Compute
The raw processing power required to train and run AI models. More compute means larger models, faster inference, and the ability to handle more complex tasks — but also higher cost. For businesses deploying AI, compute decisions shape the economics of every automated workflow.
What is Compute?
In AI, compute refers to the processing resources — primarily GPUs and specialized chips — used to train models and generate outputs. Training a large language model requires enormous compute: months of continuous processing across thousands of chips. Running that model in production (inference) requires less, but adds up quickly at scale. The companies that build AI models — OpenAI, Anthropic, Google — spend hundreds of millions of dollars on compute annually. For businesses consuming AI via API, compute costs are embedded in the per-token pricing they pay.
Compute is the physical constraint that determines what AI can do, how fast it can do it, and what it costs. Every time a language model generates a completion, reads a document, or processes a batch of invoices, it consumes compute. That consumption has a cost, and that cost scales directly with usage.
Why Compute Matters for Business AI
For operations teams deploying AI at volume — processing hundreds of invoices daily, running nightly batch analyses, handling real-time exception routing — compute cost is a real line item. The decisions that affect it include:
Model size — smaller, faster models cost less per call and are adequate for many classification and extraction tasks
Batch vs. real-time processing — batching overnight jobs reduces cost compared to real-time API calls for non-urgent tasks
Caching — reusing outputs for identical or near-identical inputs avoids redundant compute spend
Compute in Operational Context
Most midsize manufacturers and distributors consuming AI via API do not manage compute directly — they pay per token to a model provider. But understanding compute helps you make smarter decisions: why a model that processes a 50-page contract costs more than one processing a 2-page invoice, why latency increases under high load, and how to structure workflows to keep AI costs proportional to the value they generate.