How do modern prompt caching solutions like those from Anthropic actually impact monthly API expenditures for high-traffic SaaS applications?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do modern prompt caching solutions like those from Anthropic actually impact monthly API expenditures for high-traffic SaaS applications?
1 real search · 1 real-search pass
Anthropic prompt caching pricing cache write read token costs 2026 official docsGoogle ↗
OBSERVEDOther / navigational
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
Anthropic prompt caching pricing cache write read token costs 2026 official docs Google ↗
- Pricing · platform.claude.com
- Prompt caching · platform.claude.com
- skills/skills/claude-api/shared/prompt-caching.md at main · anthropics/skills · github.com
- Prompt caching · platform.claude.com