How do I troubleshoot unexpected cost spikes in our monthly OpenAI API bill when our user traffic hasn't actually increased?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do I troubleshoot unexpected cost spikes in our monthly OpenAI API bill when our user traffic hasn't actually increased?
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
OpenAI API billing usage dashboard costs breakdown model tokens cached input tokens official documentation Google ↗
OpenAI API pricing reasoning tokens tool calls batch API official documentation Google ↗
- OpenAI Platform Usage Dashboard · platform.openai.com
- API pricing page · developers.openai.com
- Pricing · developers.openai.com
- Understanding and counting tokens · help.openai.com
- Reviewing API usage and costs · help.openai.com
- Prompt Caching guide · developers.openai.com
- OpenAI Billing · platform.openai.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
OpenAI API billing usage dashboard costs breakdown model tokens cached input tokens official documentation OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
OpenAI API pricing reasoning tokens tool calls batch API official documentation OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |