AI Pricing Models: Per-Token, Cached, Batch, and Reserved Capacity

AI provider pricing now spans per-token, cached-token, batch, and reserved-capacity tiers — each with distinct fit for different workload patterns.

What AI does well here

• Per-token: low-volume, sporadic workloads
• Cached tokens: repeated long contexts at much lower cost
• Batch APIs: high-volume async work at deep discounts
• Reserved: predictable steady-state high volume

What AI cannot do

• Optimize pricing tier choice without workload data
• Predict its own input and output token usage precisely

Practice this safely

Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.

1. 1Ask AI to explain pricing in plain language, then underline anything that sounds uncertain or too broad.
2. 2Give it one detail from "AI Pricing Models: Per-Token, Cached, Batch, and Reserved Capacity" and ask for two possible next steps plus one reason each step might be wrong.
3. 3Check prompt caching against a trusted source, teacher, adult, expert, or original document before you use it.