Monitoring & Analytics Advanced

Comprehensive monitoring is essential for operating an LLM gateway reliably. You need visibility into request patterns, latency, errors, cost, and quality across all providers and consumers to make informed operational decisions.

Key Metrics

• Request Volume: Requests per second/minute/hour by model, team, and provider
• Latency: Time to first token and total response time at p50, p95, and p99 percentiles
• Error Rate: Percentage of failed requests by error type (rate limit, timeout, server error)
• Token Usage: Input and output tokens per request, per team, per model
• Cost: Real-time cost accumulation by team, model, and provider

Logging Architecture

• Log all request metadata: timestamp, model, provider, team, tokens, latency, status code, and cost. Do NOT log prompt/response content by default.
• Implement optional content logging with explicit opt-in per team/key for debugging purposes, with automatic retention limits.
• Use structured logging (JSON) and ship to a centralized log aggregation system (Elasticsearch, Loki, Datadog).

Alerting

• Configure alerts for: error rate spikes, latency degradation, budget thresholds, provider outages, and unusual usage patterns.
• Use multi-channel alerting: Slack/Teams for informational, PagerDuty for critical, and email for daily summaries.
• Implement anomaly detection to catch unusual patterns that static thresholds would miss.

Dashboards

• Build operational dashboards showing: real-time request flow, provider health, error rates, and capacity utilization.
• Build business dashboards showing: cost trends, team adoption, popular use cases, and ROI metrics.
• Build quality dashboards showing: user feedback scores, response latency trends, and model comparison metrics.