As enterprise AI moves from experimentation into production, the economics of inference are becoming harder to ignore. Organizations need more flexibility in model choice, better control over performance and cost, and an operating model that can scale with demand.

This white paper examines why Inference as a Service (IaaS) is positioned to become the primary way enterprises consume AI inference. It looks at the tradeoffs between frontier model APIs, dedicated inference services, and private inference, and explains why open-weight models are creating a major opportunity for a new generation of inference providers.

What you'll learn

• How the three primary models for consuming inference compare on cost, flexibility, performance, security, and operational complexity

• Why open-weight models are gaining share as enterprise AI workloads mature and move into production

• Why the economics of IaaS increasingly resemble the shift that made cloud computing the dominant delivery model

• The differences between serverless inference, dedicated inference endpoints, and managed private inference

• How IaaS providers can improve token economics, SLA performance, scalability, and infrastructure utilization

For infrastructure providers, cloud platforms, neoclouds, and others considering an inference service offering, the paper provides a practical view of where the market is heading and what will determine the economics of competing at scale.