Multi-Cloud AI Strategy: Architecture Guide

Q: Should you use multi-cloud for AI workloads?

Multi-cloud helps when you need best-of-breed AI services (e.g., GCP TPUs for training + AWS for serving), regulatory compliance across regions, or vendor negotiation leverage. It hurts when it adds data transfer costs and operational complexity.

Q: What is the cost of multi-cloud AI?

Major costs include: data egress fees ($0.08-0.12/GB between clouds), operational overhead (3-5x more DevOps work), training inconsistency, and slower feature adoption. Only justified when benefits clearly outweigh these costs.

Q: How do you architect AI for multi-cloud?

Use containers/Kubernetes for portability, abstract AI APIs behind a unified gateway, store models in cloud-agnostic registries, implement data mesh for cross-cloud data access, and use infrastructure-as-code for consistent deployments.

Satyam Kumar

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Multi-Cloud & Infrastructure

Multi-Cloud AI Strategy: When It Helps, When It Hurts, and How to Architect It Right

February 22, 202629 min read

multi-cloud AI AI cloud strategy multi-cloud architecture AWS Azure GCP AI cloud AI deployment hybrid AI

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Satyam

人工智能和云架构师。帮助团队构建可扩展到数百万的系统。

Multi-Cloud AI Strategy: When It Helps, When It Hurts, and How to Architect It Right

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