APIs provide access to Large Language Models (LLMs) hosted and managed by third parties. Self-hosted LLMs run fully on your own infrastructure, offering direct control over the model and environment.
APIs send your inputs to remote servers where the LLM processes data and returns results. Self-hosted deployment means you handle setup, updates, and scaling locally or in private clouds, running the model inference on your hardware.
Choosing impacts cost, control, latency, and scalability. APIs reduce overhead and speed up development but may incur higher usage fees and less customization. Self-hosting offers data privacy and performance tuning but requires significant technical resources and maintenance. For product managers, the decision shapes user experience, compliance, and long-term AI strategy.