What is an LLM Cost Analytics Dashboard?
This is your control panel for monitoring how your LLM budget is being spent. It goes beyond API usage stats and surfaces issues like over-generation, retries, and unstable prompts.
Who Needs It & What It Helps Decide
Engineering leaders, data scientists, and AI ops teams need this to answer one question: Are we burning tokens responsibly? It also helps finance teams forecast cloud usage.
Key Metrics & KPIs to Track
- Token-per-response distribution
- Retry rate and regen patterns
- Cost per completion
- Temperature variance and determinism
- Prompt health signals – see prompt health
- Prompt efficiency trends – learn more at prompt analytics for developers
Ideal Dashboard Layout
Split your dashboard into 3 columns:
- Token usage flow – track usage over time
- Error & retry surfaces – show regen, failure, and max_tokens triggers
- Cost overlays – add per-run or per-feature API costs
Try the demo dashboard to see this in action.
Implementation Checklist
- Install DoCoreAI from pip
- Enable logging of prompt/response metadata (no content stored)
- Link your key usage buckets to DoCoreAI backend
- Review insights like dashboard insights from PyPI usage
How to Prove ROI from These Metrics
Compare your model usage before and after introducing structured prompt telemetry. Look for:
- Reduction in retry rate
- Stabilized token cost per task
- Shorter debug cycles and clearer team ownership
Refer to our temperature tuning guide to complement optimization.
FAQ – Common Questions
Does this work with OpenAI & Groq?
Yes, DoCoreAI integrates with both.
Do I need to send prompt content?
No, only metadata is used—no raw prompt text is logged.