This section of theGoogle Cloud deployment archetypes guide describes the multicloud deployment archetype, provides examples of usecases, and discusses design considerations.

In an architecture that uses the multicloud deployment archetype, some partsof the application run in Google Cloud while others are deployed in othercloud platforms.

Use cases

The following sections provide examples of use cases for which the multiclouddeployment archetype is an appropriate choice.

• Low-cost, unlimitedarchive storage.
• AI and ML applications for data generated by applications deployed in other cloudplatforms.
• Data warehousing and analytics processes usingBigQuery for data ingested from applications that run in other cloud platforms.

The following diagram shows a multicloud topology that enhances an applicationrunning on another cloud platform with advanced data-processing capabilities inGoogle Cloud.

More information

For more information about the rationale and use cases for the multiclouddeployment archetype, seeBuild hybrid and multicloud architectures using Google Cloud.

Design considerations

When you build an architecture that's based on the multicloud deploymentarchetype, consider the following design factors.

Cost of redundant resources

A multicloud architecture often costs more than an architecture where theapplication runs entirely in Google Cloud, due to the following factors:

• Data might need to be stored redundantly within each cloud rather than in asingle cloud. The storage and data transfer costs might be higher.
• If an application runs in multiple cloud platforms, some of the redundantresources might be underutilized, leading to higher overall cost of thedeployment.

Inter-cloud connectivity

For efficient network communication between your resources in multiple cloudplatforms, you need secure and reliable cross-cloud connectivity. For example,you can use Google CloudCross-Cloud Interconnect to establish high-bandwidth dedicated connectivity between Google Cloudand another cloud service provider. For more information, seePatterns for connecting other cloud service providers with Google Cloud.

Setup effort and operational complexity

Setting up and operating a multicloud topology requires significantly moreeffort than an architecture that uses only Google Cloud:

• Security features and tools aren't standard across cloud platforms. Yoursecurity administrators need to learn the skills and knowledge that arenecessary to manage security for resources distributed across all the cloudplatforms that you use.
• You need to efficiently provision and manage resources across multiplepublic cloud platforms. Tools like Terraform can help reduce the effort toprovision and manage resources. To manage containerized multicloudapplications, you canuseGKE attached clusters.

Example architectures

For examples of architectures that use the multicloud deployment archetype, seeBuild hybrid and multicloud architectures using Google Cloud.