LLM inference providers

A new approach applies TCP-style congestion control to routing large language model requests across multiple inference providers. Instead of fixed failover rules, the system continuously adjusts how much traffic each provider receives based on measured performance, in the way TCP adapts to network conditions. The aim is better reliability and latency when serving models from several backends at once.

A widely discussed explain is examining why serving large language models is so economically strange: inference costs scale with every query, margins are thin, and providers like OpenAI, Anthropic, and Google compete on price while GPU costs remain high. Commenters are debating whether inference-as-a-service businesses can be profitable, how pricing models compare, and what this means for the future of AI startups.