What to Put in an LLM API Monitoring Dashboard
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LLM MonitoringAI DashboardToken UsageAI Observability
An LLM API dashboard should answer one question quickly: is our AI system healthy, useful, and affordable?
That requires more than request count.
Core metrics
Track:
- total requests
- success rate
- error rate
- latency
- time to first token
- input tokens
- output tokens
- estimated cost
- model distribution
- provider distribution
These show operational health.
Cost metrics
Show cost by:
- model
- provider
- feature
- customer
- plan
- environment
This helps teams find expensive workflows.
Reliability metrics
Include:
- timeout rate
- retry rate
- fallback rate
- rate-limit errors
- provider health
- queue delay
Fallback rate is especially important in multi-provider systems.
Quality signals
Add:
- user feedback
- regeneration rate
- validation failures
- tool call failures
- escalation rate
- support complaints
HTTP success does not guarantee answer quality.
Final thoughts
The best LLM API dashboards combine operations, cost, reliability, and quality. They help engineering, product, and finance teams make better model decisions.