
The tokenomics of self-hosted LLMs | Red Hat Developer
Paying a cloud API per million tokens is straightforward until the invoice arrives. But when you move to self-hosted large language models (LLMs), the economics flip upside down: you pay for raw compute regardless of whether your GPUs are processing requests or idling.
For platform engineers and machine learning operations (MLOps) leads, tokenomics, the economics of how tokens are produced and what they effectively cost, requires looking at both what you spend and how much you serve.