A local LLM prototype often assumes one user, warm hardware, short prompts, and no failures. Production traffic breaks those assumptions. Model loading consumes memory, concurrent requests compete for compute, long outputs increase latency, and a single process can become a bottleneck.
Design for queues, backpressure, cancellation, timeouts, and clear capacity limits. Measure time to first token, total response time, memory, queue depth, and failure rate. Batching may improve throughput, while caching can reduce repeated work. Neither fixes an overloaded system without a capacity model.
Local inference can offer privacy and predictable data location, but it also requires hardware operations, updates, monitoring, and recovery. Compare the total cost with a managed service or a hybrid design. Load-test realistic prompts before launch, and start with a limited rollout. Reliability is a system property, not a consequence of owning the model files.