Owning GPU infrastructure is not the same as owning an AI cloud business. An infrastructure provider may have racks of accelerators and fast networking. Yet, it may still lack the software layer to turn that capacity into a usable product for developers and customers.
That layer exposes models as APIs, isolates tenants, controls access and quotas, meters consumption, integrates with billing, and provides a self-service experience. Without it, the operator is still primarily selling only infrastructure rather than inference as a service.
This is where Nebius Token Factory vs Rafay comes in.
Nebius Token Factory bridges the gap by managing inference infrastructure and handling the underlying serving stack. Rafay Token Factory gives infrastructure owners the software to turn their GPU capacity into multi-tenant, token-metered AI services.
Choosing Nebius shifts infrastructure responsibilities away. Building with Rafay lets the operator control the service catalog, pricing, tenants, branding, and customer relationship.
So, the question is no longer whether you need AI inference but whether you want to offer it as a service or run a full AI business.
Nebius Token Factory is a managed production inference platform for running open and custom AI models. It lets you avoid the hassle of managing GPU infrastructure yourself.
Nebius Token Factory runs on Nebius AI infrastructure and provides an OpenAI-compatible API for various models, such as Llama, DeepSeek, Qwen, Mistral, GPT OSS, and Nemotron. It supports over 60 open-source models as well as custom and fine-tuned models for production workloads.
Nebius offers two different inference operating models.
Nebius changes the operating model by moving most of the serving stack out of the application team’s responsibility. Advantages include:
The trade-off is ownership of the service. Nebius defines the hosted catalog, pricing, supported regions, GPU templates, and platform boundaries. The customer builds on top of a Nebius inference service rather than owning the end-to-end infrastructure and commercial layer.
That puts Nebius parallel to other managed platforms such as Microsoft Foundry serverless deployments and Amazon Bedrock. Microsoft hosts serverless models on Microsoft-managed infrastructure and exposes them through APIs. Bedrock provides serverless access to models without requiring customers to manage the underlying infrastructure.
Rafay Token Factory is the software layer that lets infrastructure operators turn GPU infrastructure into a monetizable AI service.
Instead of buying inference, the operator builds an inference service. Rafay provides the framework to publish OpenAI-compatible model APIs through shared or dedicated endpoints, isolate tenants, meter consumption, enforce policies, and connect usage to billing or chargeback.
The underlying workflow is more explicit than a managed inference API.
An operator first registers GPU resources, creates an inference endpoint, and onboards a model. A model deployment then binds that model to an endpoint and defines its runtime configuration, including inference engine, replicas, CPU, memory, GPU allocation, and storage.
For example, an operator can deploy vLLM by explicitly selecting the runtime image and resource allocation. This gives production operators the precision to optimize for throughput and margin across any accelerator fleet.
On the commercial side.
Rafay Token Factory counts input and output tokens separately. For each deployment, the operator can set the pricing and currency for every million tokens. Models and deployments can then be shared only with selected tenant organizations. Tenant users access approved models through the Developer Hub, generate API keys, and call the resulting endpoints.
Rafay Serverless Inference handles the model serving side of that stack, while Token Factory provides the commercial engine to package and sell it.
Rafay changes the operating model by giving infrastructure owners the software layer needed to package GPU capacity as a governed AI service. Key capabilities include:
From GPU infrastructure to AI services: Rafay Token Factory layer for deploying, governing, metering, and monetizing distributed inference endpoints.
Rafay does not remove the responsibilities of running an inference business. The operator still needs to source GPU infrastructure, forecast capacity, select models and inference engines, and design service levels. They also need to manage support, set pricing, and meet applicable security and data-governance requirements.
Rafay provides the software layer for service delivery, governance, and monetization. But the operator remains responsible for the infrastructure, commercial strategy, and the service's long-term success.
The key difference in Nebius Token Factory vs Rafay comes down to control. With Nebius, the API is a product you buy that solves a consumption problem. With Rafay, the API is a product you build and deliver to your own customers, and it creates a new revenue stream.
Here’s a summary of the comparison table:
Buying inference optimizes for application economics. Building an inference service requires the operator to balance infrastructure and service economics in parallel.
For Nebius customers, inference is an operating expense that supports an application. Success is measured by model quality, price per token, throughput, time to first token, and latency.
For Rafay operators, inference becomes the product being delivered. That shifts the focus toward GPU costs, token generation, average utilization, replica idle time, model memory requirements, request concurrency, support cost, and the price customers will pay for each model.
For example:
Cost per million tokens = (Infrastructure + Platform + Network + Operating Cost) ÷ Tokens Served
The operator then decides how to price above that cost.
This makes utilization critical, as even a competitive token price can mask poor unit economics if expensive GPUs spend too much time idle or underutilized. So operators need to monitor throughput, queue depth, autoscaling behavior, model mix, and demand by tenant.
Token metering also changes how services are offered, allowing model consumption to be packaged in familiar units such as input tokens, output tokens, API requests, or service tiers.
Rafay supports that transition through token-level usage metering, billing-ready data, chargeback and showback, and tenant controls. But profitable economics depend on demand, utilization, model efficiency, pricing strategy, and operational execution.
Choose managed inference if inference is just an input to your product. Build the service if inference itself is integrated into your product offering.
Nebius is relevant when for your teams needing reliable model access without managing infrastructure, especially if you lack GPU capacity, need to deploy quickly, or want to focus engineering efforts on your application.
Rafay fits best when you already have GPU resources and want multiple customers, departments, or tenants to use them as services (GPU-as-a-Service).
Four questions help clarify the difference:
Running models on GPUs is only one step in delivering AI services. Operators also need a way to expose those models to developers, govern tenant access, measure consumption, and connect usage to commercial workflows.
Rafay brings those capabilities together into a unified AI platform. Token Factory packages models as consumable services, Multi-Tenancy Infrastructure governs how organizations access those services. And the AI Factory provides the operational foundation for managing infrastructure, AI workspaces, models, applications, and self-service delivery.
This allows neoclouds, sovereign clouds, telecom providers, and enterprise infrastructure teams to move beyond selling raw GPU capacity. Instead, they can deliver branded AI services with consistent governance, commercial controls, and a developer experience that scales across multiple regions and customer organizations.
GPU infrastructure creates compute capacity. Rafay helps turn that capacity into AI services customers can discover, consume, and pay for.
Learn more about the Rafay Token Factory. Want a deeper dive into the Rafay Platform? Book time with an expert.
No. Rafay provides the platform that enables organizations to deploy and deliver inference services on GPU-backed infrastructure they own, lease, or control.
Yes. Rafay can expose deployed models through OpenAI-compatible endpoints that tenants and applications access using API keys.
The operator sets the currency and separate rates per million input and output tokens for each deployment.
Yes. Nebius offers isolated dedicated endpoints with configurable GPU capacity, regions, autoscaling, and custom weights for eligible models.
No. Rafay also supports internal enterprise use cases involving governed access, usage visibility, quotas, showback, and chargeback.
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