Cloud AI
AI capabilities delivered as a service — powerful and accessible, with vendor dependency and data governance strings attached.
Cloud AI refers to AI capabilities delivered by major cloud platforms — AWS, Google Cloud, Azure, and others — as managed services. Organizations access them via API, paying based on usage rather than building or hosting models themselves. This is the dominant delivery model for enterprise AI today: it lowers the infrastructure barrier, provides access to state-of-the-art models, and scales with demand. The trade-offs are real: vendor dependency, consumption-based cost that can grow faster than anticipated, data handling obligations that require legal review, and reduced control when the provider updates model behavior.
Build-versus-buy decisions look straightforward until an organization discovers what the "buy" actually involves. Every cloud AI service that processes customer or employee data needs a reviewed data processing agreement. Cost models built on small test volumes routinely underestimate production spend. Vendor model updates can change the behavior of AI features without any code change on the organization's side. Leaders who treat cloud AI as a procurement decision rather than a governance one tend to find these issues after launch, not before.
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Cloud AI
AI capabilities delivered as a service — powerful and accessible, with vendor dependency and data governance strings attached.
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