Training a model is episodic. Serving it is continuous. Every chatbot reply, every agent step, and every API call produces tokens around the clock, which is why the industry started calling inference infrastructure a token factory: a data center measured by the tokens it produces per watt, not by uptime or FLOPS.
This post compares the providers that run that infrastructure for you. Some are inference-as-a-service and others hand you dedicated GPUs and a serving stack you control. The distinction that matters in production is whether you can move from a shared per-token endpoint to a dedicated one without rewriting your application, so that is the lens for the comparison below.
| Provider | Type | Pricing | Best for |
|---|---|---|---|
| Saturn Cloud | Token factory platform, runs in your cloud | Your GPU cost, per-user attribution | Fine-tuning and serving your own models across clouds |
| Nebius Token Factory | Managed inference, shared to dedicated | Per token, dedicated tier | A managed catalog with a path to dedicated endpoints |
| Fireworks AI | Managed inference | Per token, serverless or dedicated | Low-latency serving of open models |
| Together AI | Managed inference + fine-tuning | Per token, dedicated available | A broad open-model catalog in one API |
| Baseten | Model serving | Per compute, scale-to-zero | Custom and fine-tuned models you package yourself |
| Amazon Bedrock | Managed inference | Per token, provisioned throughput | Teams standardized on AWS |
| Groq | Managed inference on LPU hardware | Per token | Latency-critical, interactive applications |
| CoreWeave | GPU infrastructure | Reserved or on-demand GPUs | Kubernetes-native teams running their own stack |
| RunPod | GPU infrastructure | Per-second GPU, serverless | Cost-sensitive serving and experimentation |
| Hugging Face | Managed endpoints | Per compute | Serving models straight from the Hub |
1. Saturn Cloud
Best for: Teams that want to fine-tune, deploy, and serve their own models with autoscaling across one or more GPU clouds.
Saturn Cloud is the AI token factory platform. It turns GPU capacity into token-metered inference services, deployed inside your own cloud account (Nebius, Crusoe, CoreWeave, or a hyperscaler you already use) rather than on someone else’s infrastructure. The token factory covers the full open-model path: register a dataset, run a fine-tuning job, and serve the resulting checkpoint as a persistent endpoint. Serve with vLLM or NVIDIA NIM, deploy custom APIs with FastAPI or BentoML, and scale GPU nodes to actual request volume, with autoscaling and idle detection so you are not paying for idle GPUs. Everything runs in your own account, with SSO, per-token metering, and cost tracking per user and project.
It is the only entry here that owns the whole path from a training dataset to an autoscaling production endpoint without being tied to a single GPU cloud. If you want to hold your own weights, serve across more than one provider, and see cost per user and project, Saturn Cloud is the layer that ties the rest of this list together. See the token factory docs to start.
2. Nebius Token Factory
Best for: Teams that want managed per-token inference with a path to dedicated endpoints on the same infrastructure.
Nebius Token Factory is managed inference-as-a-service on Nebius AI Cloud. It serves the major open models (Llama, Qwen, DeepSeek, GPT-OSS, Nemotron) through an OpenAI-compatible API with transparent per-token pricing and a 99.9% uptime SLA, running on NVIDIA H100, H200, B200, and GB200 hardware. Teams that outgrow shared endpoints can move to dedicated deployments on the Enterprise tier, with sub-second latency held even for workloads in the hundreds of millions of requests per minute.
It is the clearest example of the token factory model in this list: start on shared per-token endpoints while you experiment, then scale to dedicated capacity when volume justifies it, without leaving the platform. Paired with Saturn Cloud, you also get one-click deployment of your own models alongside the managed catalog.
3. Fireworks AI
Best for: Low-latency serving of open models.
Fireworks tunes its serving stack for high tokens-per-second and low time-to-first-token. It offers both serverless per-token endpoints and dedicated deployments, and it serves LoRA adapters on shared base weights, which is efficient when you run many fine-tuned variants of one base model. It is a strong fit once you have settled on a small set of open models and care mainly about latency and cost per token.
4. Together AI
Best for: A broad open-model catalog with inference and fine-tuning in one place.
Together runs a large catalog of open models behind per-token endpoints, plus fine-tuning and dedicated GPU clusters, all on an OpenAI-compatible API. It is a common first stop when your evaluation phase means benchmarking a dozen open models against your own prompts, since one API over a wide catalog saves the integration work of wiring up each model separately.
5. Baseten
Best for: Serving custom and fine-tuned models with control over the serving container.
Baseten serves models you package yourself, with autoscaling and scale-to-zero on managed GPU infrastructure. Instead of a fixed catalog, the unit is your model plus a serving definition, which suits proprietary or heavily customized weights: multimodal pipelines, custom pre- and post-processing, or fine-tuned weights you want to own end to end.
6. Amazon Bedrock
Best for: Teams already standardized on AWS.
Amazon Bedrock exposes a set of foundation models through one API with per-token pricing and provisioned-throughput options for steady workloads. Its advantage is integration: IAM, VPC, and CloudWatch apply without extra work, and there are guardrails and knowledge-base features for RAG. You pay a premium over specialized providers, and in return you do not take on another vendor relationship. It fits organizations whose security and networking posture is already built around AWS.
7. Groq
Best for: Latency-critical applications where time-to-first-token dominates.
Groq serves a curated set of open models on its own LPU hardware, built for very high tokens-per-second. The catalog is narrower than a GPU-based provider’s, but for supported models the latency is hard to match on commodity hardware. It is the right pick for interactive applications where users feel every hundred milliseconds: voice agents, live coding assistants, anything with the model in the human’s critical path.
8. CoreWeave
Best for: Kubernetes-native teams that want dedicated GPU capacity for their own serving stack.
CoreWeave provides GPU infrastructure rather than a managed model API. You bring your own serving stack (vLLM, NIM, or a custom container) and run it on dedicated GPUs with fast interconnects. It is a token factory in the infrastructure sense: you own the serving layer, they own the hardware. It suits teams with the Kubernetes expertise to run their own stack and full control over batching, caching, and routing.
9. RunPod
Best for: Cost-sensitive serving and experimentation.
RunPod offers on-demand and serverless GPU compute at competitive prices, including a serverless endpoint product for inference, billed per second. It suits smaller teams and workloads where the priority is cheap GPU access over a managed catalog: early-stage projects that can assemble their own serving stack, or that want serverless endpoints without hyperscaler pricing.
10. Hugging Face Inference Endpoints
Best for: Serving models straight from the Hub.
Hugging Face Inference Endpoints deploy models from the Hub onto dedicated managed infrastructure in a few clicks, with autoscaling and support for Transformers, TGI, and custom handlers. For teams whose models already live on the Hub, it is the shortest path from a model card to a running endpoint.
Choosing the Right Token Factory
A few factors decide it.
Shared vs. dedicated. Per-token endpoints are cheapest to start and need no capacity planning, but cost and latency get unpredictable at scale. Dedicated endpoints cost more at low volume and less at high volume, with predictable latency. Providers that let you move between the two on the same platform, like Nebius Token Factory, lower the risk of picking wrong early.
Catalog vs. custom. For standard open models, a managed catalog (Nebius, Together, Fireworks) removes work. For proprietary or fine-tuned weights, you want control over the serving container (Baseten, CoreWeave, or your own stack).
Operational ownership. Managed APIs hide batching, caching, and autoscaling. Infrastructure providers (CoreWeave, RunPod) hand you those knobs. The right choice depends on whether tuning cost per token is a differentiator for you or overhead.
Fine-tune and serve together. If you fine-tune open models and serve the results, look for a platform that manages the whole path from dataset to endpoint. Saturn Cloud does exactly this: register a dataset, run a fine-tuning job, and serve the checkpoint as an endpoint, on GPU infrastructure from providers like Nebius.
Saturn Cloud sits at the layer above these providers, so you can deploy your own models, autoscale GPU nodes to real request volume, and track cost per user and project across clouds. If you are evaluating where to run production inference, visit Saturn Cloud to learn more.
