AI Monitoring Dashboard Template
Template for a dashboard monitoring AI models and LLM calls. Latency, costs, errors, drift.
This template describes a monitoring dashboard for AI workloads: latency, token usage, error rates, cost per request. Optional: drift detection and quality metrics.
Variations
LLM Call Monitoring
Dashboard for OpenAI/Claude calls: latency, tokens, cost, error rate per model.
Best for: Teams making multiple LLM calls and wanting to control costs.
ML Model Monitoring
Monitoring for deployed models: prediction distribution, drift, performance over time.
Best for: Teams with custom ML models in production.
How to use
Step 1: Instrument your LLM/ML calls (log model, tokens, latency, cost). Step 2: Stream logs to time-series DB or analytics. Step 3: Build dashboard (Grafana, Metabase, custom). Step 4: Set alerts on error spike, cost threshold, latency degradation. Step 5: Review weekly and adjust models or prompts.
Frequently asked questions
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