> For the complete MongoDB documentation index, see www.mongodb.com/docs/llms.txt

# Multi-Cloud Deployment Paradigm

Multi-cloud Atlas deployments are a special case of multi-region deployment in which you set up cluster nodes across multiple geographic regions *and* multiple cloud providers. Multi-cloud deployments enhance protection in the case of concurrent regional outage and cloud provider outage. By automatically rerouting traffic to a different cloud provider's node in another region, you get continuous availability and a smooth user experience. Multi-cloud deployments can also protect against vendor lock-in and enhance performance.

Atlas supports multi-cloud deployment across any combination of AWS (Amazon Web Services), Azure (Microsoft Azure), and GCP (Google Cloud Platform).

To learn how to configure multi-cloud deployments and learn about the different types of nodes you can add, see [Configure High Availability and Workload Isolation](https://www.mongodb.com/docs/atlas/cluster-config/multi-cloud-distribution/) in the Atlas documentation.

## Use Cases for Multi-Cloud Deployments

The following image shows a multi-region, multi-cloud Atlas deployment for regions that support availability zones. Note that this differs from a multi-region deployment only in that multiple cloud providers are used.

![Five-node deployment across three regions and two cloud providers.](/_includes/images/multi-cloud.png)

A multi-region, multi-cloud deployment may be best for you if you want to:

- Provide an application development team with the option to use any one of the cloud providers for deploying their application stack (including the Atlas cluster).

- Migrate an application to a new cloud provider. You can add a node to a new cloud provider region, and Atlas will replicate the data to that new node.

- Ensure faster data recovery if there is a cloud provider failure. If the backup is replicated to a separate cloud, automation will bring up a full app stack on a separate cloud and remount the backup to start serving traffic.

- Run analytics on a separate cloud. For example, your the data science team wants to use BigQuery for business intelligence but your app stack is in AWS (Amazon Web Services).

- Deploy your application stack on both AWS (Amazon Web Services) and Azure (Microsoft Azure) and serve read and write traffic from both clouds, whether sharded or not.

## Considerations and Recommendations

Multi-cloud deployments have the same considerations and [recommendations](https://www.mongodb.com/docs/atlas/architecture/current/deployment-paradigms/multi-region.md#std-label-arch-center-multi-region-rec-summary) as other multi-region deployments.

To automate multi-cloud deployment using Terraform, see the [MongoDB Atlas Cluster Module.](https://registry.terraform.io/modules/terraform-mongodbatlas-modules/cluster/mongodbatlas/latest)

In addition, the following are specific considerations for multi-cloud deployments:

- Cost. Since you are getting charged higher rates for ingress/egress traffic on both cloud providers during replication.

- Complexity of the deployment. There are many integrations you may want when deploying an Atlas database, and many of them are pinned to a single cloud provider. For example,

  - Encryption key management for KMS integration / encryption at rest.

  - Secrets management for database access.

  - Integration with a SSO federated identity provider.

  - Private networking, because each cloud provider only peers (uses private endpoints) with other regions within that provider.

  - Gather observability data like logs, metrics, and alerts in a centralized place.

Therefore, multi-cloud deployments often require 3rd party integrations to meet these needs.