What Is Inference-as-a-Service?
Inference-as-a-service delivers model inference as a managed API rather than as software you run yourself. A team sends requests to an endpoint and receives model outputs, and the provider handles the GPUs, the serving engine, batching, scaling, and availability underneath. Billing is by usage, most often per token of input and output, sometimes per hour for dedicated capacity.
It is the commercial expression of the token factory idea: GPU capacity turned into a product measured and sold in tokens. The buyer trades control over the serving stack for the convenience of not operating it.
Shared vs. Dedicated
Inference-as-a-service usually comes in two shapes:
- Shared, per-token endpoints: you call a model that many customers share, and pay only for the tokens you use. Cheapest to start, no capacity planning, but latency and cost per token can become unpredictable under load.
- Dedicated endpoints: GPUs reserved for you, billed by capacity. More expensive at low volume and cheaper at high volume, with predictable latency.
The cost per token you achieve depends on the serving stack behind the endpoint: batching, KV cache handling, quantization, and how tightly the model is matched to the GPU.
Building Inference-as-a-Service on Saturn Cloud
The convenience of a metered endpoint is exactly what an operator who owns GPUs wants to offer, and what a team that wants to own its weights wants to build for itself.
Saturn Cloud provides the platform layer to do both. It serves models with engines like vLLM and NVIDIA NIM, adds multi-tenant isolation and per-user usage metering, and autoscales GPU nodes to demand. Operators can turn their capacity into a billable inference service, and teams can stand up their own endpoints, on GPU infrastructure across providers.
