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# Common use cases in Amazon EKS
<a name="common-use-cases"></a>

Amazon EKS offers robust managed Kubernetes services on AWS, designed to optimize containerized applications. The following are a few of the most common use cases of Amazon EKS, helping you leverage its strengths for your specific needs.

 **Deploying high-availability applications**   
Using [Elastic Load Balancing](https://aws.amazon.com/elasticloadbalancing/), you can make sure that your applications are highly available across multiple [Availability Zones](https://aws.amazon.com/about-aws/global-infrastructure/regions_az/).

 **Building microservices architectures**   
Use Kubernetes service discovery features with [AWS Cloud Map](https://aws.amazon.com/cloud-map/) or [Amazon VPC Lattice](https://aws.amazon.com/vpc/lattice/) to build resilient systems.

 **Automating software release processes**   
Manage continuous integration and continuous deployment (CI/CD) pipelines that simplify the process of automated building, testing, and deployment of applications. For declarative continuous deployment, see [Continuous Deployment with Argo CD](argocd.md).

 **Running serverless applications**   
Use [AWS Fargate](https://aws.amazon.com/fargate/) with Amazon EKS to run serverless applications. This means you can focus solely on application development, while Amazon EKS and Fargate handle the underlying infrastructure.

 **Executing machine learning workloads**   
Amazon EKS is compatible with popular machine learning frameworks such as [TensorFlow](https://www.tensorflow.org/), [MXNet](https://mxnet.apache.org/), and [PyTorch](https://pytorch.org/). With GPU support, you can handle even complex machine learning tasks effectively.

 **Deploying consistently on-premises and in the cloud**   
To simplify running Kubernetes in on-premises environments, you can use the same Amazon EKS clusters, features, and tools to run self-managed nodes on [AWS Outposts](eks-outposts.md) or can use [Amazon EKS Hybrid Nodes](hybrid-nodes-overview.md) with your own infrastructure. For self-contained, air-gapped environments, you can use [Amazon EKS Anywhere](https://aws.amazon.com/eks/eks-anywhere/) to automate Kubernetes cluster lifecycle management on your own infrastructure.

 **Running cost-effective batch processing and big data workloads**   
Utilize [Spot Instances](https://aws.amazon.com/ec2/spot/) to run your batch processing and big data workloads such as [Apache Hadoop](https://aws.amazon.com/emr/details/hadoop/what-is-hadoop/) and [Spark](https://aws.amazon.com/big-data/what-is-spark/), at a fraction of the cost. This lets you take advantage of unused Amazon EC2 capacity at discounted prices.

 **Managing AWS resources from Kubernetes**   
Use [AWS Controllers for Kubernetes (ACK)](ack.md) to create and manage AWS resources directly from your Kubernetes cluster using native Kubernetes APIs.

 **Building platform engineering abstractions**   
Create custom Kubernetes APIs that compose multiple resources into higher-level abstractions using [kro (Kube Resource Orchestrator)](kro.md).

 **Securing applications and ensuring compliance**   
Implement strong security practices and maintain compliance with Amazon EKS, which integrates with AWS security services such as [AWS Identity and Access Management](https://aws.amazon.com/iam/) (IAM), [Amazon Virtual Private Cloud](https://aws.amazon.com/vpc/) (Amazon VPC), and [AWS Key Management Service](https://aws.amazon.com/kms/) (AWS KMS). This ensures data privacy and protection as per industry standards.