[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2024-11-20。"],[[["\u003cp\u003eThe multicloud deployment archetype involves running parts of an application in Google Cloud and other parts in different cloud platforms.\u003c/p\u003e\n"],["\u003cp\u003eA key use case is leveraging another cloud for disaster recovery while using Google Cloud as the primary site.\u003c/p\u003e\n"],["\u003cp\u003eMulticloud setups allow the use of Google Cloud's advanced capabilities like AI/ML, BigQuery, and archive storage for applications residing on other platforms.\u003c/p\u003e\n"],["\u003cp\u003eMulticloud architectures can be more expensive due to potential redundant resource storage and underutilized resources, as well as the complexities of managing security and resources across platforms.\u003c/p\u003e\n"],["\u003cp\u003eSecure and reliable inter-cloud connectivity, such as through Google Cloud Cross-Cloud Interconnect, is crucial for efficient communication in multicloud deployments.\u003c/p\u003e\n"]]],[],null,["# Google Cloud multicloud deployment archetype\n\nThis section of the\n[Google Cloud deployment archetypes](/architecture/deployment-archetypes)\nguide describes the multicloud deployment archetype, provides examples of use\ncases, and discusses design considerations.\n\nIn an architecture that uses the multicloud deployment archetype, some parts\nof the application run in Google Cloud while others are deployed in other\ncloud platforms.\n\nUse cases\n---------\n\nThe following sections provide examples of use cases for which the multicloud\ndeployment archetype is an appropriate choice.\n| **Note:** For each of these use cases, the architecture in each cloud can use the zonal, regional, multi-regional, or global deployment archetype.\n\n### Google Cloud as the primary site and another cloud as a DR site\n\nTo manage disaster recovery (DR) for mission-critical applications in\nGoogle Cloud, you can back up the data and maintain a passive replica in\nanother cloud platform, as shown in the following diagram. If the application in\nGoogle Cloud is down, you can use the external replica to restore the\napplication to production.\n\n### Enhancing applications with Google Cloud capabilities\n\nGoogle Cloud offers advanced capabilities in areas like storage,\nartificial intelligence (AI) and machine learning (ML), big data, and analytics.\nThe multicloud deployment archetype lets you take advantage of these advanced\ncapabilities in Google Cloud for applications that you want to run on\nother cloud platforms. The following are examples of these capabilities:\n\n- Low-cost, unlimited [archive storage](/storage).\n- [AI and ML](/products/ai) applications for data generated by applications deployed in other cloud platforms.\n- Data warehousing and analytics processes using [BigQuery](/bigquery/docs/introduction) for data ingested from applications that run in other cloud platforms.\n\nThe following diagram shows a multicloud topology that enhances an application\nrunning on another cloud platform with advanced data-processing capabilities in\nGoogle Cloud.\n\n### More information\n\nFor more information about the rationale and use cases for the multicloud\ndeployment archetype, see\n[Build hybrid and multicloud architectures using Google Cloud](/architecture/hybrid-multicloud-patterns).\n\nDesign considerations\n---------------------\n\nWhen you build an architecture that's based on the multicloud deployment\narchetype, consider the following design factors.\n\n### Cost of redundant resources\n\nA multicloud architecture often costs more than an architecture where the\napplication runs entirely in Google Cloud, due to the following factors:\n\n- Data might need to be stored redundantly within each cloud rather than in a single cloud. The storage and data transfer costs might be higher.\n- If an application runs in multiple cloud platforms, some of the redundant resources might be underutilized, leading to higher overall cost of the deployment.\n\n### Inter-cloud connectivity\n\nFor efficient network communication between your resources in multiple cloud\nplatforms, you need secure and reliable cross-cloud connectivity. For example,\nyou can use Google Cloud\n[Cross-Cloud Interconnect](/network-connectivity/docs/interconnect/concepts/cci-overview)\nto establish high-bandwidth dedicated connectivity between Google Cloud\nand another cloud service provider. For more information, see\n[Patterns for connecting other cloud service providers with Google Cloud](/architecture/patterns-for-connecting-other-csps-with-gcp).\n\n### Setup effort and operational complexity\n\nSetting up and operating a multicloud topology requires significantly more\neffort than an architecture that uses only Google Cloud:\n\n- Security features and tools aren't standard across cloud platforms. Your security administrators need to learn the skills and knowledge that are necessary to manage security for resources distributed across all the cloud platforms that you use.\n- You need to efficiently provision and manage resources across multiple public cloud platforms. Tools like Terraform can help reduce the effort to provision and manage resources. To manage containerized multicloud applications, you can use [GKE Enterprise](/anthos), which is a cross-cloud orchestration platform.\n\nExample architectures\n---------------------\n\nFor examples of architectures that use the multicloud deployment archetype, see\n[Build hybrid and multicloud architectures using Google Cloud](/architecture/hybrid-multicloud-patterns)."]]
