How to Choose Between ECS, EKS and SageMaker on AWS

Teams deploying AI services on AWS usually end up comparing Amazon ECS, Amazon EKS and Amazon SageMaker. All three can run the workload. The right choice depends on what the service does and who will operate it after launch.

Step 1: Describe the workload

Answer three questions first:

• Does it call a hosted model API, or run a model itself? Services that only call Bedrock or another API are ordinary web services.
• Does it need GPUs? If so, only for inference, or for training too?
• Who will operate it? A platform team with Kubernetes experience, or an application team that wants as little infrastructure as possible?

Step 2: Consider ECS for API-calling services

ECS, especially with Fargate, runs containers without managing servers or a Kubernetes control plane. For an agent or RAG service that calls model APIs, it's often the simplest option: define a task, a service and a load balancer, then scale on requests or CPU. ECS can also use GPU instances through the EC2 launch type if you need them later.

Step 3: Consider EKS when you already have a platform

EKS makes sense when the organization already runs Kubernetes, needs portability across clouds, or runs components that benefit from the Kubernetes ecosystem, such as vLLM, Karpenter for GPU nodes and Argo CD for deployments. It offers the most control and demands the most operational skill.

Step 4: Consider SageMaker for model-centric work

SageMaker provides managed training jobs, a model registry and hosted inference endpoints with autoscaling. It suits teams that train or fine-tune their own models and want AWS to manage the serving infrastructure.

Step 5: Compare side by side

| Question | ECS | EKS | SageMaker | |---|---|---|---| | Operational effort | Low | High | Low to medium | | Control over serving | Medium | High | Lower | | Best fit | API-calling services | Existing Kubernetes platforms | Training and hosted models |

Step 6: Think about the handover

For forward deployed engineers, the customer's team inherits the system. Choose something they can operate. A perfectly tuned EKS setup becomes a liability if nobody on their side knows Kubernetes.

Things to watch

• Mixing is normal. An ECS service can call a SageMaker endpoint that hosts a custom model.
• Cost comparisons need real traffic numbers, including idle GPU time.
• Check GPU instance quotas early, whichever option you pick.

Start with the simplest option that meets the requirements, and move to more control only when you have a concrete reason.