guaranteed for IAM; AWS IAM users authenticate and access the control from the support VPC isolation and security group network policy.

The integration with IAM helps enhance our authentication process because IAM basically helps with access. Nobody can enter with any kind of access, and any kind of vulnerability will be showing in my application. If I set an IAM user to that Amazon EKS cluster, that user will have limited access, and that application will run through that IAM user only. It is very beneficial, and for security purposes, it's also important because vulnerabilities will be found and block all the vulnerability and security issues if you set an IAM user to the Amazon EKS cluster with limited access.

We have not automated patches to be applied for the Amazon EKS cluster because it will resolve live production services. We directly and manually configure and update those patches if any patches are required for the particular application.

I did not configure all related applications since we are running only one application, which is good from my end. To improve the regularity, the IAM Access Analyzer could detect risk permissions, so the IAM Access Analyzer could be applied for this.

From all the AWS services we are using, I have been working with Amazon EKS for the last three years. In that EKS, I've been using it for only one application that will be running over there, so I've been using it for nine to ten months, probably one year.

I have communicated with the technical support of Amazon EKS. When we were trying to configure the Amazon EKS cluster for the first time, we used technical support. If there is any kind of debug or related issue that we cannot find from our end, then we can go directly to the support team for Amazon EKS. It's very helpful and needful; whatever kind of issue we are facing, they immediately tackle it, debug that issue, and resolve it as soon as possible. We have found some related networking or triggering issues on the application side, and that issue was resolved from their side as well. There is no issue from the technical side support; it's always beneficial and helpful.

Amazon EKS is stable and reliable. The control plane is fully managed by the AWS service, so there is no need to run the Kubernetes master, which is very reliable. There are multiple availability zones in the regions, meaning no single point of failure, and 99% of the time, our application will have uptime with the Kubernetes API server.

Amazon EKS is highly scalable; as per the node cluster, it will provide EC2 or Fargate up to 10,000 nodes. It's a very scalable cluster, fully controlled by the Amazon EKS plane.

The support of Amazon EKS for AWS tools integration influences my application development and management process because it is on the AWS side, and the Amazon EKS managed cluster is provided from there. We didn't need to manage etcd and those control management tools; it's totally handled from the AWS side, making it very beneficial. We just monitor and configure all the related services and manage them via the pods and cluster-related aspects, while all the configuration support and configuration-related things via the control plane are directly managed by the AWS side of the services, which is very beneficial for us.

Positive

Before Amazon EKS, we did not use a different solution for these use cases. We are directly running on it. If there is any application running on a small-level architecture, we can directly build as per the build process. We follow the build process step-by-step to build our application via the production environment as well, without any Amazon EKS cluster.

We did not participate in the deployment and the initial setup of Amazon EKS; we are using it as per our need. We have an AWS service account, so as per our need, we are using those services. For whatever AWS service we have to use, we have a paid service account, and as per our need, we directly use it.

I am aware that pricing for Amazon EKS should be based on the cost structure, where what cost is planned depends on what we are using and serving on the instance. We have applied tag resources, depending on what kind of services we are using, and they provide the cost as per the service. It would be better if, when the application is running slower, the price would be less than that application; it could be implemented in the future.

Before choosing Amazon EKS, we did not evaluate other options or other vendors; we have not worked with any other tools till yet. We can directly deploy our application via Kubernetes and Docker, Docker, and Docker Compose files, and Jenkins via the automation.

With the IAM service, we have assigned an IAM policy, IAM user, and policy with limited access for that IAM user as per the needs of the application.

In my opinion, Amazon EKS is very good. It's very easy to monitor Amazon EKS, and the highly built architecture will be smoothly running over this Amazon EKS cluster.

On a scale of 1-10, I rate Amazon EKS a 10.

Public Cloud

Amazon Web Services (AWS)

I am currently using Amazon EKS to host our applications to provide scalability and security.

Amazon EKS is providing auto-scaling, which is one of the features that everyone is moving to because it eliminates the need for manual deployments on EC2 machines or on Docker.

Kubernetes manages all those things, which is why people are moving to Amazon EKS. For Kubernetes as a project, it can be difficult to manage, but that problem is solved by Amazon EKS so I just need to think of our application rather than the infrastructure, and AWS manages all the infrastructure.

Because of Amazon EKS, if I do manual deployment on Docker and my particular container fails, I will not know the reason. However, Amazon EKS automatically restarts that particular container or stops it and creates a new one, switching the traffic accordingly.

This is why Kubernetes provides a better experience. Amazon EKS provides scalability, which is one of the features everyone is using. In case of high traffic, it automatically scales the nodes and pods, and we can scale it horizontally or vertically.

The authentication process means that with IAM policies, I am giving the least privilege access so only particular persons are able to manage Amazon EKS clusters. Everyone does not have access, which creates a kind of zero-trust security.

In a minor enhancement sense, I just needed some documentation on that, which has already been provided, but in simple English language that is easy to understand for freshers as well.

I also needed to have some infrastructure to do some practice sessions before we could move on, because the pricing of Kubernetes and Amazon EKS is too high. For some people, it is hard to get hands-on experience with those.

I have been using Amazon EKS for almost 1.5 years since I joined the cloud infrastructure team.

I would rate Amazon EKS around 9 out of 10 for stability. It is stable.

I did not need to communicate with the technical support of Amazon EKS yet. I have not communicated with their support specialists for Amazon EKS, but I did for other things.

Before Amazon EKS, I used manual deployments such as getting the code and running those on servers. Then Docker containers came into place, running code on Docker, followed by using ECS, and then Amazon EKS came into play. It was a logical update that I chose to go with.

I participated in the initial setup of Amazon EKS in the deployment process at my previous organization. For the initial setup, there are types of deployments for Amazon EKS cluster. Via the console, it is somewhat difficult, but via CLI, it is easy. You just need to set up AWS CLI and configure it, and then a simple command is AWS EKS CTL create, and we can create our Amazon EKS clusters. It is straightforward and not complex.

When purchasing Amazon EKS service for my application, if I have only one or two pods, I need to pay 75 dollars per month for that control plane regardless of what I am hosting, which can be challenging for small-scale companies. For small-scale companies, it is a challenging situation.

Let me provide some additional context. Jenkins is one tool to consider.

AWS DevOps is also available. For monitoring, Grafana and Prometheus are options. There are also some open-source tools that we are working on, such as Signoz.

Microsoft Entra ID is one of the products which we are using. For AWS, we are using AWS ECS, Amazon EKS, IAM for authorization and authentication purposes, and databases. All the services which are on AWS are being utilized.

Amazon EKS is different from Kubernetes because it is a platform built on Kubernetes but managed by AWS. Amazon EKS is already dominating the market more than others, so there is no enhancement from my side.

I utilized Amazon EKS's integration with IAM and OIDC. IAM roles have provided the capability to give permission to Amazon EKS that this particular Amazon EKS is able to access particular services such as the S3 bucket, which is one of them, and RDS, which is a database. We are providing roles to Amazon EKS clusters so that on our behalf, it can access these particular things. By that, they can access the database and objects in the S3 bucket.

I am not currently using Amazon EKS's self-healing nodes. In another organization, we are not on Amazon EKS.

For the nodes, I can tell that it provides auto-scaling to minimize and maximize the number of nodes as traffic comes. By that, the pods will run on those, and if there is any failure on nodes, it gives another node.

I would rate this product a 9 out of 10 overall.

Public Cloud

Amazon Web Services (AWS)

We have been working on a use case where we need to deploy an application in Amazon EKS, which is a Kubernetes cluster. Kubernetes is a managed service provided by Amazon, and if we want to deploy any applications in Amazon EKS, such as application A, I want my application A to be more scalable, more tangible, and I want to deploy this in an Amazon EKS cluster. Then I check for any open source Helm charts, configure them, and deploy that Helm chart in the Amazon EKS cluster. This is how our day-to-day work goes. In general, we also deploy applications such as Apache Spark in the Amazon EKS clusters.

The features of Amazon EKS that I find most valuable include IRSA and IAM Roles for Service Accounts. While creating Amazon EKS clusters, Kubernetes offers a feature where we need to create an OIDC provider. Once configured, the authentication and authorization in the Kubernetes cluster is done by service accounts. Through the concept of service accounts, all the pods utilize IRSA, which is called IAM Roles for Service Accounts. By integrating AWS IAM roles with Amazon EKS service accounts, we can leverage other services from Amazon EKS. For example, if we want to access S3, Secrets Manager, or EC2, the concept of IRSA helps us integrate with external services of the Amazon EKS cluster easily. Additionally, Amazon offers Karpenter for scaling workloads and minimizing billing, along with managed node groups and advanced scheduling to manage our pods on different node groups.

To measure the impact of Amazon EKS on my customers' ability to manage complex workflows effectively, scaling in and scaling out is one of the key aspects for us. Since all our workloads are batch workloads that are intangible and unpredictable, we cannot keep nodes stale 24/7. Accordingly, scaling is of utmost priority. The appropriate node must spin up based on the workloads created at a given time, and pods should schedule on that node so that workloads run. For instance, if there are no workloads, there is no reason to maintain 100 nodes in the cluster, so scaling in and out is essential for not wasting resources or budgets.

One area of Amazon EKS that could be improved in the future is related to the behavior of the cluster autoscaler. We need enhancements so that scaling in and scaling out happens more effectively. Currently, the cluster autoscaler lacks visibility on the Kubernetes labels of a node group. The cluster autoscaler should at least be able to check the labels of the Kubernetes nodes, which is a necessary feature.

My experience with Amazon EKS is three years, and it has been very interesting to understand and easy to collaborate with other AWS services. It has been good working on Amazon EKS for the past three years.

Initially, I did not face challenges with the deployment of Amazon EKS, but I was not very familiar with Kubernetes initially, so it took me some time to understand Kubernetes first. The platform could be anything, including EKS, GKE, or AKS, but understanding Kubernetes was the first step necessary. After gaining familiarity with Kubernetes, Amazon EKS provided me with a good platform to work on, and I later cleared my CKA certification with a 95 percentile.

My impression of how stable and reliable Amazon EKS has been for me is positive. The upgrade process is also very fruitful, happening swiftly and without hassle or issues, so I have not faced numerous challenges. Moreover, the control plane is quite stable in Amazon EKS, and I find it to be 100% available.

Self-healing in Amazon EKS refers to what happens if any node becomes unreachable.

In our case, while I am not much sure about self-healing nodes, we utilize the cluster autoscaler in Amazon EKS. If any node is not ready, the cluster autoscaler ensures that it is removed from the AWS auto-scaling group and replaces it with a new node in the cluster.

My interaction with technical support and customer service at Amazon has been generally helpful, but they sometimes do not provide direct solutions. They tend to divert the conversation before giving a solution, which would be easier if they provided direct answers.

Once, we faced a situation where an EC2 instance got deleted due to unforeseen reasons. We reached out to support and inquired about the possibility of restoring our instance. They informed us that there was no possibility to bring it back, which felt like a dead end. I believe there should be a recovery solution available for at least a few hours so that we might bring it back; instead, we had to start again from scratch.

Positive

I usually participate in the deployment and setup of Amazon EKS. We create Amazon EKS clusters from scratch, deploy applications, and integrate them with all the AWS services.

Within the last 12 months, I have only been working with Amazon EKS. Currently, I have not been working with anything else other than Amazon EKS.

For the automated patching feature of Amazon EKS, we currently use EKS baked AMIs, which are straightforward to integrate with the Amazon EKS cluster. If we want to add additional packages or anything by boot time, it is very simple to do so while working with Amazon EKS. We can utilize a user data script to perform any patches, software upgrades, or installations, all accomplished at the instance creation time using EC2 node templates.

On a scale of 1-10, I rate Amazon EKS an 8.

Public Cloud

Amazon Web Services (AWS)

We use Amazon EKS mostly for deploying production workloads, and there are multiple AI models that we run on Amazon EKS.

My favorite feature of Amazon EKS is the ecosystem that it provides, including the integration with S3, along with EBS, and the networking that is smooth to run Kubernetes.

I have experience with Azure, and in comparison to Azure, a downside of Amazon EKS is that even if you want to deploy a dev workload or do some experimentation, we have to pay the charges for the control panel with no free option.

Additionally, I have faced many issues while configuring the node groups and the whole configurations; bringing up the nodes was a bit hectic, and I was not able to determine which node was failing and for what reason.

Specifically, the pricing for the control panel of Amazon EKS is hefty, and there is no cost-cutting that can be done on that side.

I have been using Amazon EKS in my career for four years overall.

Amazon EKS is pretty stable, and I have not seen any lagging, crashing, or downtime.

Amazon EKS is good in terms of scaling.

I have contacted technical support and customer support for Amazon EKS.

Positive

I have used Azure EKS, Civo, and also experimented with GCP as alternatives to Amazon EKS. If I have to rank between all three of them, GCP comes at number one, Amazon EKS comes at number two, and Azure EKS service comes at number three.

On the costing part of Kubernetes, Azure is beating Amazon EKS since I can do some experimentation without paying for the control panel; I can just pay for the node groups, which is an area where Amazon can improve.

From my point of view, the initial deployment of Amazon EKS is difficult.

I had to configure many components, such as IAM policies and other things; it was not a simple click-through process. The major issue is that there is no single point where I can see all the logs; while there is CloudWatch, it is not easily accessible, and you have to go through a hectic process to search and find information. There is nothing where you can go and click to get all the logs in a single place.

Amazon EKS requires maintenance on my end to continue functioning; we have to do upgradation from time to time since every Kubernetes version is only valid for one year.

On a scale from one to ten, I rate Amazon EKS a seven out of ten.

Public Cloud

Amazon Web Services (AWS)

We are currently using Amazon EKS as a production environment where we deploy multiple services because we serve banks using our services for identifying threat analysis and synthetic identity. Our main use case for Amazon EKS involves deploying our services based on scoring to determine whether a particular user is valid or invalid based on their social security number and any fraudulent entity they provide. We analyze this information and use Amazon EKS as the platform for deploying that application. We mainly deploy applications using Kubernetes deployments, and based on that, we deploy pods which contain the container that runs the application.

Amazon EKS helps us manage these deployments for threat analysis and synthetic identity because the controller part is managed by AWS. This addresses our concern about managing it ourselves, as we are managing a large environment where Amazon EKS ensures we can confidently manage our application rather than relying solely on the controller. We have implemented Karpenter, which manages the nodes and continuously monitors to see if any nodes or pods are underutilized or unable to load. It identifies the requirements from the pod side, automatically creates a node, and schedules the pod based on its resource needs. This automation allows us to respond quickly, especially during high load times when pods may get stuck or unable to upload images. The alerts help us react promptly, and another pod will be scheduled to fulfill the user's request within seconds.

The best feature Amazon EKS offers is automation, particularly with the automatic scheduling of pods on nodes. Karpenter, the new feature added recently that manages the node initialization, is something I really appreciate as it makes independent decisions based on the requirements, unlike older autoscaling configurations. Karpenter prioritizes cost-effectiveness by selecting the cheapest options for nodes, whether they are spot instances or on-demand. Additionally, whenever there is an issue, the pod can be automatically recreated using the defined replica set, providing a significant advantage of Kubernetes.

Karpenter helps keep costs down by consistently evaluating whether it should take on-demand instances or lower-cost alternatives. It constantly monitors for the lowest price model available in AWS. However, because the cheapest instance can be terminated with little notice, Karpenter quickly transitions to the next available on-demand instance. This proactive cost management is a key feature.

Amazon EKS has positively impacted my organization as Kubernetes offers orchestration of container-based applications, allowing us to rapidly deploy and fulfill user requests. During busy business days or promotional offers, we experience increased traffic, and Amazon EKS enables quick deployment of containers to meet this demand. If a pod is unresponsive, Amazon EKS can easily launch another pod to maintain service delivery. This adaptability not only enhances user service but also contributes to cost savings since we leverage Karpenter to manage nodes dynamically based on usage.

Since I joined this company ten months ago, we have reduced the number of failures significantly. Earlier, before using Karpenter, issues with pods hindered our efficiency and resulted in higher costs, as we did not have the option to minimize expenses effectively. Now, with Karpenter, costs have decreased and our efficiency has improved because we can swiftly address alerts and redeploy applications without delay. This transition has greatly improved our operational performance.

After adding Karpenter to our Amazon EKS setup, we have seen efficiency improvements of approximately thirty to forty percent, along with a reduction in our costs and a decrease in the number of issues our team encounters.

In my experience with Amazon EKS, I have not encountered aspects that require improvement, as AWS has invested intelligently in its development, especially with the addition of Karpenter. I do not have any particular improvement feedback for Amazon EKS or Kubernetes at this time.

I have been working in my current field for the last ten months. In terms of my total experience, I have been using Amazon EKS for the last three years, and in my current company, I have been using it for the last ten months.

Amazon EKS is stable in my experience.

Customer support for Amazon EKS is excellent, particularly at the enterprise level, as we can readily raise tickets and receive prompt responses. This interactive support is incredibly useful, allowing for quick resolutions and solutions through live sessions and screen sharing.

Positive

I have been using Amazon EKS since I started. Although I know about Docker Swarm, it poses issues for large environments like ours, which is why we opted for Amazon EKS.

We evaluated Docker Swarm before choosing Amazon EKS, as it is not well-suited for expanded environments and long-term deployments, making Amazon EKS the clear choice based on insights from my seniors.

I have experienced a return on investment with Amazon EKS. Since implementing Karpenter, costs have been reduced and operational efficiency has increased. We can deploy applications swiftly, and our monitoring tools such as DataDog alert us to any pod issues so we can act quickly. This responsiveness enables us to focus on critical issues that require our immediate attention.

I appreciate the overall pricing model of AWS, where you pay based on usage, which allows for a clear understanding of costs associated with services. The setup cost is reduced significantly since Amazon EKS simplifies the laborious process of arranging the controller plane, which typically requires substantial human resources and effort. Licensing is straightforward, making it easy to start using the service.

I would certainly recommend using Amazon EKS due to its managed services, which alleviate the complexities of controlling the Kubernetes cluster. The scalability features ensure issues with any pods are managed effectively by automatic relaunch processes, maintaining desired states. Karpenter's cost-efficient design is another highlight worth noting for anyone looking to balance container orchestration with spending.

Amazon EKS has proven to be an exceptional product, particularly as it gains popularity due to its scalability and rapid application deployment capabilities, benefiting organizations across various sectors. This review has been rated nine out of ten.

Public Cloud

Amazon Web Services (AWS)