AI Glossaryinfrastructure

Token Economy / AI Pricing

The cost structure of AI APIs based on token consumption, where pricing is determined by the number of input and output tokens processed per request.

How It Works

Understanding token economics is essential for building profitable AI products. Every major AI API charges per token: input tokens (what you send) and output tokens (what the model generates). Output tokens typically cost 3-5x more than input tokens because generation requires more compute. Pricing varies dramatically: gpt-4.1-mini costs $0.40/$1.60 per million input/output tokens. Claude Opus 4.6 costs $15/$75. A 100-word response costs ~$0.0002 with the cheap model and ~$0.01 with the expensive one — a 50x difference. For a product handling 10,000 requests/day, that is $2/day vs $100/day. Cost optimization strategies: (1) Model routing — use cheap models for simple tasks, expensive ones only when needed. (2) Prompt optimization — shorter prompts cost less. Remove unnecessary instructions, use concise system prompts. (3) Caching — cache responses for repeated queries. (4) Batch API — submit non-urgent requests in batches for 50% discounts (OpenAI offers this). (5) Output limiting — set max_tokens to prevent runaway costs. (6) Context pruning — only include relevant context, not entire documents. For pricing your AI product: calculate your average cost per request, add a 3-5x margin, and price accordingly. Most successful AI products charge $10-30/month for subscriptions or $0.01-0.05 per AI-powered action.

Common Use Cases

  • 1AI product pricing strategy
  • 2Cost optimization for AI applications
  • 3Budget planning for AI features
  • 4Usage-based billing systems
  • 5Comparing AI provider costs

Related Terms

Large Language Model (LLM)

A neural network trained on massive text datasets that can generate, understand, and reason about human language.

Tokenization

The process of breaking text into smaller units (tokens) that an AI model can process, typically subwords or word pieces.

Batch Processing

Processing multiple AI requests together as a group, typically at lower cost and higher throughput than real-time individual requests.

Inference Optimization

Techniques to make AI model predictions faster, cheaper, and more efficient in production, including quantization, batching, caching, and model distillation.

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