Deploying generative AI models in production involves far more than raw GPU compute costs. A comprehensive total cost of ownership (TCO) analysis must account for container orchestration (Kubernetes), networking, load balancing, autoscaling, logging, and CI systems. Using a hypothetical A100 80GB deployment, the post shows that infrastructure costs alone add ~27% over raw compute. When factoring in even one ML engineer's salary for maintenance, a self-built solution becomes significantly more expensive than a managed service — requiring that engineer to manage ~43 models just to break even. The post argues that managed services often deliver better long-term value, and warns against being misled by aggressively priced compute from VC-subsidized startups.
