A company plans to migrate 20 infrequently used but business-critical applications (a mix of Java and Node.js) to AWS. The company wants to minimize cost and standardize on a single deployment approach. Most apps run as part of month-end processing with few concurrent users, but they can run at other times. Typical memory use is under 1 GB, with some peaks up to 2.5 GB. One critical Java billing-report application accesses multiple data sources and can run for several hours. What is the MOST cost-effective deployment strategy?

Running applications as Amazon ECS tasks on EC2 instances with Auto Scaling is the most cost-effective option. This approach allows multiple applications to share EC2 resources, reducing overall infrastructure costs compared to dedicated resources per application. Auto Scaling based on memory utilization ensures resources are provisioned only when needed, optimizing cost for infrequently used applications. ECS task/service scaling provides granular control over individual application instances. The critical Java application, which can run for several hours and access multiple data sources, is well-suited for ECS tasks, as Lambda functions have execution duration limits (currently 15 minutes). Deploying each application as a separate AWS Lambda function is not suitable due to the 15-minute execution limit for Lambda, which the critical Java application would exceed. AWS Elastic Beanstalk, while simplifying deployment, creates dedicated environments that might be less cost-effective for infrequently used applications compared to shared ECS clusters. Creating a new EC2 instance cluster and purchasing 3-year Reserved Instances for the GroupMaxSize would lead to over-provisioning and higher costs, especially for infrequently used applications, as RI payments are made regardless of actual usage.