Generative AI infrastructure: Comparing API management and self-hosting

Enterprises are evaluating different infrastructure strategies for deploying generative AI, specifically comparing the use of commercial APIs against self-hosting open-weight models on dedicated GPUs.

While self-hosting can offer benefits regarding data sovereignty, privacy, and customization, it presents significant economic challenges related to hardware utilization. Research indicates that underutilization of hardware can dramatically increase the effective cost per token. For example, in experiments using H100 hardware, effective costs ranged from $0.21 to $15.25 per million output tokens, with costs increasing significantly when the system operates near idle capacity.

To manage these models effectively, technical workflows are increasingly focusing on API management techniques. These include implementing load balancing, authentication, semantic caching, and security measures such as private endpoints to protect enterprise applications and optimize Large Language Model (LLM) integration.