Together AI vs Rafay: Renting an AI Cloud vs Operating One

Introduction

Owning GPUs does not give neoclouds or telcos an AI cloud operating model. A rack of H200s or B200s is still infrastructure until operators add the software to allocate capacity, provision workloads, isolate tenants, expose APIs, meter consumption, enforce policy, and connect usage to billing.

That creates a strategic choice for infrastructure and platform teams building around accelerated compute. They can either rent AI infrastructure and model services from a cloud provider or build and run those services on infrastructure they control.

The Together AI vs Rafay comparison captures this divide through two different operating models.

Together AI operates an AI-native cloud spanning serverless inference, dedicated model deployments, and GPU clusters.

In contrast, Rafay provides the operating layer to turn the compute capacity an organization owns into governed self-service infrastructure, including GPU compute, virtual machines, Kubernetes, and SLURM (Simple Linux Utility for Resource Management). It also enables the delivery of AI services such as AI workspaces, models, and inference APIs.

The real decision is where to draw the operating boundary across infrastructure, service delivery, and commercial control. The rest of the comparison examines what each model puts in the provider’s hands and what it leaves with the customer.

What Is Together AI and What Do You Get When You Rent Its AI Cloud?

Together AI is a managed AI cloud platform that provides model inference, accelerated compute, storage, fine-tuning, and model shaping. It supports 200+ models across text, code, image, video, audio, embeddings, reranking, and other workloads. Together AI manages the underlying model serving, GPU capacity, scheduling, scaling, and runtime optimization.

It exposes several inference operating models.

• Serverless Inference runs requests on Together-managed shared capacity. It is designed for variable traffic where customers prefer token-based consumption.
• Provisioned Throughput reserves token-generation capacity. Customers buy Provisioned Throughput Units (PTUs) for workloads that need predictable throughput and an availability SLA.
• Dedicated Model Inference runs workloads on isolated GPUs. Customers can configure the inference engine, GPU type and count, parallelism, and optimization profile for latency, throughput, or balanced performance.
• Dedicated Container Inference gives teams a managed GPU infrastructure for custom models and non-standard serving pipelines.

Beyond inference, Together AI rents access to NVIDIA H100, H200, B200, and GB200 capacity through its GPU Cluster service, with InfiniBand networking, shared storage, and managed Kubernetes or SLURM environments. Clusters can be provisioned programmatically through APIs, CLI, and Terraform.

What the customer gains

Together AI abstracts the infrastructure and serving layer so customers can focus on running AI workloads. Advantages include:

• Managed infrastructure: The GPU fleet, networking, orchestration, serving infrastructure, scaling, and underlying service lifecycle are managed for the customer.
• Multiple consumption models: Teams can use serverless APIs, provisioned throughput, dedicated inference, or GPU clusters based on workload requirements.
• Faster access to compute: Developers can move from model APIs to dedicated inference or GPU clusters. They do not need to first build their own provisioning and service-delivery layer.
• AI-ready clusters: Managed Kubernetes and SLURM environments support distributed training, inference, and custom GPU workloads on Together-operated infrastructure.

The trade-off is that the consumer is restricted to Together AI's cloud environment. Together AI determines the available regions, GPU inventory, service catalog, deployment primitives, capacity options, and commercial model. Customers configure workloads within those boundaries rather than defining the cloud services, tenant model, and commercial experience themselves.

What Is Rafay and What Do You Build With It?

Rafay is an AI infrastructure platform that helps neoclouds, telcos, sovereign AI providers, and enterprise platform teams turn infrastructure they control into services customers can consume and the operator can govern and monetize.

Instead of exposing GPU capacity as standalone infrastructure, operators can package it into a self-service, multi-tenant GPU cloud platform with defined service offerings and commercial models.

An operator brings its GPU infrastructure into Rafay. They then define the service types that customers or internal teams can consume through Services You Can Launch, including:

• Bare-metal GPU servers
• GPU and CPU virtual machines
• Kubernetes clusters
• SLURM environments
• Fractional or shared GPU configurations
• Jupyter notebooks and AI workbenches
• Model endpoints
• Serverless and dedicated inference
• NVIDIA NIM and AI application services

Rafay adds the operating and commercial layer around these services. Operators can define reusable service catalogs and SKUs (predefined service configurations with specific infrastructure, software, and consumption parameters), then automate provisioning and lifecycle management. They can also expose services through APIs or self-service portals, meter consumption, and connect usage data to chargeback or external billing systems.

For model serving, Rafay Serverless Inference lets operators expose models through on-demand, OpenAI-compatible APIs. Rafay supports token-based usage tracking, tenant controls, observability, and billing integration, while Token Factory connects model delivery with token metering and monetization.

Multi-tenancy is part of the service boundary

A provider cloud also needs isolation above the physical infrastructure. Rafay Multi-tenancy Infrastructure supports isolated Kubernetes clusters or vClusters, namespaces, RBAC, network and admission policies, resource quotas, project tagging, audit logs, and cost allocation. Operators can also use secure runtime isolation such as Kata Containers and centralized identity or just-in-time access controls.

These layers come together in the Rafay AI Factory model, where infrastructure, governance, service delivery, and AI workloads operate as one platform. It enables operators to deliver compute, development environments, models, APIs, and applications.

What the operator gains

For operators, the same GPU capacity can support multiple products and monetization models, each with its own SKU, access model, and pricing.

• Service breadth: Match different consumption patterns to different service types, from dedicated bare-metal and VM capacity to Kubernetes, SLURM, workspaces, and inference APIs.
• Provider-controlled catalog: Decide how capacity is productized into SKUs, including resource configurations, allocation models, quotas, policies, and software stacks.
• Own-brand cloud: Present the service through a white-labeled experience with provider-controlled branding, domains, localization, and customer-facing catalogs.
• Commercial control: Define how consumption becomes a billable unit, using infrastructure and AI usage data for external billing, internal chargeback, or showback.
• Deployment control: Choose where services run and where data remains, including provider data centers, regional or sovereign environments, public infrastructure, and supported air-gapped deployments.

What remains the operator's responsibility

The provider still has to source GPU capacity, design the physical network and storage environment, forecast demand, define service-level objectives and pricing, maintain spare capacity, and support customers. Additionally, they must decide which workloads should run on which parts of the fleet.

Rafay helps providers productize and operate the GPU infrastructure (GPU as a Service), but the provider remains responsible for the reliability and economics of the resulting AI cloud.

Together AI vs. Rafay: How Do Their Operating Models Compare?

The choice between Together AI vs Rafay ultimately comes down to whether an organization wants to consume an AI cloud or operate one. Together AI delivers services from the infrastructure it operates. Rafay gives infrastructure owners the platform needed to package and deliver comparable service types from their controlled infrastructure.

When Should You Rent an AI Cloud or Operate One With Rafay?

The decision to rent AI infrastructure or operate an AI cloud depends on more than who owns the GPU capacity.

Choose Together AI when AI infrastructure is an input to your product. Application teams can evaluate the platform around workload-level metrics such as price per token, GPU-hour cost, time to first token (TTFT), inter-token latency (ITL), throughput, model availability, and cluster capacity.

Choose Rafay when AI infrastructure itself is the service you want to deliver. The operating metrics shift to GPU utilization, capacity allocation, tenant quotas, workload placement, service availability, and billable consumption.

But this is not a strict rent vs operate split. Demand predictability and utilization are central to the economics. An operator with sustained or forecastable demand may be better positioned to spread owned or leased GPU costs across billable workloads, while uncertain or highly variable demand increases the risk of paying for idle capacity.

Renting can reduce that capacity risk and shorten time to market, while operating the cloud gives the provider greater control over how infrastructure is packaged, priced, governed, and delivered.

So, the decision depends on the trade-off the organization is prepared to make across time to market, expected utilization, workload predictability, operational responsibility, and strategic control.

• Case study: TELUS used Rafay to build a sovereign AI Studio where developers can provision GPU-powered Kubernetes clusters and virtual machines through APIs and a self-service portal. TELUS retains control over the infrastructure and service delivery while keeping data at rest and in transit within Canadian borders.

Build and Operate a Differentiated AI Cloud With Rafay

The Together AI vs Rafay comparison ultimately comes down to who operates the service layer.

Building your own AI cloud requires deciding how customers consume GPU capacity, how services are isolated and governed, how usage is measured, and how that consumption is packaged commercially.

The Rafay AI Factory brings these requirements into one operating model. Rafay GPU Cloud and Services You Can Launch let providers build a service portfolio spanning bare metal, VMs, Kubernetes, SLURM, AI workbenches, and inference. Multi-Tenancy Infrastructure applies tenant isolation, RBAC, quotas, policies, and usage visibility across that portfolio.

For neoclouds, the same GPU fleet can support different users and workloads through dedicated compute, AI workbenches, and inference APIs. Each becomes a distinct service the provider can package and monetize.

Explore the Rafay Platform to see how GPU infrastructure can become a portfolio of self-service AI and compute services.

Want to build a differentiated AI cloud? Book time with a Rafay expert.

FAQs

Is Rafay an AI cloud provider?

No. Rafay provides the AI infrastructure platform that neoclouds, telcos, sovereign AI providers, enterprises, and other infrastructure owners use to build and operate their own AI cloud services.

Does Together AI provide Kubernetes and SLURM GPU clusters?

Yes. Together AI GPU Clusters support managed Kubernetes and SLURM workflows. Its current SLURM architecture runs SLURM over Kubernetes and exposes familiar HPC interfaces such as sbatch, srun, and SSH access to the SLURM environment.

Does Rafay support serverless inference?

Yes. Rafay Serverless Inference enables infrastructure operators to expose models as on-demand APIs while maintaining governance and operational control over the underlying infrastructure.

What services can a Rafay-powered AI cloud offer?

Depending on the operator's infrastructure and configuration, Rafay can support service delivery across bare metal GPUs, VMs, Kubernetes, SLURM, AI workbenches, notebooks, models, and inference endpoints.

Can Rafay be used to build a Together-like AI cloud?

Rafay can provide the operating layer needed to deliver many of the same service shapes, but the infrastructure provider still supplies or controls the GPU capacity, defines its service catalog, and operates the resulting cloud.

What is the main difference between Together AI and Rafay?

Together AI operates infrastructure and sells AI cloud services directly to users. Rafay enables infrastructure owners to build, govern, package, and deliver AI cloud services from their controlled infrastructure.