Production AI API Monitoring: Building Usage and Alerting Systems from Scratch
Using AI APIs in production requires a comprehensive monitoring system to track usage, cost, quality, and error rates. This article covers building a monitoring system from zero.
Key Monitoring Metrics
Four critical metrics for API calls: usage (call count and token count), cost (daily and monthly expenses), quality (response accuracy), and error rate (proportion of each error type).
Implementation
Instrument API calls to log: timestamp, model, input_tokens, output_tokens, latency, error_type. Then report to a monitoring system (e.g., Prometheus+Grafana).
Alert Configuration
Recommended alerts: daily cost exceeds $50, error rate exceeds 5%, single response time exceeds 60 seconds, abnormal call volume for specific models (like DeepSeek R1).
Cost Visualization
Break down costs by model, project, and time dimension. ChinaWHAPI dashboard provides basic statistics, or pull detailed data via API for custom analysis.
Anomaly Detection
Set baselines and trigger alerts when usage, cost, or error rates deviate from baseline by more than 2 standard deviations — catches unexpected traffic spikes or service issues.