Inference serving

Inference serving is the work of actually running a trained model to answer users — quickly and cheaply, at scale. It is distinct from training: training builds the model once, while serving is the recurring workload that fires every time someone sends a prompt. It matters because it is where the entire stack beneath it gets monetized; the power, chips, memory, and models only earn their keep when they are put to work answering real queries. As deployment scales, inference rather than training becomes the dominant cost and the main efficiency battleground.

If serving is inefficient, the cost of producing each unit of output climbs, and applications that looked viable stop making economic sense. Efficiency gains here compound downstream across every product built on the model. The exposed names span the specialist serving and neocloud providers — Together AI, Nebius, CoreWeave, Lambda, and Baseten — and the model owners running at massive scale, including OpenAI, Anthropic, Microsoft, Alphabet, and Amazon.