Choose Google Cloud deployment archetypes

Last reviewed 2023-11-09 UTC

This document in the Google Cloud Architecture Framework describes six deployment archetypes¹—zonal, regional, multi-regional, global, hybrid, and multicloud—that you can use to build architectures for your cloud workloads based your requirements for availability, cost, performance, and operational efficiency.

What is a deployment archetype?

A deployment archetype is an abstract, provider-independent model that you use as the foundation to build application-specific deployment architectures that meet your business and technical requirements. Each deployment archetype specifies a combination of failure domains where an application can run. These failure domains can be one or more Google Cloud zones or regions, and they can extend to include your on-premises data centers or failure domains in other cloud providers.

The following diagram shows six applications deployed in Google Cloud. Each application uses a deployment archetype that meets its specific requirements.

As the preceding diagram shows, in an architecture that uses the hybrid or multicloud deployment archetype, the cloud topology is based on one of the basic archetypes: zonal, regional, multi-regional, or global. In this sense, the hybrid and multicloud deployment archetypes can be considered as composite deployment archetypes that include one of the basic archetypes.

Note: Deployment archetypes are different from location scopes. The location scope of a Google Cloud resource defines its availability boundary. For example, the location scope of a Compute Engine VM is zonal. This means that if the Google Cloud zone in which a VM is provisioned has an outage, the availability of the VM is affected. However, by distributing VMs across multiple zones, you can build a highly available architecture that's based on the regional deployment archetype.

Choosing a deployment archetype helps to simplify subsequent decisions regarding the Google Cloud products and features that you should use. For example, for a highly available containerized application, if you choose the regional deployment archetype, then regional Google Kubernetes Engine (GKE) clusters are more appropriate than zonal GKE clusters.

When you choose a deployment archetype for an application, you need to consider tradeoffs between factors like availability, cost, and operational complexity. For example, if an application serves users in multiple countries and needs high availability, you might choose the multi-regional deployment archetype. But for an internal application that's used by employees in a single geographical region, you might prioritize cost over availability and, therefore, choose the regional deployment archetype.

Overview of the deployment archetypes

The following tabs provide definitions for the deployment archetypes and a summary of the use cases and design considerations for each.

Zonal

Your application runs within a single Google Cloud zone, as shown in the following diagram:

Use cases	Development and test environments. Applications that don't need high availability. Low-latency networking between application components. Migrating commodity workloads. Applications that use license-restricted software.
Design considerations	Downtime during zone outages. For business continuity, you can provision a passive replica of the application in another zone in the same region. If a zone outage occurs, you can restore the application to production by using the passive replica.
More information	See the following sections: Zonal deployment archetype Comparative analysis of all the deployment archetypes

Regional

Your application runs independently in two or more zones within a single Google Cloud region, as shown in the following diagram:

Use cases	Highly available applications that serves users within a geographic area. Compliance with data residency and sovereignty requirements.
Design considerations	Downtime during region outages. For business continuity, you can back up the application and data to another region. If a region outage occurs, you can use the backups in the other region to restore the application to production. Cost and effort to provision and manage redundant resources.
More information	See the following sections: Regional deployment archetype Comparative analysis of all the deployment archetypes

Multi-regional

Your application runs independently in multiple zones across two or more Google Cloud regions. You can use DNS routing policies to route incoming traffic to the regional load balancers. The regional load balancers then distribute the traffic to the zonal replicas of the application, as shown in the following diagram:

Use cases	Highly available application with geographically dispersed users. Applications that require low end-user latency experience. Compliance with data residency and sovereignty requirements by using a geofenced DNS routing policy.
Design considerations	Cost for cross-region data transfer and data replication. Operational complexity.
More information	See the following sections: Multiregional deployment archetype Comparative analysis of all the deployment archetypes

Global

Your application runs across Google Cloud regions worldwide, either as a globally distributed (location-unaware) stack or as regionally isolated stacks. A global anycast load balancer distributes traffic to the region that's nearest to the user. Other components of the application stack can also be global, such as the database, cache, and object store.

The following diagram shows the globally distributed variant of the global deployment archetype. A global anycast load balancer forwards requests to an application stack that's distributed across multiple regions and that uses a globally replicated database.

The following diagram shows a variant of the global deployment archetype with regionally isolated application stacks. A global anycast load balancer forwards requests to an application stack in one of the regions. All the application stacks use a single, globally replicated database.

Use cases	Highly available applications that serve globally dispersed users. Opportunity to optimize cost and simplify operations by using global resources instead of multiple instances of regional resources.
Design considerations	Costs for cross-region data transfer and data replication.
More information	See the following sections: Global deployment archetype Comparative analysis of all the deployment archetypes

Hybrid

Certain parts of your application are deployed in Google Cloud, while other parts run on-premises, as shown in the following diagram. The topology in Google Cloud can use the zonal, regional, multi-regional, or global deployment archetype.

Use cases	Disaster recovery (DR) site for on-premises workloads. On-premises development for cloud applications. Progressive migration to the cloud for legacy applications. Enhancing on-premises applications with cloud capabilities.
Design considerations	Setup effort and operational complexity. Cost of redundant resources.
More information	See the following sections: Hybrid deployment archetype Comparative analysis of all the deployment archetypes

Multicloud

Some parts of your application are deployed in Google Cloud, and other parts are deployed in other cloud platforms, as shown in the following diagram. The topology in each cloud platform can use the zonal, regional, multi-regional, or global deployment archetype.

Use cases	Google Cloud as the primary site and another cloud as a DR site. Enhancing applications with advanced Google Cloud capabilities.
Design considerations	Setup effort and operational complexity. Cost of redundant resources and cross-cloud network traffic.
More information	See the following sections: Multicloud deployment archetype Comparative analysis of all the deployment archetypes

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1. Anna Berenberg and Brad Calder, Deployment Archetypes for Cloud Applications, ACM Computing Surveys, Volume 55, Issue 3, Article No.: 61, pp 1-48 ↩