Inference Cost
The computational expense of running a trained AI model to generate outputs.
Definition
Inference cost refers to the resources required to run a trained AI model in production, generating predictions or outputs from new inputs. Unlike training costs (one-time expenses to create the model), inference costs recur with every use and scale directly with usage volume.
Inference costs include compute hardware, electricity, and API fees. For large language models, these costs are typically measured per token processed, with both input and output tokens contributing to total expense.
Why It Matters
Understanding inference costs is crucial for budgeting AI-powered applications. A use case that seems cost-effective in testing can become expensive at scale if inference costs weren't properly modeled.
Marketing teams deploying AI tools need to factor inference costs into ROI calculations, especially for high-volume applications like personalization engines or automated content generation.
Examples in Practice
A company calculates that generating personalized email subject lines for their 500,000 subscriber list using a premium AI model would cost $2,000 monthly in inference fees, leading them to optimize prompt length and consider smaller models.
An agency builds a content generation workflow that routes requests to different models based on complexity, using expensive frontier models only when quality demands justify the higher inference cost.
A chatbot deployment project includes inference cost projections in the business case, modeling expected conversation volumes to ensure sustainable unit economics.