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Running a model locally and calling a hosted API involve different operational trade-offs. Neither choice is automatically free, private or simple in every situation.
Include the full cost and latency of a successful task, then compare the data-handling requirements of each deployment. Running locally changes responsibilities rather than removing them.
Count the complete workload
For local inference, consider hardware, memory, runtime setup, energy, updates and the time needed to maintain the system. For a hosted service, consider usage charges, limits, availability, data handling and integration work.
Compare the same task and quality target. A local model that needs substantial manual correction may not be cheaper for the finished workflow, while a hosted model may be unsuitable for data that cannot leave a particular environment.
Test before committing
Use public or fictional data and a representative set of inputs. Measure response quality, completion time and resource usage under realistic context lengths. Distinguish model files fitting in storage from the memory needed to serve requests.
Check the model licence and the service terms for your intended use. Do not assume local execution makes it acceptable to process data you do not have permission to use.
A sensible first decision
Choose the smallest experiment that answers your main uncertainty. That may be a brief hosted trial with a spending cap or a local prototype using hardware you already own. Avoid buying equipment before measuring the requirement.
Discussion: What would make you choose local inference over a hosted API for your next project?
Technical background: Hugging Face quantization documentation.
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