Every organization deploying AI eventually faces the same question: should we build our own infrastructure, buy a managed service, or rent compute on demand? The answer has long-term strategic and financial implications that go well beyond the immediate budget conversation.
Renting is the default starting point for most teams. Cloud providers offer AI as a Service (AIaaS) and Machine Learning as a Service (MLaaS) that abstract away nearly all infrastructure decisions. You call an API, you pay per token or per inference, and someone else handles the hardware, the scaling, and the maintenance. AI Serverless takes this further: you don't even think about servers. This model is fast to start, requires minimal infrastructure expertise, and scales automatically. It is also the most expensive per unit of compute at high volumes, and it puts your workloads on infrastructure you don't control.
Buying sits in the middle. Infrastructure as a Service (IaaS) gives you dedicated compute that you configure and manage yourself, typically on cloud hardware. Platform as a Service (PaaS) adds a managed layer on top: the platform handles the runtime and scaling, but you own the application and its configuration. These models offer more control and better unit economics at scale than fully managed APIs, at the cost of more operational responsibility.
Building means owning your own hardware. This is the most expensive option upfront and requires the most expertise to operate. It makes sense when workloads are large and predictable enough that the economics of ownership beat the economics of renting, when data sovereignty requirements prohibit sending data to external providers, or when latency requirements demand on-premises inference.
The decision framework comes down to four questions. How predictable is your workload? Unpredictable or spiky workloads favor renting; steady high-volume workloads favor ownership. What are your data requirements? Sensitive data may require on-premises or dedicated infrastructure. What does it cost at scale? The crossover point where ownership beats renting is lower than most teams expect. And what is your organizational capacity to manage infrastructure? The operational cost of running your own hardware is real and ongoing.
The articles in this section cover each of these models and the tools for managing costs across all of them.
