Cache economics6 minUpdated 2026-07-04
The cache-heavy workload model
How prompt caching changes the economics of document processing, coding agents, support drafts, research workflows, and repeated tool use.
Audience
Product and platform teams
Metric
Cache hits change the route
Sources
2 official links
Why caching matters
Repeated system prompts, shared instructions, stable schemas, and reused context can make cached input pricing the difference between a workable architecture and an inflated bill.
Where caching fails
Caching assumptions fail when prompts include noisy user data, timestamps, unstable retrieval chunks, or oversized contexts that change on every request.
- Measure cache hit rate in logs, not in planning documents.
- Separate stable instructions from volatile task input.
- Chunk documents instead of sending a large context every time.
Routing implication
A cache-heavy workload can support a small frontier reserve. A cache-light workload usually needs more batch processing, shorter outputs, or a cheaper primary model.