What strategies actually work for managing LLM token budgets when your development team keeps maxing out the monthly limits?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What strategies actually work for managing LLM token budgets when your development team keeps maxing out the monthly limits?
2 real searches · 1 real-search pass
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 (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
LLM token cost optimization prompt caching model routing batch API official documentation OpenAI Anthropic 2026 Google ↗
OpenAI API prompt caching usage pricing batch API official docs Google ↗
- Prompt caching · developers.openai.com
- Prompt caching · developers.openai.com
- Production best practices · developers.openai.com
- Batch processing · platform.claude.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
LLM token cost optimization prompt caching model routing batch API official documentation OpenAI Anthropic 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
OpenAI API prompt caching usage pricing batch API official docs OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |