Designing AI Infrastructure in a Multi-Cloud World

Multicloud is now new normal for Network engineer.

When it comes of AI workload , Companies are no way interested to bet on single cloud provider ,they prefer multi cloud option and for good reason .Reason is simple no one cloud is best on everything

💡 Why Go Multi-Cloud for AI?

Each cloud provider has its own strength

• One might be excellent in providing top tier AI/ML service

• Another might be good in serverless compute

• Third may be providing good pricing as compared to others

So its now the cloud architect task to pick the best provider considering all below points.

• Better uptime and distory recovery

• Dependability on one vendor

• Flexibility and more power while renewing the contract

• Most importantly meeting data residency & sovereignty laws

❓ Is It Common for AI Workloads?

While multicloud is now standard in many IT companies but still less common for AI training Why ?

• Training models is very resource-intensive

• Highly Optimized on a single cloud or even on premises

• Often involves massive datasets which is more expensive to move across clouds.

But once the model is trained...

• Inference (making predication) is less resource intensive

• Can be deployed closer to user

• Reducing latency and providing better user experience.

That’s why inference workloads (predications) are more likely to be spread across clouds.

💾 Managing Data in a Multi-Cloud World

AI workloads run on data—and lots of it.

Managing that across clouds? Needs solid strategy. Here’s a breakdown:

• Portability & Interoperability 

  Keep data in sync across clouds using available tools like AWS DataSync.

• Smart Storage Strategies

  Localize storage near compute to reduce data transfer latency

  Hybrid storage(on prem+cloud) offers flexibility and compliance benefits

• Data Consistency

  Use distributed database and leverage available synchronization tools to keep data up to date and accurate across cloud

• Security & Compliance

  Encrypt data in transit and at rest.

  Use governance tools for compliance and auditing

• Cost Optimization

  Use compression

  Avoid unnecessary cross-cloud movement

  Use fast storage for active data wheras cheap storage for archived data.

🔍 Final Thought

Multi-cloud AI isn’t just a trend—it’s a smart, strategic move if done right. But it requires planning, the right tools, and a mindset focused on portability, security, and efficiency.