AI Infrastructure

What is Multi-Cloud AI?

Multi-Cloud AI is the strategy of distributing AI workloads across two or more cloud providers such as AWS, Google Cloud, and Azure, enabling businesses to leverage the best AI services from each provider while avoiding vendor lock-in, improving resilience, and meeting diverse regulatory requirements across different markets.

What Is Multi-Cloud AI?

Multi-Cloud AI is the practice of running AI workloads across multiple cloud providers rather than relying exclusively on a single one. A business might use Google Cloud for model training because of its TPU offerings, AWS for model serving because of its broad ASEAN infrastructure, and Azure for AI services that integrate with their existing Microsoft enterprise tools.

This approach recognises that no single cloud provider excels at everything. Each has strengths in different areas of AI infrastructure, pricing models, geographic coverage, and specialised services. A Multi-Cloud strategy allows organisations to select the best tool for each job rather than accepting compromises within a single provider's ecosystem.

Why Businesses Adopt Multi-Cloud AI

Several compelling business reasons drive the adoption of Multi-Cloud AI:

Avoiding Vendor Lock-in

Relying on a single cloud provider creates significant dependency. If that provider raises prices, experiences an outage, changes their service offerings, or restricts access to AI chips during global shortages, your entire AI capability is affected. Multi-Cloud provides negotiating leverage and strategic flexibility.

Leveraging Best-of-Breed Services

Each cloud provider has distinct AI strengths:

Google Cloud: Leading AI/ML platform with Vertex AI, exclusive access to TPUs, strong in natural language processing and large language model hosting. Has data centres in Singapore and Jakarta.
AWS: Broadest overall infrastructure, strong in managed AI services like SageMaker, proprietary inference chips (Inferentia and Trainium) for cost optimisation. Extensive ASEAN presence including Singapore, Jakarta, and Bangkok.
Azure: Deep integration with Microsoft enterprise tools, strong in OpenAI model access through the Azure OpenAI Service, and growing ASEAN data centre presence including Singapore and Kuala Lumpur.

A Multi-Cloud approach lets you use Google's TPUs for training while serving predictions through AWS infrastructure that is closest to your customers.

Meeting Regulatory Requirements

Businesses operating across multiple ASEAN markets face varying data residency and sovereignty regulations. Indonesia, Vietnam, and Thailand each have specific requirements about where certain categories of data must be stored and processed. A Multi-Cloud strategy allows you to select the provider with the best data centre coverage for each market's regulatory needs.

Improving Resilience

Distributing AI workloads across providers means that an outage at one cloud provider does not completely disable your AI capabilities. Critical AI services can fail over to alternative providers, maintaining business continuity.

Challenges of Multi-Cloud AI

Multi-Cloud is not without its difficulties:

Complexity: Managing infrastructure, security, and networking across multiple providers requires more sophisticated tooling and expertise than a single-cloud approach.
Data movement costs: Transferring data between cloud providers incurs egress fees that can be substantial for data-intensive AI workloads.
Skill requirements: Your team needs expertise across multiple cloud platforms, which is more demanding than mastering a single provider.
Inconsistent tooling: Each provider has different interfaces, APIs, and management tools. Achieving a consistent operational experience requires additional abstraction layers.

Practical Multi-Cloud AI Strategies

For businesses in Southeast Asia considering Multi-Cloud AI:

Primary-Secondary Approach

Choose one cloud provider as your primary platform for most AI workloads and use a second provider selectively for specific capabilities or markets. This balances the benefits of Multi-Cloud with manageable complexity.

Workload-Based Distribution

Assign different types of AI workloads to different providers based on their strengths. For example, model training on the provider with the best GPU availability and pricing, inference serving on the provider with the lowest latency in your key markets, and data analytics on the provider that integrates best with your existing tools.

Abstraction Layer Strategy

Use open-source tools and frameworks like Kubernetes, MLflow, and Terraform that work consistently across cloud providers. This creates a portable AI platform that can run on any cloud with minimal modification, reducing switching costs and lock-in.

Getting Started with Multi-Cloud AI

Assess your current cloud usage and identify pain points. Are you experiencing vendor lock-in? Missing capabilities? Compliance gaps in specific markets?
Identify specific workloads that would benefit from a different provider rather than attempting to move everything at once.
Invest in cloud-agnostic tooling like Kubernetes and Terraform that provide consistent infrastructure management across providers.
Build cross-cloud networking securely. Ensure data can move between providers with encryption and proper access controls.
Train your team on the second cloud provider before migrating workloads. Operational mistakes during migration are common and avoidable with proper preparation.
Start small with a non-critical workload to build experience before migrating production AI systems.

Measuring Multi-Cloud Success

Organisations should track several metrics to evaluate their Multi-Cloud AI strategy:

Availability improvement: Measure whether uptime has increased compared to a single-cloud deployment. The target should be a measurable reduction in AI service outages.
Cost per prediction: Compare costs across providers for equivalent workloads to ensure you are genuinely benefiting from best-of-breed pricing.
Deployment velocity: Track whether Multi-Cloud adds friction to your deployment process. If deployments take significantly longer than a single-cloud setup, the abstraction layer may need improvement.
Compliance coverage: Verify that your Multi-Cloud architecture successfully meets data residency requirements across all ASEAN markets you operate in.

Multi-Cloud AI is increasingly becoming the standard approach for mature AI organisations. While it adds complexity, the strategic benefits of flexibility, resilience, and best-of-breed capabilities make it a sound long-term infrastructure strategy for businesses operating across the diverse ASEAN market.

Why It Matters for Business

Multi-Cloud AI is fundamentally a risk management and strategic flexibility decision. For business leaders in Southeast Asia, the question is not whether to consider Multi-Cloud but when the benefits of diversification outweigh the additional complexity.

The vendor lock-in risk is particularly acute for AI workloads. Unlike commodity services like email or file storage, AI systems often develop deep dependencies on a provider's specific tools, data formats, and service APIs. Migrating a production AI system from one cloud to another can take months and cost significant engineering resources. Building Multi-Cloud capability gradually, before you urgently need it, is far more cost-effective than attempting an emergency migration.

For businesses operating across ASEAN, the regulatory landscape adds urgency to the Multi-Cloud discussion. With data sovereignty requirements varying across Indonesia, Vietnam, Thailand, and other markets, having the flexibility to place workloads on whichever provider offers the appropriate data centre coverage for each jurisdiction is a practical necessity rather than a theoretical advantage. Companies that lock themselves into a single provider may find themselves unable to serve certain markets compliantly.

Key Considerations

Start with a clear rationale for Multi-Cloud adoption. Common drivers include avoiding vendor lock-in, accessing specific AI capabilities, meeting regulatory requirements, and improving resilience.
Adopt a primary-secondary model rather than trying to use all providers equally from the start. This limits complexity while still providing diversification benefits.
Invest heavily in cloud-agnostic tooling and infrastructure-as-code from the beginning. Kubernetes, Terraform, and MLflow significantly reduce the operational burden of multi-cloud management.
Budget for data transfer costs between clouds. Egress fees can be substantial for data-heavy AI workloads, so design your data architecture to minimise cross-cloud data movement.
Ensure your team has adequate skills across all providers in your Multi-Cloud strategy. Under-trained teams managing multiple clouds create more risk than they mitigate.
Standardise security policies and access controls across providers to prevent gaps that create vulnerabilities.
Evaluate the Multi-Cloud strategy annually. The competitive landscape of cloud providers changes rapidly, and your strategy should evolve accordingly.

Frequently Asked Questions

Is Multi-Cloud AI necessary for every business?

No. For many SMBs, especially those early in their AI journey, a single cloud provider is the right choice. It reduces complexity, simplifies operations, and allows your team to develop deep expertise in one platform. Multi-Cloud becomes more relevant as your AI operations scale, as you expand across ASEAN markets with different regulatory requirements, or when you identify specific capabilities that are significantly better on a different provider. The decision should be driven by concrete business needs, not a general preference for diversification.

How much more expensive is Multi-Cloud compared to single cloud?

Multi-Cloud typically adds 15-30% to infrastructure management costs due to additional tooling, cross-cloud networking, data transfer fees, and the need for broader team skills. However, this can be partially or fully offset by using each provider for what it does most cost-effectively. For example, using a cheaper provider for training while using a different provider with better regional coverage for inference can reduce total compute costs. The net financial impact depends on how strategically you design your Multi-Cloud architecture.

Need help implementing Multi-Cloud AI?

Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how multi-cloud ai fits into your AI roadmap.

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