Inference is the runtime use of a trained model to generate predictions or outputs. In 2026, inference cost and latency dominate the operating economics of
Inference is running a trained model to get outputs: every chat reply, embedding, or classification is an inference call. Serving stacks optimize it with batching, KV caching, quantization, and speculative decoding; capacity planning revolves around tokens per second, latency targets, and GPU economics. Training builds the model once; inference pays its bills forever.
Inference is where AI meets the P&L: it's the recurring cost line, the latency users feel, and the scaling constraint products hit. The 2026 cost-performance race, better chips, smarter serving, smaller capable models, is mostly an inference story, and 'inference-time compute' (reasoning longer for better answers) made it a quality dial too.
Training is a rare, massive capital event; inference is the perpetual operating cost scaling with usage. Most organizations never train frontier models but live and die on inference economics: optimization effort should follow the money.
Model size, tokens processed (input and especially output), context length, and hardware efficiency. Levers: smaller/quantized models, prompt caching, batching, shorter outputs, and routing easy queries to cheap models.
Spending more computation per query, extended reasoning, multiple samples, search, to buy accuracy on hard problems. It reframes inference from fixed cost to a quality/cost dial set per use case.