Two deployment choices; scoped for the workload.
Define what a useful answer looks like
Choose representative inputs and a task-specific way to judge outputs. Record unacceptable failures as well as successful answers. A model that performs well on a public benchmark may still be a poor fit for your documents, terminology or workflow.
Establish the data boundary
Identify what is sent to the model and what must remain in your environment. Review the provider’s current data-handling terms, retention and deployment options for the specific service. Self-hosting changes where inference runs; it does not remove the need to control logs, access and application data.
Measure the complete request
Measure time in the queue, time to first output and time to finish under representative load. Account for context length and concurrent requests when assessing memory and capacity. A fast isolated demo can hide the behaviour users encounter when the service is busy.
Compare the service cost, not just the model price
For self-hosting, include hardware or instance cost, capacity headroom and the work of deployment and operation. For an API, include usage, retries, limits and the infrastructure that still surrounds the provider. Neither route removes backend engineering.
Keep the application boundary stable
Give the product a defined backend contract instead of coupling every screen to one model. That makes it easier to evaluate a different deployment later. Provider changes still require testing: compatible request formats do not guarantee equivalent outputs or behaviour.