Discover the new ECS Managed Instances as a sweet spot between Fargate’s ease and EC2’s flexibility. Dive into the pricing nuances, including a management fee that can surprise you. Explore practical setups like deploying GPU-enabled workers with OpenAI Whisper for audio transcription. Uncover the ideal workloads for ECS MI, like queue-driven jobs, and the limitations to watch out for, such as startup times. Join in on the debate over the trade-offs between control and simplicity in your cloud architecture.
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insights INSIGHT
Middle Path Between Fargate And EC2
ECS Managed Instances sits between Fargate and ECS on EC2 by offering control without full instance management.
You declare requirements and AWS selects and launches matching instances for you.
volunteer_activism ADVICE
Benchmark Costs Before Choosing MI
Compare total cost of ownership before switching because ECS MI adds a management fee on top of EC2 pricing.
Benchmark against your current Reserved Instances, Savings Plans, and Spot usage to decide.
insights INSIGHT
Management Fee Reduces Savings Plan Value
The ECS MI management fee is roughly ~12% of the on-demand price according to their analysis.
That fee applies even if you use compute savings plans or Reserved Instances benefits.
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Love AWS Fargate, but occasionally hit the “I need more control” wall (GPUs, storage, network bandwidth, instance sizing)? In this episode of AWS Bites, Eoin and Luciano put the brand-new Amazon ECS Managed Instances (ECS MI) under the microscope as the “middle path” between Fargate simplicity and ECS on EC2 flexibility. We unpack what ECS MI actually is and where it fits in the ECS spectrum, especially how it changes the way you think about clusters and capacity providers. From there we get practical: we talk through the pricing model (EC2 pricing with an additional ECS MI fee that can be a bit counterintuitive if you rely heavily on Reserved Instances or Savings Plans), and we share what it feels like to finally get GPU support in an experience that’s much closer to Fargate than to “full EC2 fleet management”. To make it real, we walk through what we built: a GPU-enabled worker that transcribes podcast audio using OpenAI Whisper, including the end-to-end setup in CDK (roles, capacity provider wiring, task definitions, and service configuration). Along the way we call out the rough edges we ran into, like configuration options that look like they might enable Spot-style behavior, and the operational realities you should expect, such as tasks taking roughly 3–4 minutes to start when ECS needs to provision fresh capacity. We close by mapping out the workloads where ECS MI shines (queue-driven GPU jobs, HPC-ish compute, tighter storage/network control) and the scenarios where it’s probably the wrong choice, like when you need custom AMIs, SSH access, or stricter isolation guarantees.
In this episode, we mentioned the following resources:
AWS Bites