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Inference Cost

The cost of running AI models in production

What is Inference Cost?

The price you pay every time an AI model generates a response — measured per token, per request, or per hour of compute. Inference cost is the silent killer of AI products: a demo is cheap, but serving 10,000 users 24/7 adds up fast. Smart builders optimize model selection (not everything needs GPT-4), cache aggressively, and track cost-per-task the way they track unit economics.

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LV’s AI hub

Original context on AI applied to creation, research, and systems.

Creator OS

Useful when AI is part of a content workflow that still needs human judgment.

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In plain words

"Think of it like a taxi meter — every token generated adds to the fare, so route wisely."

How it works

Optimize: cache + right-size models

Key takeaways

  • Measured in cost per million tokens

  • Cache, right-size models, and compress prompts

  • The hidden variable in any AI product's unit economics

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Real-world example

Processing 1M tokens on GPT-4 costs ~$30. A busy app generating 10M tokens/day racks up $300/day. Caching frequent queries, using smaller models for easy tasks, and prompt compression can cut this by 80%.

Related terms

Inference

Using a trained AI model to generate outputs

Distillation

Compressing knowledge from a larger model into a smaller, faster one

Fine-Tuning

Training an AI model on your specific data

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