What Is Inference?
Running a trained model to get answers: the phase you actually pay for, and the industry's great cost battle. Here is the plain-English deep dive: what it means, why it matters, and how to use the concept in practice.
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AI has two lives: training, the months-long, wildly expensive process of creating a model, and inference, every moment after, when the trained model answers questions. Each word ChatGPT writes you is inference: your prompt flows through billions of parameters on GPU-loaded servers, output emerges one token at a time, and someone pays for the electricity and silicon that made it happen.
Training costs make headlines (hundreds of millions per frontier model), but inference is where the industry's economics actually live, because it scales with every user, every day, forever. A model trained once serves billions of requests, and shaving 30 percent off inference cost is worth more than almost any other engineering win. This is the war behind falling API prices: better chips (Nvidia's latest, Google's TPUs, custom silicon from every major player), smarter serving (batching, caching repeated prompt prefixes, speculative decoding), and smaller models distilled to match bigger ones on specific tasks.
For users and builders, inference economics explain most of what you see. Why is the frontier model slower and pricier than the small one? More parameters per token generated. Why do providers offer batch discounts and prompt caching? Because reuse is nearly free for them. Why can a laptop run a decent model now? Because quantization and efficient architectures shrank inference to consumer hardware (see open weights). Our API calculator turns these dynamics into your actual monthly number.
The trendline worth knowing: inference cost for a given capability level has fallen roughly tenfold every year or so. What is expensive today is cheap next year, which rewards building things slightly ahead of the economics.
