When does running an AI model locally make more sense than using a cloud API?

Local inference makes sense when your application needs offline operation, predictable responsiveness, or data processing that must remain on the device. Cloud inference is generally the better candidate when model capability, centralized operations, or workloads exceeding client hardware matter more. For Windows, Microsoft documents Foundry Local for on-device models and Windows AI APIs for supported tasks such as OCR and summarization. These are practical examples, not proof that a particular model will meet your quality requirements. Benchmark representative prompts on the actual devices, measuring accuracy, startup time, memory, latency, and power use. Check model availability and download requirements before relying on offline operation. Compare hardware and maintenance costs with anticipated API usage rather than treating local inference as free. A hybrid design can handle suitable tasks locally and send permitted tasks to the cloud, but make that routing explicit and prevent sensitive content from silently falling back online