Deep Dive into Amazon ECS: Designing the “Just Right” Container Platform by Comparing It with GKE, AKS, Cloud Run, and Container Apps

Introduction (Key Takeaways)

• The star of this article is Amazon Elastic Container Service (Amazon ECS).
  ECS is a fully managed container orchestration service that makes it easy to deploy, manage, and scale containerized applications.
• ECS supports three launch / compute types: EC2 / Fargate / ECS Anywhere, which lets you run workloads consistently across cloud, on-prem, and edge environments.
• In the GCP world, GKE (Google Kubernetes Engine) plays the role of managed Kubernetes, and in Azure, AKS (Azure Kubernetes Service) occupies the same position. Both are services that “run Kubernetes itself in a managed way.”
• At an even higher level of abstraction, we also have the following “serverless container execution platforms”:
  • AWS: App Runner
  • GCP: Cloud Run
  • Azure: Azure Container Apps
    Understanding how these are positioned relative to ECS is an important part of the story.
• This article is aimed at:
  • Web/backend developers who want to start using containers in production
  • SREs and infrastructure engineers who want to clarify the differences between ECS / GKE / AKS
  • Tech leads and architects who are unsure which layer to adopt (ECS or App Runner / Cloud Run / Container Apps)
• By the end of the article, the goal is that you’ll be able to explain in your own words things like:
  • “For this use case, ECS is the right fit.”
  • “If we want this level of abstraction, we should use App Runner / Cloud Run / Container Apps.”
  • “If we truly need Kubernetes, we should choose GKE / AKS / EKS.”

1. What Is Amazon ECS? – A “Just Right” Orchestrator Optimized for AWS

1.1 Where ECS Fits in

Amazon ECS is a fully managed container orchestration service provided by AWS. It acts as a “control tower” to efficiently place, scale, and monitor containerized applications on AWS.

Roughly speaking:

• ECS
  • A closed, AWS-optimized orchestrator
  • Very simple integration with IAM / VPC / ALB / CloudWatch, etc.
• EKS (for reference)
  • Managed Kubernetes (OSS Kubernetes) on AWS
  • A choice when you want a “Kubernetes-native” design
• GKE / AKS
  • Both are managed Kubernetes services
  • A world where Kubernetes APIs and the Kubernetes ecosystem are the core abstractions

ECS is not Kubernetes, but that’s exactly why it has fewer concepts and is simpler, and for systems that are “AWS-only,” you can think of ECS as “the first candidate to consider.”

1.2 Main Use Cases for ECS

• Web APIs / backend (microservices)
• Batch processing / job execution
• Workers that consume queues (SQS / Kinesis, etc.)
• Game servers / real-time processing
• Machine learning inference / preprocessing

In other words, ECS can handle almost any “always-on or continuously running container.”

2. Core ECS Concepts: Cluster, Task Definition, Service

ECS is made up of a handful of core building blocks. Once you get used to the terms, it’s not that complicated.

2.1 Cluster

• A logical “box” in which containers are executed
• Inside a cluster, you can have:
  • EC2 instances (EC2 launch type)
  • Fargate capacity (Fargate launch type)
  • On-prem nodes registered via ECS Anywhere

2.2 Task Definition

• A template for running containers that specifies
  • which image,
  • which resources, and
  • which environment variables
    to use.
• A single task definition can contain multiple containers (for example, a sidecar pattern).

2.3 Task

• The actual running group of containers created based on a task definition.
• For those with Kubernetes experience, you can think of “one task ≈ one Pod” to get a feel for it.

2.4 Service

• A mechanism to say “keep at least N copies of this task running at all times.”
• When you create a service, ECS automatically takes care of:
  • health checks and restarts of tasks
  • auto scaling (ECS Service Auto Scaling)
  • integration with ALB / NLB (target registration)

If you understand these four pieces, you can read most basic ECS architectures without getting lost.

3. ECS Launch Types and Positioning Against Similar Services

3.1 The Three Launch Types: EC2 / Fargate / Anywhere

There are three main ways (launch types) to run containers with ECS:

1. EC2 launch type
   • Run containers on a group of EC2 instances you manage yourself.
   • Good when you want to deeply tune OS, middleware, disk layout, etc.
2. Fargate launch type
   • A serverless compute engine where you can run containers by simply specifying vCPU and memory, without thinking about EC2 instances.
   • You’re freed from patching, scaling, and capacity management of instances, but the per-unit cost is often a bit higher than EC2.
3. ECS Anywhere
   • A way to register on-prem and other-cloud nodes into an ECS cluster and manage them with the same control plane as in AWS.

In simple terms:

• If you want to minimize infrastructure management → Fargate
• If you have strict cost or special requirements → EC2
• If you want unified management across hybrid / edge → ECS Anywhere

3.2 Positioning vs Kubernetes-Based Services (ECS vs GKE / AKS)

• ECS
  • AWS’s proprietary orchestrator with relatively few concepts to learn.
• GKE (GCP) / AKS (Azure)
  • Both are managed Kubernetes. You can fully leverage Kubernetes APIs, manifests, Helm, and other OSS ecosystem tools.

If your team is strong in Kubernetes, it may be more cost-effective in learning terms to standardize on:

• AWS: EKS
• GCP: GKE
• Azure: AKS

On the other hand, if your systems are primarily on AWS and you don’t particularly care about “Kubernetes itself,” ECS is often the simpler choice.

3.3 Comparison with Higher-Level Serverless Container Services

One layer above ECS, you have:

• AWS App Runner: A fully managed container application service that builds, deploys, and scales from source or container images.
• GCP Cloud Run: A serverless platform for HTTP/event-driven containers. Excellent at scaling to zero.
• Azure Container Apps: A serverless container execution platform. Scales based on HTTP, events, or CPU/memory load using KEDA-based scalers.

These services are more “PaaS-like.” As a rough guide:

• If you want to touch infrastructure and clusters as little as possible → App Runner / Cloud Run / Container Apps
• If you need VPC, ALB, fine-grained network control, or custom job handling → ECS / GKE / AKS

This distinction helps keep your thinking organized.

4. ECS Architecture Examples: Web APIs, Batch, Event-Driven Workers

Let’s briefly look at three representative ECS patterns to help you visualize how it’s used.

4.1 A Simple Web API on Fargate

• Build an ALB + ECS (Fargate) cluster inside a VPC.
• Traffic flow: ALB → ECS service (multiple tasks).
• The task definition includes:
  • Docker image (pulled from ECR)
  • Resources such as 0.25 vCPU / 0.5 GB
  • Environment variables (e.g., DB connection info from Secrets Manager)

Developers can focus on Dockerfile and application code, and deployment can be as simple as running ecs deploy from CI/CD.

4.2 Batch / Job Processing

• In a shared ECS cluster, you can run:
  • A long-running web service as an ECS service
  • Batch jobs as scheduled tasks (EventBridge + RunTask)
• For example, “every hour, pull a file from S3, run ETL, and load into RDS” can be modeled as a one-off ECS task.

On GCP, the equivalents would be GKE Jobs or Cloud Run Jobs; on Azure, Jobs in AKS or Container Apps play similar roles.

4.3 Event-Driven Workers (SQS / Kinesis)

• Run workers in ECS that poll an SQS queue, and
  • use Service Auto Scaling to increase/decrease the number of tasks based on the number of messages.
• On GCP, you can do similar things with Cloud Run + Pub/Sub push or GKE + Pub/Sub subscriptions; on Azure, with Container Apps + Event Hub / Service Bus, etc.

5. A Simple Task Definition Example (Just to Get a Feel for It)

Details will vary by environment, but here is a rough example of a task definition for Fargate:

{
  "family": "sample-api",
  "networkMode": "awsvpc",
  "requiresCompatibilities": ["FARGATE"],
  "cpu": "256",
  "memory": "512",
  "executionRoleArn": "arn:aws:iam::123456789012:role/ecsTaskExecutionRole",
  "taskRoleArn": "arn:aws:iam::123456789012:role/app-task-role",
  "containerDefinitions": [
    {
      "name": "app",
      "image": "123456789012.dkr.ecr.ap-northeast-1.amazonaws.com/sample-api:latest",
      "portMappings": [{ "containerPort": 8080 }],
      "essential": true,
      "environment": [
        { "name": "APP_ENV", "value": "prod" }
      ],
      "logConfiguration": {
        "logDriver": "awslogs",
        "options": {
          "awslogs-group": "/ecs/sample-api",
          "awslogs-region": "ap-northeast-1",
          "awslogs-stream-prefix": "app"
        }
      }
    }
  ]
}

• Application IAM permissions are attached via taskRoleArn.
• Logs are sent to CloudWatch Logs.
• In the ALB target group, you configure port 8080.

Based on this task definition, you create an ECS service and specify something like “keep at least two tasks running,” and ECS will handle replacements on failure and during scaling for you.

6. Networking and Security: VPC, IAM, Logging

6.1 VPC with ALB/NLB

ECS (especially Fargate) is designed with the assumption that it runs inside a VPC.

• Traffic: ALB/NLB → ECS service (awsvpc mode)
• Security groups control the flows.
• Tasks are placed in private subnets, and access from the internet comes via an ALB in public subnets.

On GKE, you’d typically do this with VPC-native clusters and internal/external load balancers; on AKS, with VNet integration and Azure Load Balancer / Application Gateway. The core idea is very similar across clouds.

6.2 IAM Roles and Task Roles

• In ECS, you can assign an IAM role to each task (task role).
• For example, if an app only needs S3 read access:
  • You create a role with only s3:GetObject and assign it to the task.
  • You don’t give that permission to EC2 instances or other resources.
  • This implements least-privilege design where “only this app can read from S3.”

On GCP, think Workload Identity + service accounts; on Azure, think managed identities.

6.3 Logs and Metrics

• Typically, application logs are sent to CloudWatch Logs.
• CPU and memory usage are automatically collected as CloudWatch metrics and can be used for:
  • Auto scaling triggers
  • Alarms
  • Dashboards

On GKE, the equivalents are Cloud Logging and Cloud Monitoring; on AKS, Log Analytics and Azure Monitor. Across clouds, the pattern of sending container logs and metrics to a managed monitoring service is the same.

7. CI/CD and Deployment Strategies: ECR / CodePipeline vs GCP / Azure

7.1 A Typical ECS Deployment Pipeline

1. Git push
2. CI (GitHub Actions / CodeBuild, etc.) builds the container image
3. Push the image to Amazon ECR
4. Call aws ecs update-service to switch the task definition revision
5. ECS service performs a rolling deployment (replacing tasks while maintaining minimum capacity)

With AWS CodePipeline / CodeDeploy, it’s relatively easy to configure blue/green deployments and canary releases.

7.2 In GCP / Azure

• GCP: Cloud Build builds → Artifact Registry → deploy to GKE / Cloud Run. With Cloud Deploy, you can orchestrate progressive delivery across multiple environments.
• Azure: Azure DevOps / GitHub Actions → Azure Container Registry → deploy to AKS / Container Apps.

In all cases, the underlying pattern is the same: “push to a registry → apply a new version to the orchestrator.”

8. Cost and Scaling: Fargate vs EC2 and Differences from GKE/AKS

8.1 Trade-Offs Between Fargate and EC2

• Fargate
  • No instance management.
  • A good fit for a small number of services, bursty workloads, or dev environments you want to shut down at night.
• EC2
  • You manage instance sizes, counts, and lifecycles yourself.
  • For long-running, large-scale workloads, you can often optimize costs more aggressively with Reserved Instances or Savings Plans.

GKE has a similar split between:

• Standard mode + self-managed nodes, and
• Autopilot mode (more serverless-like)

AKS also offers virtual nodes and serverless options. In every cloud, the central question is where to place the slider between “ease of infra management” and “per-unit cost.”

8.2 Scaling Strategies

• ECS Service Auto Scaling
  • Automatically adjusts the number of tasks based on CPU/memory usage, ALB request counts, SQS message counts, etc.
• GKE / AKS
  • HPA (Horizontal Pod Autoscaler) and Cluster Autoscaler to adjust Pod counts and node counts.
• App Runner / Cloud Run / Container Apps
  • Scale in a fully serverless way based on request rates, event rates, or CPU/memory pressure, sometimes down to zero when idle.

When you choose ECS, it’s important to design how fine-grained you want your container-level scaling to be, so you can balance cost and performance.

9. Who Benefits and How? – By Reader Type

9.1 Application Developers (Web APIs / Backend)

• If you’re already building apps with Docker but feel Kubernetes is too heavy,
  ECS (Fargate) + ALB is very approachable.
• Once you learn task definitions, you can:
  • deploy existing Docker images to ECS directly, and
  • quickly wire up logging, metrics, scaling, and WAF integration.

9.2 SREs / Infrastructure Engineers

• In an AWS-centered organization, you can combine:
  • ECS + Fargate / EC2
  • IAM / VPC / CloudWatch / X-Ray / WAF
    to build a standard container application platform template.
• As a choice that doesn’t fully commit you to Kubernetes, ECS offers a very balanced option.

9.3 Tech Leads / Architects

• With multi-cloud in mind, you need an internal “map of orchestration layers” like:
  • AWS: ECS or EKS
  • GCP: GKE or Cloud Run
  • Azure: AKS or Container Apps
• Once you understand ECS as one piece of that map, it becomes easier to compare clouds calmly in terms of “which layer does this service correspond to?”

10. Design Checklist (What to Solidify in the First 1–2 Weeks)

1. Decide which layer to use
   • ECS? Or go further up to App Runner / Cloud Run / Container Apps?
2. Choose the launch type
   • Which will be primary: Fargate / EC2 / Anywhere?
3. Cluster design
   • Will you separate clusters by prod / staging / dev? What about account and VPC separation?
4. Task role design
   • For each app, which AWS resources does it need and what actions (e.g., S3 GetObject only, DynamoDB PutItem only)?
5. Network and ALB/NLB design
   • Public/Private subnet usage, whether to expose to the internet, WAF integration.
6. Logs, metrics, and tracing
   • Naming and retention for CloudWatch Logs groups, metric alarms, X-Ray or other APM tools.
7. Deployment strategy
   • Rolling, blue/green, or canary?
8. Scaling policies
   • Based on CPU/memory, or on business metrics like queue length or request rate?
9. Security and update policy
   • Base image updates, vulnerability scanning, ECR lifecycle policies.
10. Alignment with future multi-cloud strategy
    • If you later want to move to a Kubernetes-based stack, roughly which parts would move from ECS to EKS/GKE/AKS?

11. Three Steps You Can Start Today

1. Pick one existing container application
   • Choose a small app and sketch out on paper, “what would it look like to run this on ECS (Fargate)?”
2. Write one task definition
   • Using the console or CLI, create a task definition that includes an ECR image, port, environment variables, and logging configuration.
3. Build your own correspondence table with GKE / AKS / Cloud Run / Container Apps
   • Make your own mapping like: “ECS cluster ≒ GKE cluster,” “task ≒ Pod,” “service ≒ Deployment + Service,” “Fargate ≒ something like GKE Autopilot,” etc.
     This will make you much less confused when switching clouds.

12. Conclusion: ECS Is the First Candidate for an “AWS-Native Container Platform”

As we’ve seen, Amazon ECS is:

• A fully managed orchestrator deeply integrated with AWS, and
• Through EC2 / Fargate / Anywhere, it can handle:
  • traditional IaaS,
  • serverless containers, and
  • hybrid / edge workloads
    under a single control plane.

Meanwhile, Kubernetes-based worlds like GKE / AKS / EKS and serverless worlds like App Runner / Cloud Run / Container Apps also have their own strengths. The important questions are:

• “How much of the operations do we want to own ourselves?”
• “How much vendor lock-in are we willing to accept?”
• “Which layer is our team best at?”

If you answer those honestly and then draw a “layer map” that includes ECS and the other services, you’ll be in a much better position to choose wisely.

When you move on to other AWS services, try asking, “What is this closest to in GCP? In Azure?” as you read. That habit will nurture a cross-cloud way of thinking that works well in the multi-cloud era.

For now, take a moment to give yourself credit for reading this far. Maybe grab a cup of tea and quietly think: “For our team, how far up the abstraction ladder do we want to go?” and consider where ECS and the other services sit in that picture.

References (Titles Only)

• What Is Amazon Elastic Container Service?
• Launch types and capacity providers in Amazon ECS
  • Launch types / Capacity providers
• Google Kubernetes Engine Overview / Product Page
  • Overview / Product Page
• What Is Azure Kubernetes Service (AKS)? / Core concepts
  • What Is AKS / Core concepts
• What Is AWS App Runner?
• Cloud Run documentation / Product Page
  • Documentation / Product Page
• Azure Container Apps overview / documentation
  • Overview / Docs hub

※ For specific pricing, limits, and the latest features, always check the official documentation and pricing pages of each cloud provider.