Short answer: no — pushing a “worker image” to ECR and asking your engineer to make an ECS service from it is not enough by itself. In Prefect 3.x, the worker is just a long‑running Prefect process that submits ECS tasks which use your flow images. You’ll need an ECS work pool, AWS permissions, and job settings so the worker can launch your flow containers on ECS. Here’s the practical path forward. What you actually need - Your flow images in ECR (you already have this). - An ECS work pool in Prefect. - A Prefect worker process that polls that pool and calls ECS RunTask. - IAM set up so: - The worker’s AWS identity can RegisterTaskDefinition (optional), RunTask, DescribeTasks, and iam:PassRole for the task/execution roles. - The ECS task execution role can pull from ECR and write CloudWatch logs. - The ECS task role gives your flow code access to AWS services it uses (S3, etc.). - Network: ECS tasks must be able to reach your Prefect Server API (e.g., http://your-ec2:4200/api) via your VPC/NAT/ALB, etc. Two deployment patterns (pick one) 1) Run the worker on EC2 (simplest to start) - Install Prefect with AWS extras on your EC2:

pip install "prefect[aws]"

- Create an ECS work pool and set its job template:

prefect work-pool create my-ecs-pool --type ecs
  prefect work-pool get-default-base-job-template --type ecs > ecs-template.json
  # edit ecs-template.json: cluster, launch_type (FARGATE or EC2), cpu, memory,
  # execution_role_arn, task_role_arn, network_configuration, container_name, etc.
  prefect work-pool create my-ecs-pool --type ecs --base-job-template ecs-template.json --overwrite

- Start the worker on your EC2 (with AWS creds available via instance profile or env vars), pointing at your self-hosted server:

export PREFECT_API_URL=http://<your-prefect-server>:4200/api
  prefect worker start -p my-ecs-pool

2) Run the worker as an ECS service (if you really want everything “in ECS”) - Build a tiny image that just runs the worker:

FROM python:3.11-slim
  RUN pip install "prefect[aws]"
  ENV PREFECT_API_URL=http://<your-prefect-server>:4200/api
  CMD ["prefect", "worker", "start", "-p", "my-ecs-pool", "-n", "ecs-worker"]

- Push that image to ECR and have your engineer create a long‑running ECS service for it with an IAM role that allows RunTask/RegisterTaskDefinition/DescribeTasks and iam:PassRole. You still need the ECS work pool and flow images as above. Tie your deployments to ECS In your deployment call, reference the ECS work pool and your flow image. For example:

from prefect import flow

@flow
def myflow():
    ...

(
  myflow
  .from_source("<https://github.com/owner/repo>", entrypoint="flows/my.py:myflow")
  .deploy(
    name="prod",
    work_pool_name="my-ecs-pool",
    job_variables={
      "image": "<http://123456789012.dkr.ecr.us-east-1.amazonaws.com/flows:abc123|123456789012.dkr.ecr.us-east-1.amazonaws.com/flows:abc123>",
      "cluster": "my-ecs-cluster",
      "launch_type": "FARGATE",
      "cpu": 1024,
      "memory": 2048,
      "execution_role_arn": "arn:aws:iam::123456789012:role/ecsTaskExecutionRole",
      "task_role_arn": "arn:aws:iam::123456789012:role/myFlowTaskRole",
      "network_configuration": {
        "awsvpcConfiguration": {
          "subnets": ["subnet-abc", "subnet-def"],
          "securityGroups": ["sg-123"],
          "assignPublicIp": "ENABLED"
        }
      },
      "container_name": "flow"
    },
  )
)

You can set most of these once in the work pool’s base job template so deployments only override the image tag. IAM quick checklist - Worker (wherever it runs): ecs:RunTask, ecs:DescribeTasks, ecs:DescribeTaskDefinition, ecs:RegisterTaskDefinition (if you let Prefect auto-register), iam:PassRole for your task/execution roles. - Execution role on the task: AmazonECSTaskExecutionRolePolicy (includes ECR pull + CloudWatch logs). - Task role: whatever your flow code needs (S3, SNS, etc.). Recommendation for your case

- Since you’re new to ECS/ECR and already have EC2: start with the worker on your EC2 alongside Prefect Server. It’s simpler and gets you running faster. Later, if you really want, move the worker into an ECS service. Docs you’ll want on hand - Prefect 3 ECS guide: Run Prefect on AWS ECS A couple of clarifications that will save some head-banging: - Do you plan to use Fargate or EC2-backed ECS? - Can your ECS tasks reach your Prefect Server API endpoint from their subnets? - Will your worker run on EC2 first, or should we prep an ECS service for it now? Once you confirm those, I can give you the exact ecs-template.json fields to fill in. Otherwise, we can continue pretending “it should just work,” like all the best AWS demos.

Fargate is a great fit for on-demand runs. The worker just queues ECS tasks when a run is created; it doesn’t need to be “always scaling” like your flows do. Will an EC2-hosted worker crash under load? - Unlikely. The worker is lightweight; it submits/monitors ECS tasks, it doesn’t run your flow code. - You can cap throughput with a work pool concurrency limit so the worker never floods ECS:

prefect work-pool set-concurrency-limit my-ecs-pool 10

- If you expect big bursts, run multiple workers against the same pool (on EC2 or ECS); Prefect will distribute runs across them. How to do ECS Fargate cleanly 1) Create an ECS work pool and set a Fargate job template: - Create the pool:

prefect work-pool create my-ecs-pool --type ecs
  prefect work-pool get-default-base-job-template --type ecs > ecs-template.json

- In ecs-template.json, set at minimum: - launch_type: "FARGATE" - cluster: "my-ecs-cluster" - cpu/memory (e.g., 1024/2048) - container_name (e.g., "flow") - execution_role_arn (ECR pull + CloudWatch logs) - task_role_arn (permissions your flow needs) - network_configuration:

{
  "awsvpcConfiguration": {
    "subnets": [
      "subnet-abc",
      "subnet-def"
    ],
    "securityGroups": [
      "sg-123"
    ],
    "assignPublicIp": "ENABLED"
  }
}

- Apply it:

prefect work-pool create my-ecs-pool --type ecs --base-job-template ecs-template.json --overwrite

2) Start a worker - Easiest: run it on your existing EC2 (with an instance profile or AWS creds), making sure it can reach your Prefect Server API:

export PREFECT_API_URL=http://<your-prefect-server>:4200/api
  pip install "prefect[aws]"
  prefect worker start -p my-ecs-pool

- Or, run the worker as its own ECS service using a tiny image:

FROM python:3.11-slim
  RUN pip install "prefect[aws]"
  ENV PREFECT_API_URL=http://<your-prefect-server>:4200/api
  CMD ["prefect", "worker", "start", "-p", "my-ecs-pool", "-n", "ecs-worker"]

Your engineer creates a long‑running ECS service from that image with an IAM role that can ecs:RunTask, ecs:DescribeTasks, ecs:RegisterTaskDefinition (if you let Prefect auto-register), and iam:PassRole. 3) Point deployments at the pool and your ECR flow images - In code:

from prefect import flow

  @flow
  def myflow(): ...

  (
    myflow
    .from_source("<https://github.com/owner/repo>", entrypoint="flows/my.py:myflow")
    .deploy(
      name="prod",
      work_pool_name="my-ecs-pool",
      job_variables={
        "image": "<http://123456789012.dkr.ecr.us-east-1.amazonaws.com/flows:abc123|123456789012.dkr.ecr.us-east-1.amazonaws.com/flows:abc123>",
        "cluster": "my-ecs-cluster",
        "launch_type": "FARGATE",
        "cpu": 1024,
        "memory": 2048,
        "execution_role_arn": "arn:aws:iam::123456789012:role/ecsTaskExecutionRole",
        "task_role_arn": "arn:aws:iam::123456789012:role/myFlowTaskRole",
        "network_configuration": {
          "awsvpcConfiguration": {
            "subnets": ["subnet-abc","subnet-def"],
            "securityGroups": ["sg-123"],
            "assignPublicIp": "ENABLED"
          }
        },
        "container_name": "flow"
      },
    )
  )

You can keep most fields in the pool template and only override the image per deployment. Triggering on-demand via API - From your systems, create runs when needed: - CLI:

prefect deployment run myflow/prod --param key=value

- Python:

from prefect.deployments import run_deployment
    run = run_deployment(name="myflow/prod", parameters={"key": "value"})

- Or call the Prefect Server “create flow run from deployment” endpoint if you prefer raw HTTP. IAM checklist - Worker’s role: ecs:RunTask, ecs:DescribeTasks, ecs:DescribeTaskDefinition, ecs:RegisterTaskDefinition (optional), iam:PassRole to your task/execution roles. - Task execution role: AmazonECSTaskExecutionRolePolicy (ECR pull + CloudWatch logs).

- Task role: whatever your flows need (S3, Secrets Manager, etc.). Docs (highly recommended before AWS-induced despair) - Run Prefect on AWS ECS Two final sanity checks: - Can Fargate tasks reach http//<your prefect server>4200/api from their subnets? - Are you using Fargate public subnets with public IPs, or private subnets with NAT/ALB? Confirm those and I’ll give you a minimal ecs-template.json you can drop in.

Short answer: it can, if you let it. A Docker-type worker on EC2 launches your flow runs as local Docker containers on that same instance. If you allow too many runs at once or your flows are heavy, you can exhaust CPU/RAM/disk and the host can thrash or OOM-kill processes (including the worker). The worker itself is lightweight, but it shares the box with your runs. How to make it safe - Cap concurrency at the work pool:

prefect work-pool set-concurrency-limit my-docker-pool 5

Set this to something your EC2 instance can actually handle. - Enforce per-container limits in the Docker job template:

{
    "image": "<http://123456789012.dkr.ecr.us-east-1.amazonaws.com/flows:abc123|123456789012.dkr.ecr.us-east-1.amazonaws.com/flows:abc123>",
    "mem_limit": "1g",
    "container_create_kwargs": {
      "nano_cpus": 2000000000  // ~2 vCPU
    },
    "auto_remove": true,
    "stream_output": true
  }

Use

mem_limit

and CPU controls (

nano_cpus

via

container_create_kwargs

) to keep a single job from eating the whole box. - Scale horizontally or upsize: - Run multiple workers on separate EC2 instances against the same pool, or use a larger instance type. - Run the worker as a service with restarts: - systemd or Docker

--restart=always

helps it recover if the OS kills it. Not magic, but better than nothing. Since you want on-demand bursts, ECS Fargate is the safer option: the worker remains light, and the heavy lifting happens in Fargate tasks that scale independently of your EC2. Less chance of turning your EC2 into a very expensive space heater.