Platforms such as Replicate and Fireworks AI provide forms of hosted inference.
What does hosted inference remove?
It removes much of the hardware provisioning and model serving burden from the application team. The service manages the runtime, but the customer still owns input handling, evaluation, permissions, and recovery. Deployment options may include shared endpoints or dedicated capacity.
Managed infrastructure reduces operational work, not accountability for the resulting application.
When should a team use hosted inference?
It is a sensible default when speed to production and variable demand matter more than direct hardware control. Evaluate latency, cold starts, throughput, regions, data retention, reliability, and cost under the intended load. Compare serverless inference with reserved capacity when traffic becomes predictable. Choose the inference provider from measured workload results.
What remains the customer’s responsibility?
The provider runs the serving infrastructure, but the customer still owns model choice, prompt behavior, application permissions, data classification, evaluation, and recovery. It must decide what happens when the endpoint is slow, unavailable, or returns an invalid result. Version changes also need controlled testing before production adoption. Managed infrastructure reduces operational scope, not accountability for the workflow. Add AI observability around the hosted endpoint and retain an explicit fallback or human handoff for actions with material consequences.
Hugging Face’s Inference Endpoints documentation is one current example of managed, dedicated model serving.