Multi-cloud deployment
A multi-cloud deployment is an architecture or operating strategy that uses services or infrastructure from two or more cloud providers.
Platform Engineering on Kubernetes
A multi-cloud strategy is an approach for using or operating across services and infrastructure from multiple cloud providers.
- How it is used
- Coordinates application infrastructure across cloud providers while abstracting provider-specific provisioning through Kubernetes APIs.
- How deep it goes
- Defines the operational problem and gives a declarative Crossplane path, stopping short of detailed provider configuration or composition syntax.
- Examples
- Uses databases, message brokers, identity services, and Crossplane-provisioned composite resources as infrastructure examples.
- What you can do after
- Apply it via Crossplane to provision application infrastructure through Kubernetes APIs and request resources using self-service interfaces.
Read how Platform Engineering on Kubernetes explains multi-cloud deployment →
Google Anthos in Action
A multicloud platform is a platform designed to operate across multiple independent cloud environments.
- How it is used
- Supports resilient, regulated, geographically distributed, edge, and provider-independent applications through a common deployment model.
- How deep it goes
- Defines three deployment patterns and three connectivity choices, then explains operational trade-offs without detailing complete implementation procedures.
- Examples
- Uses GCP, AWS, Azure, on-premises data centers, regulated banking, regional outages, and Dedicated Interconnect diagrams.
- What you can do after
- Operate a Kubernetes-based platform across public and private environments using standardized deployment and selected connectivity patterns.
Read how Google Anthos in Action explains multi-cloud deployment →
Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform
A multicloud configuration defines and coordinates infrastructure resources across multiple public cloud providers.
- How it is used
- Optimizes workload placement and infrastructure cost across public cloud providers.
- How deep it goes
- Explains provider modules, workload migration, networking, and transfer costs, stopping short of provider-specific implementation code.
- Examples
- Uses Google Cloud Dataflow, Amazon EMR, Azure HDInsight, batch pipelines, and single-zone integration testing.
- What you can do after
- Operate/configure provider-specific IaC modules while evaluating workload placement and cross-cloud networking costs.
Related concepts
- Anthosincludes · Anthos is Google Cloud's platform for managing and deploying applications consistently across on-premises and multiple cloud environments.
- cloud provideruses · A cloud provider is an organization that delivers computing resources, platforms, or managed services over a network, typically on demand.
- MongoDB Atlasuses · MongoDB Atlas is MongoDB's managed cloud database service for provisioning, operating, and securing database deployments.
- infrastructure as codeconfigured by · Infrastructure as code is the practice of defining, provisioning, and managing computing infrastructure through machine-readable code or configuration rather than manual procedures.
- Kubernetes clusteruses · A Kubernetes cluster is a set of control-plane and worker machines managed by Kubernetes to run and coordinate workloads and services.
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