What are the most cost effective ways to monitor and log token consumption across multiple LLM providers in a fast growing SaaS product
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the most cost effective ways to monitor and log token consumption across multiple LLM providers in a fast growing SaaS product
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
2026 LLM observability token usage tracking pricing OpenTelemetry Langfuse Helicone LiteLLM documentation Google ↗
OpenAI API usage token details cached input reasoning tokens Anthropic usage API Google Gemini usage metadata documentation Google ↗
- Token & Cost Tracking · langfuse.com
- Vendor Usage Field Reference · docs.nvidia.com
- Reviewing API usage and costs · help.openai.com
- Usage and Cost API · platform.claude.com
- Counting tokens · developers.openai.com
- Langfuse documentation · langfuse.com
- LiteLLM documentation · docs.litellm.ai
- OpenTelemetry documentation · opentelemetry.io
- Grafana documentation · grafana.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
2026 LLM observability token usage tracking pricing OpenTelemetry Langfuse Helicone LiteLLM documentation OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
OpenAI API usage token details cached input reasoning tokens Anthropic usage API Google Gemini usage metadata documentation OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |