Multi-cloud strategies involve distributing workloads, data, and applications across multiple cloud service providers (AWS, Azure, GCP, and others) rather than relying on a single vendor. Organizations adopt multi-cloud to avoid vendor lock-in, leverage best-of-breed services, enhance resilience, optimize costs, and place AI/ML workloads on the most capable or cost-effective infrastructure—but they must also manage the complexity of orchestration, governance, and integration across heterogeneous platforms. The key is balancing flexibility with operational overhead: successful multi-cloud deployments require strong automation, consistent tooling, and clear policies that work across provider boundaries without forcing teams into proprietary silos.
What This Cheat Sheet Covers
This topic spans 16 focused tables and 115 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Deployment Models
| Model | Example | Description |
|---|---|---|
AWS + Azure + GCP | • Uses services from two or more public cloud providers simultaneously • workloads distributed based on best fit for each use case. | |
On-prem + AWSPrivate cloud + Azure | • Combines on-premises infrastructure with one or more public clouds • enables data and application portability between private and public environments. | |
On-prem + AWS + Azure + GCP | • Merges hybrid and multi-cloud approaches • integrates on-premises systems with multiple public cloud providers for maximum flexibility and resilience. | |
Kubernetes + TerraformDocker + Crossplane | • Designs applications and infrastructure to run on any cloud provider without modification • uses abstraction layers and portable tooling to avoid vendor-specific services. |