EKS & GenAI
Deploying an AI service on EKS means packaging it as a container, defining its resource limits, and fronting it with a load balancer and AI gateway.
Prerequisites
Overview
EKS is AWS’s managed Kubernetes control plane — it removes the operational burden of running Kubernetes itself, but the application-level concerns (GPU scheduling, autoscaling, gateway configuration) are the same as running Kubernetes anywhere else.
Where It Fits
User
Load Balancer
EKS Cluster
AI Service
AI Gateway
Key Points
- Managed control plane
- AWS operates the Kubernetes control plane, so a team manages worker nodes and workloads, not the cluster itself.
- GPU node groups
- EKS supports GPU-backed node groups for self-hosted model serving, provisioned separately from general-purpose nodes.
- IAM integration
- Pods on EKS can assume AWS IAM roles directly, useful for securely accessing Bedrock or other AWS AI services.
Interview Question
How would you deploy a scalable AI service on EKS?
I’d package the service as a container behind a load balancer and an EKS cluster, put any self-hosted model on GPU-backed node groups with autoscaling tied to a real inference metric, and front external model access with an AI gateway for routing, rate limiting, and fallback — using IAM roles for pods rather than static credentials for anything that needs to call AWS AI services like Bedrock.
Explain It in 30 Seconds
EKS is AWS’s managed Kubernetes control plane — deploying an AI service on it involves the same GPU scheduling and autoscaling concerns as Kubernetes generally, plus IAM role integration for accessing other AWS AI services securely.
Real-World Stack
Technologies commonly used to implement this in production.