It is one form of hosted inference, not a claim that physical servers disappear.
How does serverless inference work?
An inference provider receives a request and assigns managed capacity to execute the model. The provider owns scaling and infrastructure, while the customer consumes an endpoint. Idle capacity may scale down, which can reduce waste but introduce cold-start delay.
Paying per request is economical only when the workload shape supports it.
When should a company use it?
Serverless inference fits prototypes, bursty traffic, and workloads without strict always-warm latency. Test both typical and worst-case response times under realistic concurrency. Compare queue limits, regional availability, data controls, and unit economics with dedicated model serving. For sustained high utilization, reserved capacity can be more predictable even when its fixed price appears higher.
What changes when traffic grows?
As demand becomes steady, per-request convenience can give way to queueing, concurrency limits, or a higher unit cost than reserved capacity. Teams should compare both options using actual utilization, input and output lengths, and service-level requirements. Migration also requires capacity planning and a rollback path. Move away from serverless inference only when measured traffic makes the alternative simpler or more economical. Continue tracking cold starts and tail latency through LLM observability so the decision follows production evidence rather than a forecast.
AWS documents serverless inference endpoints as an on-demand serving option with explicit memory and concurrency settings.