What Is a Token Delivery Network? The Next Operating Model for AI Inference

June 10, 2026

The Evolution of AI Infrastructure

The first wave of AI infrastructure was about securing GPU capacity. The next is about delivering inference wherever users, applications, agents, and physical AI systems need it. A new operating model is emerging to describe it: the Token Delivery Network.

A Token Delivery Network (TDN) is a distributed inference network that brings AI model endpoints closer to users, applications, agents, physical AI systems, and other consumers of generative AI models. It delivers model responses from the best available endpoint based on proximity, performance, capacity, sovereignty, policy, and cost. Tokens can be allotted centrally and consumed across the network, giving providers simplified governance with improved performance. This model enhances the operating landscape for distributed AI inference, building on a pattern the internet has used before.

From Content Delivery to Token Delivery

Content delivery networks reshaped the internet. By moving content closer to users, they made global streaming, real-time applications, and low-latency access possible at a scale the early web could not support. When an experience becomes distributed and latency-sensitive, the architecture moves closer to the user.

A CDN serves static content from edges positioned near user populations to improve load times for media in the browser. A Token Delivery Network carries that pattern into the age of AI with one defining difference. A TDN serves models from programmable edges positioned near user and machine populations, delivering curated insights rather than cached files. Tokens are generated in real-time by models running on accelerated infrastructure, serving as the usage meter.

The Two Capabilities Behind a Token Delivery Network (TDN)

AI delivery comes together through two key capabilities:

1. Endpoint Deployment: Managing inference endpoints dynamically across distributed infrastructure with built-in governance, multi-tenancy, and metering.
2. Endpoint Selection: Directing each request to the right endpoint based on location, performance, capacity, and policy.

Rafay's Role in TDNs

Rafay operates the inference endpoints at the heart of a Token Delivery Network. It provides the operational layer that deploys, governs, exposes, meters, and monetizes distributed AI services across many locations, integrating with routing systems that connect users to the endpoints. This enables operators to launch, govern, and grow distributed AI services effectively.

From GPU Hours to Token Revenue

The economic model is shifting from GPU hours as an infrastructure metric to tokens as a service metric. Selling GPU access is valuable today but will commoditize as AI services market expands. Rafay Token Factory connects the operating model to a business model, converting GPU inference infrastructure into governed, token-metered AI services accessible via APIs, enabling tracking for billing and monetization.

The Road Ahead

Telcos, neoclouds, and sovereign clouds are well positioned for this era, as they possess the assets required for AI inference delivery. The missing layer is software that makes those assets programmable for AI inference. Most model builders will conclude that a partnership with TDNs is essential to delivering their models to the market.

Conclusion

Token Delivery Networks delineate a paradigm shift in AI delivery and infrastructure, anchoring the next decade of AI on the principles of distributed capacity and governed AI services. Rafay builds the operational layer necessary for executing this model successfully.

See how a Token Delivery Network operates

Read how the Rafay Platform powers Token Delivery Networks.