ARM-based processors, such as the AWS Graviton family, have become a first-class option for production workloads. Their pitch is simple and powerful: better price-performance than traditional x86 alternatives for many workloads, with lower power consumption.
In a cloud landscape that keeps getting more expensive —one of the big trends of 2026— migrating to ARM is one of the most effective savings levers inside a FinOps strategy.
How much can you save
The numbers vary by workload, but the industry consistently reports meaningful savings when moving to Graviton, in the order of 20 % to 40 % on compute cost for suitable workloads, thanks to a better price per vCPU and greater efficiency. It is not magic: you have to validate it with your real workload, but the potential is large and it is often money left on the table.
What migrates well and what needs care
| Migrates well | Needs validation |
|---|---|
| Interpreted languages: Python, Node, Java, Go | Binaries compiled for x86 only |
| Containers with multi-architecture images | Native dependencies with no ARM build |
| Web, APIs and microservices | Proprietary software with no ARM version |
| Common databases and caches | Specific drivers or extensions |
Most modern workloads migrate without drama; watch out for native dependencies.
The role of multi-architecture images
The key to a clean migration is building your images for both architectures, x86_64 and arm64. In the container world, multi-arch images let the same tag work on either one. In the AMI world, it pays to have your pipeline —Packer or EC2 Image Builder— ready to produce the arm64 image alongside the x86 one, reusing the same provisioners.
A five-step migration plan
- Inventory your workloads and spot dependencies that may have no ARM version.
- Build arm64 images in your pipeline, in parallel with the x86 ones.
- Test in staging: performance, compatibility and functional results.
- Migrate in phases with canary or blue/green, measuring real cost and performance.
- Optimise: match the Graviton instance type to the workload profile.
ARM on Azure and GCP too
The trend is not AWS-only. Azure offers ARM-based machines —Cobalt and partner ones— and Google Cloud has ARM instances such as Axion and Tau T2A. If you design your images as code and for multiple architectures, you earn the freedom to chase the best price-performance on any cloud.
Frequently asked questions
Exactly how much will I save with Graviton?
It depends on your workload, but savings of 20 % to 40 % on compute cost are common for suitable workloads. The only way to know for sure is to run your real workload on ARM instances and compare.
Do I have to rewrite my application for ARM?
Rarely. Interpreted languages and most modern software run on ARM unchanged. The work shows up with binaries compiled for x86 only or native dependencies with no ARM build.
Can I have images that work on x86 and ARM at the same time?
Yes: with multi-architecture container images and AMI pipelines that produce both variants. That way you migrate gradually and never get stuck.
At imaxe.cloud we design our images to make the most of each architecture and help you optimise cost and performance.
