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.

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.

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.

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

I'm actually working for a company that uses AWS as a cloud platform, and for our clients, we use Amazon EKS. We utilize multiple clusters and other requirements, making Amazon EKS our choice for deployment service or orchestration service.

The usual use case for Amazon EKS is to deploy an application intended for heavy user load and traffic. In technical terms, there are multiple services to choose from, but we choose Amazon EKS for its orchestration, load balancing, and auto-scaling capabilities. With this service, you don't have to worry about manual auto-scaling or manual load balancing. Before Kubernetes, manual intervention was needed for scaling applications, leading to potential crashes if capacity was exceeded. Amazon EKS alleviates those concerns with its auto-scaling feature, where predefined thresholds automatically trigger the launching of additional resources to handle increased traffic. Also, Amazon EKS allows configurations such as minimum and maximum server requirements, ensuring scalability while minimizing costs.

The features of Amazon EKS that I find most valuable include load balancing, auto-scaling, networking, security, and scalability.

Scalability in Amazon EKS refers to the ability to automatically scale up or down your application based on traffic needs. For instance, if you initially expect 10 users but suddenly have 20, Amazon EKS automatically handles the scaling, thereby preventing application crashes and maintaining service availability.

Reliability is crucial when running an application on Amazon EKS, as it ensures your application never crashes. With Amazon EKS, you don't manage the infrastructure yourself; Amazon takes care of it all. You simply need to deploy your container, select the required configurations, and Amazon EKS handles the rest without requiring you to manage the underlying resources.

I have utilized Amazon EKS's integration with IAM, which stands for identity and access management. IAM restricts access to services, ensuring only authorized personnel can access certain capabilities. This prevents mistakes or unauthorized actions, maintaining security throughout the platform.

The support for AWS tools integration in Amazon EKS influences our application development and management significantly. With integrated features related to security, scalability, and billing, we ensure the efficiency of our processes. At my company, we manage around 600 clusters on Kubernetes and emphasize reliability by integrating Amazon EKS with various third-party applications. This integration aids in deployment, security, and ultimately, efficiency, as it ensures that applications remain available and perform efficiently.

One area of Amazon EKS that could be improved is the manual process for adjusting the number of nodes. When I've already defined configurations in Docker or YAML files, it seems unnecessary to go back and make similar adjustments in the console.

I have been working with Amazon EKS for 4.7 years.

I do not often communicate with the technical support and customer service of Amazon EKS.

Currently, I am using GKE in Google Cloud, which is similar to Amazon EKS. The differences between GKE, Amazon EKS, and AKS mainly come down to minor functional variations; overall, they provide similar capabilities.

Regarding the pricing and licensing of Amazon EKS, I am not entirely certain, but from my perspective, it's somewhat comparable to AWS's compute instances. While it may be on the pricier side due to being a managed service provided by Amazon, the features and functionalities justify the cost, especially for applications requiring reliability and scalability.

I participate in the setup and deployment of Amazon EKS, though I don't do it directly through the console. I use a third-party application called Argo CD, which allows me to deploy Kubernetes applications without accessing the Amazon console directly, making the process efficient and straightforward.

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

Our usual use cases for Amazon EKS include IoT applications for edge computing devices, where we deploy some of our proprietary IoT applications to edge devices running in multiple locations, and artificial intelligence deployment to multiple systems, with a couple of them purely on the cloud where we manage bundled infrastructures into Amazon EKS for several proprietary customers.

The features and capabilities of Amazon EKS that I have found most valuable include the ease of deployment and the interesting part being the cost, which is not as expensive as setting up other cloud infrastructure.

Amazon EKS's support for AWS tools integration has influenced our application development and management process by being quite easy, with the integration being straightforward. Whenever issues arise, we talk to the support team who provide us with documentation, which is how we basically sort out most of those issues.

Amazon EKS's self-healing nodes help minimize administrative burdens in my organization by being wonderful and seamless, as it reduces the need for a whole lot of people on the team to handle issues, and it has really been seamless for us.

An area of Amazon EKS that could be improved in the future is its use for edge computing, which has been a small issue for us, especially since most of our recent work has been on edge computing applications such as Raspberry Pi and Jetson. If they could integrate things such as K3s, that would really be helpful as K8s feels a little bit bulky for edge computing deployment.

I have been working with Amazon EKS since last year, when we started moving some of our solutions to AWS EKS.

My experience with the stability and reliability of Amazon EKS has been very positive, with only a couple of intermittent shutdowns previously, but recently there have been no issues at all.

My impression of Amazon EKS's scalability has been positive, though we have not done very large-scale deployments. Most of what we've done has been on a much smaller but continuous scaling for multiple systems, and there has not been an issue on that aspect so far, although we haven't scaled up to a million or five million devices yet.

I often communicate with Amazon EKS technical support, as they have been our main go-to people.

An example of my interaction with Amazon EKS technical support was during the initial setup when we talked with them, and they provided us an easier route by suggesting how we should bundle our solutions in Docker for easy deployment.

Before Amazon EKS, I did not use a different solution for these use cases, as our path has always been with Amazon EKS.

My experience with the initial setup of Amazon EKS was straightforward with no challenges at all on my part, although the interns might complain about some snags. It's basically about studying the documentation.

My setup process involves building the application on GitHub, bundling it in Docker, and connecting with Amazon EKS.

I have seen a return on investment with Amazon EKS for time, as it has helped me save a significant amount of time. However, the cost side has not been as positive since some of the applications run in dollars, leading to complaints from customers and ourselves about the cost, particularly when providing services to customers across Nigeria and some African countries. The return on investment has not been great due to the foreign exchange rate, but for time savings, it has been wonderful in helping with deployment.

My opinion on the pricing and licensing of Amazon EKS is that it is quite varied, especially when doing projects in the African continent. It's quite expensive considering the local currency with respect to the conversions to dollars or euros, and if this could be lowered, it would help more deployments on our side with Amazon EKS.

Before choosing Amazon EKS, I evaluated other options or technologies, including Kubernetes on AWS, Google Cloud, and Microsoft Azure, but most of our experiences came from AWS, so we stayed with AWS.

I have not utilized Amazon EKS's integration with IAM solution.

I have not encountered specific benefits using Amazon EKS's automated patching feature for my Kubernetes clusters, but it has been satisfactory as we haven't actually had many issues with using Kubernetes.

The impact of Amazon EKS on my organization's ability to manage complex workflows effectively has been purely managed by my colleague, and it has been quite seamless with no issues on that particular aspect.

Some of the benefits and positive impact that I have received from Amazon EKS include getting cloud credits through Activate and certain deployments around migration, which have been quite beneficial, along with business support credit and support during certain issues. During the initial times of integrations and migrations, AWS connected us with more specialists in different locales with much more experience while also paying for their services.

Based on my overall experience with Amazon EKS, I would rate it an eight out of ten due to the lack of K3s from Rancher.

Our use cases for Amazon EKS include deploying and managing microservice-based applications, where Kubernetes excels at orchestrating microservices and Amazon EKS handles the heavy lifting of managing the control plane. We also use it for application modernization such as migrating legacy applications to containers and for hybrid and multi-cloud deployments, running Kubernetes workloads across on-premise and cloud environments. Additionally, we run secure and compliant workloads that require strict security and compliances, utilizing AWS IAM, VPC, and security services.

Furthermore, we leverage CI/CD pipelines to automate build, test, and deployment processes, and for machine learning, we implement SageMaker.

The most valuable features of Amazon EKS are the managed Kubernetes control plane, where AWS handles the provisioning, scaling, and maintenance, ensuring high availability and automatic patching. Integrations with AWS services offer seamless access such as IAM for access control, CloudWatch for monitoring, ELB and ALB for load balancing, and storage options including EBS, EFS, and S3. In terms of security and compliance, we utilize fine-grained access control through IAM role service accounts, support for private clusters, and network policies.

Amazon EKS supports both EC2 for full control over nodes and Fargate for serverless Kubernetes pods.

The positive impacts I have seen from using Amazon EKS include enhanced security and compliance with a managed control plane, automatic patching, updates, IAM integration for secure access to AWS services, private clusters, network policies, and encryption options. Additionally, I experience operational efficiency, scalability, performance, developer productivity, flexibility, portability, and observability and monitoring through CloudWatch, Prometheus, Grafana, and OpenTelemetry, which assists in troubleshooting issues and optimizing resources, ultimately leading to cost optimization.

Areas for improvement within Amazon EKS include the management of infrastructure. Prior to using Amazon EKS, we handled manual provisioning, patching, and scaling of our Kubernetes cluster, but now AWS manages control plane operations, automatic patching, and scaling, which has reduced our operational burden and resulted in fewer infrastructure-related incidents.

I believe only operational management could be improved in the next releases of Amazon EKS.

When it comes to stability and reliability in Amazon EKS, the reliability of the control plane managed by AWS is paramount, running across three availability zones in each region to ensure high availability and fault tolerance. AWS also automatically manages the scalability and health of crucial components such as the Kubernetes API server and etcd cluster. We have options for worker nodes, including auto mode, Fargate, managed node groups, and self-managed nodes, ensuring data plane reliability.

Regarding scalability in Amazon EKS, we see managed node groups and Fargate profiles, where we can automatically scale the number of EC2 instances in a node group using Cluster Autoscaler or Karpenter. For serverless pods, Amazon EKS can scale without managing EC2 nodes, and we can utilize horizontal pod auto-scaling based on CPU, memory, or custom metrics, along with support for cluster limits, multi-cluster, and multi-region load scalability.

Amazon EKS is highly scalable, showing improvement in areas such as infrastructure management, security, and cost efficiency, with features such as auto-scaling for pods and nodes, making it suitable for bursty and high-demand workloads.

Before using Amazon EKS, we relied on self-managed Kubernetes on EC2 as well as Docker Swarm for our workloads.

We decided to switch from Docker Swarm to Amazon EKS because it is a managed service that simplifies the handling of complex scalable and modern application workflows.

Setting up Amazon EKS for the first time involves prerequisites such as installing and configuring the Amazon CLI, then installing `kubectl`, and while `eksctl` is optional, I install it for easier setup. IAM permissions are also needed to create EKS resources.

My experience with the initial setup has been straightforward, and I did not face any challenges so far, especially with `eksctl`, although there are common challenges such as IAM role configuration, network complexity, and cluster access control.

We have managed to estimate savings of around 20 to 40% using Amazon EKS, specifically achieving savings on Fargate ranging from $30 to $45 per month based on our usage.

I consider Amazon EKS to be an affordable product overall.

Before choosing Amazon EKS, I did not evaluate other solutions as I found it to be the best one for us after checking the market.

The integration of Amazon EKS with IAM enhances our authentication process as IAM users or roles can be granted access to the Kubernetes API server, managed via the AWS Auth ConfigMap in the EKS cluster, allowing us to map IAM roles or users to Kubernetes RBAC roles.

When it comes to Amazon EKS integrating IAM into application development, we utilize IAM roles for service accounts that allow our application pods to securely access services such as S3 and DynamoDB without storing credentials. We first create a Kubernetes service account and associate it with IAM roles using annotations, enabling the pod to use this role to access AWS services via temporary credentials, providing a significant developer benefit by eliminating the need to manage secrets manually and ensuring access is secure and scoped per pod.

The benefits of Amazon EKS's automated patching feature for our Kubernetes clusters primarily include improved security through the automatic application of critical security patches to the control plane and worker nodes, which reduces exposure to known vulnerabilities such as CVEs and ensures compliance with security standards. A second benefit is the reduction of operational overhead, and thirdly, enhanced cluster stability, minimized downtime, and consistency across environments. With intelligent patch management, Amazon EKS often tests patches before release.

When it comes to managing complex workflows effectively on Amazon EKS, I find that it simplifies infrastructure management by abstracting away the complexity of managing Kubernetes control planes, allowing us not to worry about patching, scaling, or securing the master nodes. It also supports scalability for high-demand applications with auto-scaling features for both pods and nodes and provides enterprise-grade security.

I utilize the AWS EKS official documentation, accessible via docs.aws.amazon.com.

My impression of the documentation is that it is very easy to learn from scratch, making it accessible even for beginners, as it is comprehensive, well-structured, and production-ready. Especially for developers and DevOps engineers such as myself, we find the user guide, best practice guide, API reference, CI tools, and workshops to be highly reliable, developer-friendly, scalable, and flexible for deployment needs.

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

I have been working in my current field since I work in the cloud, and I worked for AWS and for Amazon EKS because all of my customers are using Amazon EKS or application container solutions.

My use cases for Amazon EKS involve working for an AWS partner, one of the biggest in LATAM, where we have many customers or clients around the world with different solutions. I have a client that had many applications in Amazon EKS, and I had the full administration of the cluster. I manage not only networking, pods, or resources but also security, monitoring, the billing for the expenses, access to the cluster, and to the AWS account. I think that is a big field that I can handle within Amazon EKS.

What I appreciate most about Amazon EKS is that I use tools such as Datadog for monitoring and reporting, and if I have a problem with the cost, I use Prometheus, Grafana, and Loki, as this Prometheus package is cheaper than Datadog. I use Helm to install packages or applications, making it easier and secure to install, uninstall, or update them. I also use Argo CD for CI/CD workflows. In general, these are the main package solutions that I use for Amazon EKS.

Regarding the downsides of Amazon EKS, I remember a case where I used a network add-on different from what is provided by AWS because the pods request one IP address for each pod. The solution had many pods, and the blocks in the VPC were limited. I didn't have enough addresses to assign to the pods, and I had to change the add-on to handle the IP address using another third-party solution not from AWS. This was one of the first challenges I encountered with Amazon EKS.

Since then, AWS still hasn't fixed this issue or given me an opportunity to use the IPs that I needed.

Basically, the problem was that we did not have enough IP addresses for the pods, and we had to change the network add-on in Amazon EKS.

I have been using Amazon EKS in my career for three and a half years since I worked in the cloud.

It took me approximately one month to learn how to use Amazon EKS. The tricky part was implementing Amazon EKS cluster completely from Terraform, as creating all of the resources is challenging. If you create the cluster from the AWS console, many resources are created behind the scenes, but in Terraform, you must create each resource one by one, which was quite difficult. Overall, it took about one month.

Regarding stability, I have not experienced any lagging, crashing, downtime, or instability with Amazon EKS. I think that Amazon EKS, and Kubernetes in general, is stable. I have had problems with billing because it's expensive, but not with stability.

When it comes to scalability, I use Carpenter with Amazon EKS because it is a tool that offers significant granularity for configuration, and it works really well and fast. The inherent scalability of Kubernetes is not the best for me based on resources, but Carpenter works really nicely.

I have contacted the technical support of Amazon EKS when I had issues with the IP addresses, and they helped me solve it by installing another network add-on. On a scale from 1 to 10, I would give the support of Amazon EKS a nine because it was nice and fast.

I have used a direct alternative to Amazon EKS, which is ECS in AWS. I have many clients using ECS, Elastic Container Service, which is the native service for containers in AWS. It's not the same as Amazon EKS; it's another orchestrator, but it works fine when your application is not big.

The initial deployment with Amazon EKS was easy, but I remember that my first deployment was done with a YAML file, just using kubectl. I used kubectl run and the YAML file, and after that, I learned about Argo CD, which made the process much easier.

Amazon EKS does require maintenance on my end. Last year, Kubernetes had many updates, which was a difficult task. This is why I use Helm to install all of the applications in the cluster. If the application is built for you, you can create the Helm chart for this application and install it using this tool. I think that is the best option when you need to update the cluster. I know that AWS now offers many new applications add-ons included in the console, making it easier, but I still think that maintenance is one of the most complicated aspects of the cluster.

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

The main use cases for Amazon EKS are that we use it normally in some new projects to optimize our costs. Instead of having many ECS services running, we prefer to set up a Kubernetes cluster and set everything there. For me, it is primarily for optimizing our resources.

What I find valuable about Amazon EKS is that it helps us manage all the Kubernetes. It isn't the workload, it is the main part of the Kubernetes, the head of all the cluster. Automatic updates are available, and we can set everything we created in AWS in Kubernetes, including IAM configuration. We can create policies such as creating a private endpoint for S3. The integration of Kubernetes with the AWS ecosystem is the best feature that Amazon EKS provides.

The IAM integration in Amazon EKS helps enhance the authentication processes because we can do this in a more granular way. Using IAM, you can set exactly what the service needs. If a service or application needs to upload objects or data to S3, connect to RDS, or perform other tasks, using IAM is the easiest way. The benefit is that it works in a granular way and it's easy to set up and validate. When you examine the permissions and rules to ensure everything has the correct permission at the correct moment, using IAM is perfect because you can validate and set up everything effectively.

Amazon EKS's support for different AWS tools integrations has accelerated our application development because we can think about all aspects comprehensively. We can architect using AWS services and objects, and Amazon EKS accepts this seamlessly. We don't need to translate the idea for AWS. We can write this idea using AWS objects and services, and Amazon EKS corresponds to that. It accelerates projects and is easy to manage because we can use Terraform to implement it.

I am using the self-healing nodes in the Amazon EKS solution. We have a client with a production workload running on spot instances. When a spot or node crashes, Amazon EKS starts a new node and moves everything before the node stopped. This self-healing is excellent because we don't experience disruptions. We don't face situations where a node stops and we need five minutes to start a new one. We use it in specific environments and can observe the difference when enabling or disabling Amazon EKS self-healing.

We are utilizing the automated patching in Amazon EKS. The valuable benefits I have experienced using the automated patching feature for the Kubernetes clusters directly increase security. Kubernetes typically releases patches focused on security rather than new features. It's beneficial because we can focus on our work without constantly thinking about new patch releases or upgrade deployments. Amazon EKS handles this automatically for us.

We face some issues with Amazon EKS when using the node group to control which nodes can start. We have a limitation where we need to set just one kind of instance - only large instances, only small instances, or only extra-large instances. This is a problem. It would be beneficial if we could specify that certain containers or services start on small instances rather than large ones.

I am uncertain whether Amazon EKS supports all LTS versions, and I think this would be something beneficial. Additionally, AWS has great AI features, so when we need to make updates to Amazon EKS, it would be helpful if AI could assist with planning, identifying migration requirements, and considering costs.

I have been working with Amazon EKS for about two years in production. Including study time and other experiences, I have been involved with it for approximately four years.

I faced challenges in the initial stages with Amazon EKS. The main challenge is that when we set up the cluster, it appears as a huge infrastructure just for a small application. When you set up Amazon EKS, it is configured at a large scale by default. You can't start small and gradually expand. This makes sense because for smaller applications, ECS works effectively. If you want a more integrated ecosystem, you can use Amazon EKS. The challenge lies in migrating everything, as you can't start using Amazon EKS on a small scale. It typically requires a big cluster with one, two, or three nodes. We also faced challenges with developers needing to adapt their mindset to the new way of doing things.

I have escalated questions to the technical support of Amazon EKS two or three times, and they always provided good solutions. When we don't understand the questions, we schedule a call to demonstrate the issue, and we always receive the correct answer.

I reached out for technical support with Amazon EKS because we faced issues starting a service. The way we declared the services was incorrect, but we weren't aware of this. We called AWS support for assistance. Another issue involved a security problem that we identified and reported to AWS.

I would rate the technical support of Amazon EKS a 10. The documentation is good, and when human interaction is needed, it's readily available.

From my perspective, I don't see any disadvantages of Amazon EKS compared to competitors in the market. Amazon EKS represents the state of the art. While Google has a powerful engine that offers more granular control, the additional configuration can be overwhelming. Amazon EKS balances the power of custom configuration with ease of setup.

I find the pricing of Amazon EKS complicated because I live in Brazil, where we use reals. With the exchange rate and taxes, the price appears six times higher. However, when viewed in dollars, it offers great features at reasonable pricing. Lower prices are always beneficial, and a reduction in hourly cost or promotional discounts would be appreciated, but the current price-to-benefit ratio is worthwhile.

My advice to other organizations considering Amazon EKS for their environment is to plan carefully. I strongly recommend planning and reading the documentation because Amazon EKS is resourceful and typically offers multiple ways to accomplish the same task. Careful planning, reviewing case studies for comparison, and thoughtful migration to Amazon EKS are worthwhile investments. Overall, I rate Amazon EKS a 9 out of 10.

I have a total of around four years of experience in multiple clouds, especially in AWS, and many times I used Amazon EKS for our multiple products and projects.

In our environment, we already have all the other infrastructure and services running on AWS, so we benefit from using Amazon EKS because the other services can easily communicate with it. For example, some of our services need to access S3, and our application objects reside there, so we can easily integrate them with Amazon EKS. We also use IAM rules for integration to provide granular access to resources. As per your question, in our environment, most of our clients and resources reside in AWS, which is why we prefer to deploy other services there, as most of our development environment uses Lightsail. This gives us an edge, allowing us to easily move from development to staging or production environments within the same cloud.

When we compare other clouds, AWS has an edge among all the crowded options right now. My observations and reviews with AWS also affirm this because it provides a user-friendly experience and offers many options that other clouds do not provide. When my team and I work with AWS, we always feel comfortable. I do not know the exact reason behind it; maybe we have a lot of previous experience, and we are familiar with AWS. That is why other reviews from my colleagues at previous companies indicate that AWS has some edge compared to other clouds.

I would recommend Amazon EKS to other organizations because it provides simple configuration, easy management, safety, granular access, and vast monitoring capabilities where we can easily monitor our clusters using CloudWatch. However, I would think about a clearer dashboard for Amazon EKS, but overall, I think it is sufficient.

When we need to deploy the application, we require a large number of instances. Therefore, I hope and believe I will not face out-of-capacity issues in AWS, especially since I have not yet experienced traffic around 50,000 plus, and I believe I will not face such issues in Amazon EKS the next time we deploy with a large number of nodes and worker nodes.

Additionally, the upgradation process of Kubernetes rapidly rolls out new releases, so it should be easier for our production environment to upgrade Amazon EKS clusters. Sometimes when we are going to upgrade the Amazon EKS cluster, we need to check the backups, and we should have options to export our configurations, such as exporting the configuration to S3 or somewhere else to find backups. Other tools, such as Velero, provide this functionality to back up configurations, so I hope this backup process will help us fulfill our backup policies and other requirements.

For the pricing aspect of Amazon EKS, one specific issue arises when we deploy applications, especially as we provide SaaS services to our clients. We would like to know the cost for each customer, but we face issues because AWS charges $70 USD for the Amazon EKS engine. We struggle to divide the worker nodes' fees and the engine cost among clients, as some users have low traffic and visibility while others have large amounts of visibility and traffic. Thus, we face cost-related issues when running multiple customers on the same Amazon EKS cluster.

I have been using the solution for approximately four years.

The initial setup and deployment of Amazon EKS was straightforward; I easily provisioned the correct cluster. Sometimes, due to the depreciation of the AMIs, AWS provides warnings to use the latest version of the EKS AMIs because some of our scripts or Terraform scripts are old. I think it is good practice for AWS to provide messages to the console to upgrade your cluster, but overall, my experience with provisioning the Amazon EKS cluster is good, and I highly appreciate it.

I have not faced any issues that require escalation to customer support, and until today, I do not feel any need to escalate anything to the support team. I am happy with the service.

To meet our requirements, our services need a large amount of CPU and memory, so we need high-spec machines. When we deploy applications, we require a large number of instances.

I have not faced any issues that require escalation to customer support, and until today, I do not feel any need to escalate anything to the support team. I am happy with the service.

I also work with other clouds, such as Oracle Cloud, but I feel comfortable with AWS because I faced some issues, such as out of capacity when we designed the infrastructure for our large traffic.

The initial setup and deployment of Amazon EKS was straightforward; I easily provisioned the correct cluster. Sometimes, due to the depreciation of the AMIs, AWS provides warnings to use the latest version of the EKS AMIs because some of our scripts or Terraform scripts are old. I think it is good practice for AWS to provide messages to the console to upgrade your cluster, but overall, my experience with provisioning the Amazon EKS cluster is good, and I highly appreciate it.

I used Amazon EKS through its console rather than through the AWS Marketplace.

I have not checked the pricing yet, but I will look into it to see if EKS brings ROI or a return on investment for us.

Regarding self-healing nodes in Amazon EKS, I have not worked on that self-healing feature.

For automated patching in Amazon EKS, I have not used that feature.

Regarding disadvantages of Amazon EKS compared to competitors in the market, I think every cloud provider has the same Kubernetes engine and worker nodes. However, I believe AWS provides a more user-friendly environment, which is why many of our customers are trying to deploy their infrastructure or applications on AWS. I do not think there is any specific reason not to prefer Amazon EKS.

I have not integrated IAM tools with Amazon EKS yet, but my other teams have. I think they used Okta, but I'm not certain about it. I have some demos from a long time ago, but I think Okta is for SSO.

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

For Amazon EKS, the usual use cases I have been working with include many microservices where we cannot orchestrate in a better way in the ECS, which is an AWS native component. The major reason for moving to Amazon EKS is that if we have a microservice that takes more time to do the job, we need to return it while also running some other small microservices in parallel with that application, which we cannot do in ECS.

We moved to Amazon EKS where we have the feasibility to do parallel jobs with different microservices within the same pod, since we can run multiple containers in one pod. This helps us mitigate challenges in the company, and it works smoothly and fast, providing good performance and strong security. That has been our journey towards Amazon EKS across all customer platforms.

The most valuable feature of Amazon EKS is the add-ons service, which includes the Ingress feature that allows us to connect to both public and internal-facing load balancers. The best thing I have found is the management; using Rancher management, we can connect to Amazon EKS and manage the deployment from the UI without always needing to use the AWS console. We can create our own Rancher portal and directly manage the deployments and all other tasks from there.

Amazon EKS is a major tool for our application functionality and job purposes; it helps us at the orchestration level, allowing us to not worry about the entire deployment and service migration. We manage everything from Amazon EKS, and it's also very convenient to set up CI/CD deployment through GitHub. Additionally, we can utilize AWS native services such as CloudDeploy, so in both ways Amazon EKS has been convenient for running our applications.

I believe there is room for improvement in Amazon EKS, particularly regarding security; if Amazon EKS would provide more options for cluster-based security, it would be beneficial. Currently, it's completely managed by AWS, but I suggest that if Amazon EKS could allow monitoring of the backend of the nodes or workflows, it would greatly help users. Sometimes, AWS's shared responsibility model means that if any issues arise, the misconception lies with the users.

For instance, failures from containers running on nodes in AWS data centers can halt our workflows, especially when running in single availability zones. If AWS could enhance user interaction for improving security, it would be very helpful.

I have been working with Amazon EKS since 2017, which amounts to approximately eight years.

I have utilized Amazon EKS's integration with IAM through the service account, which is a great feature because we don't need to depend on storing secrets in Amazon EKS. We can directly use service accounts and rotate our tokens every 24 hours, which helps us achieve fine-grained access for both the user and the cluster-wise.

Compared to different cloud providers, AWS EKS pricing and licensing is absolutely reasonable and one of the best options available.

In terms of stability and reliability, I can say that among all options in the market, Amazon EKS is very reliable. I haven't experienced much downtime in the Amazon EKS cluster, so I can confidently say it is stable.

Amazon EKS has been quite scalable; people nowadays are moving to multi-AZ setups. When we choose Fargate, the beauty of Amazon EKS is that we can run multiple containers from different availability zones within the same pod. This is beneficial for availability and scalability because, using ReplicaSet, we can ensure that our containers remain at the desired count, automatically pulling new containers whenever one goes down.

I often communicate with Amazon EKS technical support, and my impression is positive. When I was initially setting up, I needed to understand a few things from AWS, and they provided substantial support, being very professional and helpful.

I had to address technical support when one of our nodes was suddenly terminated while utilizing a single availability zone, which caused application downtime and business disruption. However, AWS management tackled this situation very well by providing us with a solution that enabled us to shift to multi-AZs, as AWS guarantees 99.9% availability but acknowledges the need for users to avoid relying entirely on a single AZ.

I usually participate in the initial setup and deployment of Amazon EKS; I've done this in four projects, giving me good hands-on experience with Amazon EKS.

My usual deployment and initial setup process for Amazon EKS is straightforward. First, we need to spin up the cluster, which involves providing the cluster name, VPC details, subnet details, security details, and network mode—either AWS VPC or a customized network mode. There are several options, including attaching an IAM role and adding on features such as proxy connection and network protection. After spinning the cluster, we start the Kubelet client installation from where we can manage the cluster and begin deployments using YAML scripts and manifest workflows.

Recently, Amazon EKS launched an automated patching feature that allows us to schedule a time frame for cluster scaling up, down, and patching, which has been very helpful across all aspects.

I usually measure the impact of Amazon EKS on managing complex workflows by evaluating performance, specifically how the containers are available and performing. Based on application latency, we can determine that Amazon EKS performs well. We measure in this way because latency is the differentiator between every service; with ECS, latency can be slightly higher, which causes user difficulties when accessing applications.

Overall, based on everything I've described about Amazon EKS, I would rate this solution eight out of ten.

My usual use cases for Amazon EKS are for my company's projects, as they instructed us to set up Amazon EKS and then deploy the applications.

I use Amazon EKS mainly for my company's projects, where we deploy applications according to company requirements.

The features and capabilities of Amazon EKS have proven to be valuable, as we use EKS in most of our projects. Our company has selected AWS as one of our three cloud preferences, which are AWS, GCP, and Azure.

The specific features I find most useful in Amazon EKS include the ability to deploy our applications directly using the pipeline file in YAML, the capacity to create multiple instances, and the capability to scale as per the requirement.

Amazon EKS's self-healing nodes help minimize administrative burdens in my organization by automatically creating a new node if any node crashes, allowing us to manage only the minimum and maximum nodes as needed.

To use Amazon EKS, we create the cluster first, and then we deploy the applications using the YAML file.

I have not used the automated patching feature for my Kubernetes clusters in Amazon EKS yet.

I think if new features, especially AI capabilities, are developed for Amazon EKS, it will enhance the product as it allows us to continually improve our applications.

I have been working with Amazon EKS for the past three years.

I participated in the initial setup and deployment of Amazon EKS.

My impression is that Amazon EKS is very stable and reliable as a product.

I have not noticed any outages, delays, or downtime with Amazon EKS; everything operates smoothly.

Amazon EKS is easy to evaluate in terms of scalability; we can auto-scale easily as needed.

I would rate the scalability of Amazon EKS as a nine out of ten.

I do not often communicate with the technical support of Amazon EKS as I have not needed their assistance.

For our work with Amazon EKS, we utilize the available documentation and guides, which are useful for understanding and starting with any AI tool or prompt.

I am satisfied with the documentation that Amazon offers.

Amazon EKS was my first Kubernetes platform. Prior to that, I had only used Minikube locally, and after that, I have worked exclusively with Amazon EKS.

During the setup process for Amazon EKS, I set it up locally by configuring AWS config, and then I focused on the necessary setups like kubectl.

To start working with Amazon EKS, I first configure it with AWS, install kubectl, and then begin working after creating the cluster.

I chose Amazon EKS to work with because I have not yet started using GCP's or Azure's Kubernetes services. I have experience only with Amazon EKS so far.

I have only worked on one application in Amazon EKS, so I haven't fully developed it. I have only deployed the front end, and I think I need to gather more knowledge about EKS.

The interface of Amazon EKS is very good, but I prefer to use the CLI for creating clusters. I initially used the UI just once to understand everything before starting to create using the CLI only.

On a scale of one to ten, I rate Amazon EKS a nine.