Cost control: prompt caching, batching and budgets
Comes up in: AI engineering, AI system design
This concept is being written. It gets the full five steps — explanation, predict, interview angle, practice with tests, and review cards. Here is exactly what it will cover, so you can study it elsewhere in the meantime.
What it covers
- prompt caching (prefix-based
- stable content first
- verifying cache hits)
- batch APIs (async, discounted — facts)
- token counting
- model routing and cascades (cheap model first, escalate on low confidence)
- per-user and global budgets
- logging usage per request
Words you will know
What you will practise
- add caching + a daily cost cap to a demo service
Interview questions this answers
- How does prompt caching work and how do you structure prompts to benefit from it?
- When would you use a batch API?