Token Delivery Network for AI Inference | Rafay
Operate Token Delivery Networks for Distributed AI Inference
AI inference is becoming increasingly distributed as applications, agents, and intelligent systems demand faster responses, stronger data sovereignty, and lower latency. However, GPU infrastructure is often fragmented across data centers, cloud regions, telco edge locations, and sovereign environments.
A Token Delivery Network is a distributed AI inference architecture that brings AI model endpoints closer to users and solves this issue. However, Rafay's Token Delivery Network (TDN) takes this further. It enables providers to transform distributed compute into a unified AI inference platform that delivers governed, token-metered AI services, bringing inference closer to where it is consumed while creating new opportunities to monetize AI services.
The Rafay Platform delivers the operational workflows and controls that make it easy for providers to centrally deploy generative AI models across the network, manage endpoint lifecycle, meter token usage, etc. The result is a highly performant Token Delivery Network.
What Is a Token Delivery Network?
A Token Delivery Network, or TDN, is a distributed AI inference network that brings model endpoints closer to users, applications, agents, physical AI systems, and other consumers of generative AI models.
AI factory platforms make GPU infrastructure consumable through self-service access, governance, and automation. Token Delivery Networks extend these platforms by enabling distributed AI inference, delivering token-metered AI services from the optimal location based on latency, cost, capacity, and sovereignty requirements.
With all applications beginning to leverage generative AI models to deliver improved user experiences, the need for model endpoints to be closer to devices is driving many providers to invest in TDNs. Tokens can be allotted centrally but consumed across the network, resulting in the best of both worlds: Simplified governance with improved performance.
TDNs vs. CDNs: From Content Delivery to AI Delivery
A Content Delivery Network (CDN) distributes static content such as images, videos, web pages, and application assets closer to users to reduce latency and improve performance. Raw data transfer is tracked, and the size of the data transferred serves as the usage meter for these interactions.
A TDN applies the same distributed architecture principles to AI inference, delivering model responses from the optimal endpoint based on latency, capacity, cost, and sovereignty requirements.
| Features | Content Delivery Networks | Token Delivery Networks |
|---|---|---|
| Deliver | static or pre-generated content | real-time AI inference |
| Content | Optimize model response performance | Cache content at edge locations |
| Deploy | model endpoints across programmable edges | Measure usage by data transfer volume |
| Route requests | based on proximity and availability | based on proximity, capacity, cost, and policy |
| Improve | page load times | AI application responsiveness |
GPUs vs Tokens: Why Token Economics Are Replacing GPU Economics
GPU hours measure infrastructure consumption, whereas tokens measure the value AI services deliver. Here's a quick overview of how they compare:
| GPU-Hour Model | Token-Based Model |
|---|---|
| Infrastructure-centric | Service-centric |
| Low visibility | Per-request visibility |
| Difficult chargeback | Granular attribution |
| Limited pricing options | Flexible consumption models |
| Capacity-driven | Outcome-driven |
As AI moves from experimentation to production, the need for monetization models that align with how customers actually consume AI will grow. Charging for GPU capacity treats inference as a hardware resource, while token-based pricing reflects the real unit of value: model interactions.
This development is good news for telcos, neoclouds, and sovereign AI providers, as this shift creates an opportunity to move beyond GPU utilization metrics and participate more directly in the economics of AI inference.
How Rafay Enables Token Delivery Networks
Rafay takes distributed GPU infrastructure into a unified AI inference platform that supports distributed AI inference, token-based pricing, and AI service monetization.
Token Factory
Converts GPU inference infrastructure into governed, token-metered AI services exposed through APIs.
Programmable Edge Orchestration
Deploys and manages inference endpoints across distributed data centers, sovereign regions, and edge-adjacent sites, making non-contiguous compute consumable as one coordinated platform.
Multi-Tenancy and Governance
Enforces isolation, RBAC, quotas, policy, and secure access across teams, tenants, customers, and regions without sacrificing shared infrastructure efficiency.
Usage Metering, Chargeback, and Billing APIs
Tracks token consumption, attributes cost and revenue, and feeds billing workflows — enabling commercially viable token-metered AI services.
Frequently Asked Questions
What counts as a node?
A node is a physical or virtual server/machine.How much does Enterprise Support (24x7x365) cost?
Enterprise Support is available at an additional fee equaling 20% of the cluster or node subscription.What is a Token Delivery Network?
A Token Delivery Network, or TDN, is a distributed AI service architecture that delivers model responses from the best available inference endpoint based on proximity, performance, policy, sovereignty, capacity, and cost.What role does Rafay play in a Token Delivery Network?
Rafay provides the operational layer that turns distributed GPU infrastructure into a governed Token Delivery Network.How does Rafay help operators move from GPU infrastructure to AI services?
Rafay helps operators transform GPU infrastructure into self-service AI platforms with governance, multi-tenancy, metering, catalogs, API access, and monetization workflows.