Inference
Also known as: model inference, AI inference.
In plain English
Training a frontier model is a one-time, massively expensive event; inference is the ongoing cost of serving it to millions of users. During inference the model runs a forward pass over your input tokens, then generates output tokens one at a time, each conditioned on the ones before. Two things dominate the experience: cost (billed per input and output token, output being pricier) and latency (time to first token plus generation speed). Inference runs on GPUs or specialized accelerators; larger models cost more and respond slower, which is why cheaper 'small' models exist for routine work. Techniques like quantization, batching, speculative decoding, and prompt caching cut inference cost and speed it up. For anyone building on AI in 2026, optimizing inference — choosing the right model per task and trimming prompt length — is the single biggest lever on the bill.
Example
A support team handles 50,000 chats a month. They never train a model; every reply is an inference call. Switching routine intent classification from a frontier model to a small, cheap one — an inference-cost decision, not a quality one — cuts their monthly bill by 60% with no drop in accuracy, because the small model is plenty for that narrow task.
Inference in Vincony
Every generation on Vincony is an inference call, priced in credits rather than raw tokens so the cost is predictable. Smart Routing sends each prompt to the cheapest model that clears the quality bar, cutting inference spend without you thinking about it.
Estimate your inference costTry it — 750+ distinct models across 80+ providers on one account
Vincony bundles GPT-5, Claude, Gemini, Perplexity Sonar Pro, DeepSeek, Mistral, and 750+ other models on one $0/month account.