Real-Time Multi-Model Inference Cost Monitoring Dashboard Designer

You are a FinOps engineer specializing in LLM API cost optimization. I need you to design a real-time monitoring dashboard for tracking multi-model LLM inference costs. Here is my setup: [describe your models, providers, and usage patterns] Design the following: ## 1. Dashboard Layout Create a detailed specification for a monitoring dashboard with these panels: - **Cost Overview**: Total spend (daily/weekly/monthly), burn rate, projected monthly cost - **Per-Model Breakdown**: Cost by model (GPT-4o, Claude Opus, Gemini Pro, etc.) with input/output token split - **Per-Feature Breakdown**: Cost by application feature or API endpoint - **Token Efficiency**: Average tokens per request, cache hit rates, prompt compression savings - **Anomaly Detection**: Spike alerts, unusual patterns, runaway loops - **Cost Optimization Score**: 0-100 score with actionable recommendations ## 2. Alert Rules Define alert thresholds: - Daily spend exceeds $X (configurable) - Single request costs more than $Y - Token usage spikes >3σ from rolling average - Cache hit rate drops below Z% - Model error rate increases (wasted tokens) ## 3. Data Schema Design the logging schema for capturing: ```json { "timestamp": "ISO-8601", "model": "string", "provider": "string", "feature": "string", "input_tokens": "int", "output_tokens": "int", "cached_tokens": "int", "cost_usd": "float", "latency_ms": "int", "status": "success|error|timeout" } ``` ## 4. Optimization Recommendations Engine Based on usage patterns, automatically suggest: - Model downgrades for simple tasks (e.g., use Haiku instead of Opus for classification) - Prompt caching opportunities - Batch processing candidates - Rate limiting strategies - Provider arbitrage (cheapest model for equivalent quality) Output the complete dashboard specification with Mermaid diagrams for data flow, SQL queries for key metrics, and implementation recommendations (Grafana/Datadog/custom).