The three big clouds solve the same problem —having a reusable template to launch identical machines— with their own approaches and naming. Knowing the equivalences is the first step to designing a frictionless multicloud strategy.
On AWS it is called an AMI (Amazon Machine Image); on Azure, a Managed Image and, above all, the Azure Compute Gallery (formerly Shared Image Gallery); on Google Cloud, a Custom Image. They all encapsulate a preconfigured boot disk, but they differ in how they are versioned, shared and distributed.
Equivalences at a glance
| Concept | AWS | Azure | Google Cloud |
|---|---|---|---|
| Machine image | AMI | Managed Image | Custom Image |
| Catalogue or gallery | None native: tags and SSM | Azure Compute Gallery | Image Family |
| Managed versioning | Manual, by name and tags | Native in the Gallery | Image Family: latest per family |
| Multi-region distribution | AMI copy | Replicas in the Gallery | Global images by default |
| Underlying store | EBS snapshots | Managed Disks | Persistent Disk |
| Encryption | KMS | Platform or customer keys | Google-managed or CMEK |
Functional equivalences of machine images across the three big clouds.
AWS AMI: the de facto standard
The AMI is probably the best-known image format and the one with the largest ecosystem. Its strength is maturity: a huge catalogue, integration with EC2 Image Builder, Marketplace and an immense community. Its historical weak spot is the absence of a native image gallery with managed versioning: versioning and multi-region distribution are solved with naming conventions, tags, SSM Parameter Store and explicit copies across regions.
Azure Compute Gallery: versioning and replicas out of the box
Azure has bet heavily on image governance. The Compute Gallery natively offers image definitions, versions and automatic replicas to several regions, plus granular access control. For large organisations that need to distribute images in an orderly way across teams and regions, it is a very comfortable model. The trade-off is a slightly steeper conceptual learning curve.
GCP Custom Image: global simplicity
Google Cloud stands out for its simplicity. Its images are global by default —you do not have to copy them region by region— and the Image Family concept solves versioning elegantly: you point at the family and always get the latest non-deprecated image. It is a minimalist model that reduces friction, especially attractive for teams that value operational simplicity.
The multicloud strategy: one template, three images
If you publish or deploy across several clouds, maintaining three separate build processes is painful. The industry’s answer is Packer: a single template with shared provisioners and one source block per cloud, able to generate the AMI, the Managed Image and the Custom Image in parallel from the same definition.
- Reuse the same installation and hardening scripts across the three clouds.
- Reduce drift between environments: the same configuration, three destinations.
- Version coherently with a common naming and metadata scheme.
- Automate publication to each gallery: Gallery, Image Family, tags and SSM.
Which one should you choose?
There is no absolute winner; it depends on your context. If you want ecosystem and maturity, AWS. If you need enterprise image governance with native versioning and replicas, Azure’s Compute Gallery shines. If you value simplicity and global reach with no copies, GCP. And if you live across several clouds, the answer is not a platform but a practice: describe your images as code and build them portably.
Frequently asked questions
Can I move an AWS AMI to Azure or GCP directly?
Not directly: the formats and underlying stores differ. The usual approach is to rebuild the image on each cloud from a common template, for instance with Packer, or to import the disk using each provider’s import process.
Which cloud has the best image versioning?
Azure Compute Gallery offers the most complete managed versioning out of the box; GCP solves it elegantly with Image Families; AWS requires more of your own conventions, although it is very flexible.
Is a multicloud image strategy worth it?
If you operate across several clouds for data sovereignty, resilience or to avoid vendor lock-in, yes. The key is to use images as code so you do not multiply the maintenance effort.
At imaxe.cloud we think about portability from the design stage so your deployments do not depend on a single cloud.
