Why is AI inference expensive?
AI inference costs money because every answer uses specialized chips, memory bandwidth, model time, and often extra tools or retrieval.
The short version
Inference is the process of running a trained model to generate an answer. Large models require expensive GPUs or AI accelerators, high memory bandwidth, power, networking, and orchestration. Longer prompts and longer answers usually cost more.
Tokens drive cost
Most text models process tokens, which are chunks of words. Input tokens, output tokens, retrieved documents, conversation history, and tool results all add work. A long context window can be powerful but costly.
Latency also costs
Fast responses require available capacity. Providers may run multiple copies of models, keep hardware warm, batch requests, or reserve premium capacity for low-latency products.
Why agents cost more
Agentic workflows can call models many times, search the web, run code, inspect files, and verify outputs. The user sees one task, but the backend may perform dozens of steps.
How products control cost
Teams use caching, smaller models, routing, summarization, retrieval limits, shorter answers, batching, and hard budgets to keep inference from eating the business.