Terraform AWS ECS Fargate Module Alpha Deployment and Step Functions Integration

AWS Fargate is presented as a serverless compute engine that supports several common container use cases, like running micro-services architecture applications, batch processing, machine learning applications, and migrating on premise applications to the cloud without having to manage servers or clusters of Amazon EC2 instances. The compute model removes the operational burden of provisioning and maintaining the underlying host infrastructure. AWS customers have a choice of fully managed container services, including Amazon Elastic Container Service and Amazon Elastic Kubernetes Service. Both services support a broad array of compute options, have deep integration with other AWS services, and provide the global scale and reliability you’ve come to expect from AWS. For more details to choose between ECS and EKS please refer this blog. The pairing of Terraform with AWS Fargate creates an infrastructure as code path for defining, versioning, and planning container workloads that are executed without servers. The reference material anchors on an AWS official module that is explicitly marked as alpha and on a HashiCorp blog example that demonstrates running an Amazon ECS Task on AWS Fargate initiated using AWS Step Functions.

Module Alpha State and Production Considerations

The aws-ia/terraform-aws-ecs-fargate module is in alpha state and is likely to contain bugs and updates may introduce breaking changes. It is not recommended for production use at this time. The direct fact is the alpha classification and the associated risk statement. The impact layer for a practitioner is that any pipeline built against this module must be treated as experimental. Changes in module version can alter resource definitions, variable names, or default behaviours without a deprecation window. Teams that attempt to use the module for production workloads inherit instability in state files, potential resource drift, and the need for manual remediation after updates. The contextual layer connects this warning to the deployment instructions that follow. Because the module is alpha, the documented workflow includes explicit workspace setup steps and a separate deploy directory. The separation allows users to test the module in isolation before applying it to a target VPC. The alpha status also frames the recommendation to pass vpc_id directly to the Fargate module when an existing VPC is present, which reduces the scope of changes introduced by the module.

Prerequisites and Authentication Chain

To deploy the Terraform Amazon Fargate module, do the following. The prerequisite list is ordered to establish tooling, credentials, and repository access before any Terraform execution.

Install Terraform. For instructions and a video tutorial, see Install Terraform. Terraform is an infrastructure as code tool similar to AWS CloudFormation that allows you to create, update, and version your Amazon Web Services infrastructure. Terraform provide friendly syntax along with other features like planning, graphing, create templates to break configurations into smaller chunks to organize, maintain and reusability. The impact of installing Terraform is that the user gains the ability to preview changes before they happen, which is critical when working with an alpha module.

Sign up and log into Terraform Cloud. There is a free tier available. Configure Terraform Cloud API access. Run the following to generate a Terraform Cloud token from the command line interface:

terraform login

For Mac/Linux the configuration path is set as follows:

export TERRAFORM_CONFIG="$HOME/.terraform.d/credentials.tfrc.json"

For Windows the configuration path is set as follows:

export TERRAFORM_CONFIG="$HOME/AppData/Roaming/terraform.d/credentials.tfrc.json"

The impact of configuring Terraform Cloud is that remote runs can be executed in a controlled environment and state is stored remotely. The contextual link is that the deployment step later notes that the deployment is remotely run in Terraform Cloud.

Configure the AWS Command Line Interface. For more information, see Configuring the AWS CLI. The AWS CLI provides the credentials and region context that Terraform uses to interact with AWS APIs. Without proper CLI configuration, the subsequent Terraform apply will fail authentication.

If you don't have git installed, install git. Clone this aws-ia/terraform-aws-ecs-fargate repository using the following command:

git clone https://github.com/aws-ia/terraform-aws-ecs-fargate.git

Change directory to the root repository directory.

cd terraform-aws-ecs-fargate/

The impact of cloning the repository is that the local file system contains the module source, examples, and workspace setup. The context connects to the later step of moving into setup_workspace and deploy directories.

Workspace Setup and Initialization Flow

For setting up a new terraform workspace:

cd setup_workspace

Initialize the deploy directory. Run

terraform init

Run

terraform apply

The workspace step creates the foundational resources required for the module to operate. The impact is that a failure here blocks all downstream deployment. The contextual layer notes that the workspace is separate from the deploy directory, which suggests a two-stage provisioning pattern where networking or IAM prerequisites are established first.

To create new VPC and deploy Fargate module:

Change to the deploy directory. Run

cd ../deploy

Initialize the deploy directory. Run

terraform init

Start a Terraform run using the configuration files in your deploy directory. Run

terraform apply

The deploy directory contains the configuration that references the Fargate module. The impact is that this is where the actual ECS cluster and Fargate resources are created. The context of running terraform init ensures provider plugins are downloaded and backend configuration is applied.

An alternative invocation with a variable file is noted:

terraform apply -var-file="$HOME/.aws/terraform.tfvars"

Note: The deployment is remotely run in Terraform Cloud. The impact is that the apply operation may be executed asynchronously and outputs are visible in the Terraform Cloud UI. The contextual connection is to the earlier Terraform Cloud token configuration.

For existing VPC pass vpc_id directly to the Fargate module. Change to the deploy directory. Run

cd ../deploy

The impact of passing vpc_id is that the module does not attempt to create a new VPC, reducing resource creation time and avoiding networking conflicts. The context is that this option exists because the module supports both greenfield and brownfield network scenarios.

Fargate Compute Model and ECS Integration

AWS Fargate is a serverless compute engine for containers that works with both Amazon Elastic Container Service and Amazon Elastic Kubernetes Service. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design. Amazon Elastic Container Service is a highly scalable, high performance container management service that supports Docker containers. Amazon ECS a scalable, high performance container management service that supports Docker containers that are provisioned by Fargate to automatically scale, load balance, and manage scheduling of your containers for availability.

The direct fact is that Fargate is serverless for containers. The impact layer is that teams no longer size EC2 instances, patch operating systems, or manage capacity providers for a single workload. The contextual layer links to the Terraform module that provisions this compute without servers.

An ECS Fargate cluster in Terraform starts with awsecscluster, capacity providers for Fargate and Fargate Spot, and supporting IAM roles. Enable Container Insights for monitoring, configure ECS Exec for debugging, and set up a Cloud Map namespace if your services need to discover each other. The cluster is the foundation - the real work happens in task definitions and services, which we cover in our guides on creating ECS task definitions and ECS services with load balancers.

The choice of EC2 launch type when you need GPU support, need to optimize costs for steady-state workloads, or need specific instance types is presented as an alternative. You can mix both in the same cluster by adding EC2 capacity providers alongside Fargate. The impact is that organizations can keep GPU intensive workloads on EC2 while moving stateless microservices to Fargate. The context connects to the capacity provider mention above.

Terraform as Infrastructure Code for ECS Workflows

The solution presented in this post uses Terraform by HashiCorp, an AWS Partner Network Advanced Technology Partner and member of the AWS DevOps Competency. Terraform is an infrastructure as code tool similar to AWS CloudFormation that allows you to create, update, and version your Amazon Web Services infrastructure. Terraform provide friendly syntax along with other features like planning, graphing, create templates to break configurations into smaller chunks to organize, maintain and reusability.

We will leverage the capabilities and features of Terraform to build an API based ingestion process into AWS. The example solution leverages Amazon ECS a scalable, high performance container management service that supports Docker containers that are provisioned by Fargate to automatically scale, load balance, and manage scheduling of your containers for availability. For defining the infrastructure, you can use AWS CloudFormation, AWS CDK or Terraform by HashiCorp.

The impact of using Terraform for this workflow is version control of infrastructure, peer review of changes, and repeatable deployments across accounts. The contextual layer ties the HashiCorp blog example to the Step Functions orchestration described later.

Step Functions Orchestration with ECS Fargate

In this blog, we will walk you through a use case of running an Amazon ECS Task on AWS Fargate that can be initiated using AWS Step Functions. The example solution leverages Amazon ECS a scalable, high performance container management service that supports Docker containers that are provisioned by Fargate to automatically scale, load balance, and manage scheduling of your containers for availability.

Deploying the Terraform template to spin up the infrastructure

Download the code from the GitHub location.

git clone https://github.com/aws-samples/aws-stepfunctions-ecs-fargate-process

Take a moment to review the code structure as mentioned above in the walkthrough of the solution. In the “exec.sh” script/bash file provided as part of the code base folder, make sure to replace with your AWS account number and the with your AWS account region. This will create the infrastructure and pushes the java application into the ECR. Last section on the script also creates sample/dummy input files for the source S3 bucket.

cd aws-stepfunctions-ecs-fargate-process

chmod +x exec.sh

./exec.sh

The impact of the exec.sh script is that it automates Terraform execution, ECR image push, and seed data creation in one step. The context is that the Step Functions state machine triggers an ECS task on Fargate, which processes files from an input S3 bucket.

We will provide the Terraform infrastructure definition and the source code for a Java based container application that will read and process the files in the input AWS S3 bucket. The files will be processed and pushed to an Amazon Kinesis stream. The Kinesis stream is subscribed to an Amazon Data Firehose which has a target of an output AWS S3 bucket.

The impact is a complete event driven pipeline where Fargate tasks are ephemeral workers. The context ties back to the serverless nature of Fargate and the managed nature of Kinesis and Firehose.

Validate the result in logs and the output in S3 bucket.

Cleanup Script, that will clean up the AWS ECR, Amazon S3 input files and destroy AWS resources created by the Terraform.

By creating the infrastructure shown in the diagram below, you will incur charges beyond free tier. Please see below pricing section for each individual services’ specific details. Remember to clean up the built infrastructure for the purposes of completing this tutorial to avoid any recurring cost.

Supporting AWS Services in the Reference Architecture

Overview of some of the AWS services used in this solution

Amazon Elastic Container Service, a highly scalable, high performance container management service that supports Docker containers
AWS Fargate is a serverless compute engine for containers that works with both Amazon Elastic Container Service and Amazon Elastic Kubernetes Service. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information.
Amazon Virtual Private Cloud is a service that lets you launch AWS resources in a logically isolated virtual network that you define

The direct facts list four services. The impact layer is that VPC provides network isolation, Kinesis provides streaming ingestion, and Fargate provides compute without servers. The contextual layer connects the VPC mention to the earlier instruction to pass vpc_id to the module and to create a new VPC when desired.

Prerequisites and Service Mapping

The following table summarizes the prerequisites and their purpose in the deployment chain.

The table provides a structured view of the sequence. The impact is that each step depends on the previous one. The context is that the workspace step is separated from deploy to allow incremental validation.

The following table maps services to their role in the Step Functions ECS Fargate example.

The table reinforces the end-to-end data flow from S3 input through Fargate processing to Kinesis and Firehose output.

Monitoring, Debugging and Service Discovery Considerations

The direct fact is the recommendation to enable Container Insights, ECS Exec, and Cloud Map. The impact is observability into task metrics, the ability to exec into running containers without SSH, and service discovery for inter-service communication. The context is that these are post-cluster steps that depend on a functioning cluster foundation.

The cluster is the foundation - the real work happens in task definitions and services. The impact is that module deployment is only the first phase. The contextual layer ties to the earlier note about task definitions and services with load balancers.

Conclusion

The reference material presents Terraform AWS Fargate as a two-part story. One part is the aws-ia/terraform-aws-ecs-fargate module, which is explicitly marked as alpha and therefore carries breaking change risk, bug exposure, and a production use discouragement. The deployment path for that module is gated by a precise toolchain setup: Terraform installation, Terraform Cloud authentication with explicit credential file paths for Mac/Linux and Windows, AWS CLI configuration, git availability, repository cloning, and a two-stage Terraform workflow that first builds a workspace and then moves into a deploy directory where terraform init and terraform apply are executed, optionally with a variable file for tfvars. Passing vpc_id directly is offered as a brownfield option, which reduces the scope of networking changes introduced by an alpha module.

The second part is the HashiCorp blog example that couples Terraform with AWS Step Functions to initiate ECS tasks on Fargate. The architecture relies on Fargate as a serverless compute engine that removes server management, pairs with Amazon ECS for container orchestration, and integrates with Amazon S3 for input and output, Amazon ECR for image storage, Amazon Kinesis for streaming, and Amazon Data Firehose for delivery. Terraform is positioned as an infrastructure as code tool with planning, graphing, and template reuse capabilities that enable API based ingestion pipelines.

Together these references establish a pattern where Terraform defines the ECS cluster with awsecscluster, capacity providers for Fargate and Fargate Spot, and supporting IAM roles, while monitoring via Container Insights, debugging via ECS Exec, and service discovery via Cloud Map are recommended as operational add-ons. The choice between Fargate and EC2 launch types is framed by GPU needs, cost optimization for steady state workloads, and specific instance type requirements, with the ability to mix capacity providers in a single cluster. The entire workflow remains experimental due to the alpha status of the module, and the Step Functions example carries cost implications beyond free tier, requiring explicit cleanup to avoid recurring charges. The material therefore defines both the mechanics of deploying an alpha Terraform Fargate module and the broader architectural context in which that module would be used for event driven, serverless container workloads.