Observability for AI Systems: Metrics That Matter

Why Observability Matters

AI systems introduce unique observability challenges. Traditional application monitoring isn't sufficient—you need specialized approaches to understand LLM behavior, costs, and quality.

Key Metrics

Performance Metrics

Latency: p50, p95, p99 response times
Throughput: Requests per second
Error Rates: Failed requests, timeouts
Queue Depth: Pending requests

Cost Metrics

Tokens per request (input + output)
Cost per request
Daily/weekly/monthly costs
Cost per user or feature

Quality Metrics

Response quality scores
User satisfaction ratings
Error rates by type
Hallucination detection

Logging Strategies

Structured Logging

Use structured logs for better analysis:

logger.info({
  event: 'llm_request',
  model: 'gpt-4',
  prompt_length: 150,
  response_length: 300,
  latency_ms: 1250,
  cost_usd: 0.03,
  user_id: 'user_123'
});

What to Log

All prompts and responses
Model parameters (temperature, etc.)
Token usage
Errors and exceptions
User interactions

Distributed Tracing

Trace AI Workflows

Track requests across services:

API gateway → Application → LLM service
Identify bottlenecks
Understand dependencies

Monitoring Dashboards

Essential Dashboards

Real-time Metrics: Current system health
Cost Dashboard: Spending trends
Quality Dashboard: Response quality over time
Error Dashboard: Error patterns and trends

Alerting

Key Alerts

High error rates
Unusual latency spikes
Cost anomalies
Quality degradation
Service outages

Debugging Tools

Request Replay

Replay specific requests for debugging:

Store request/response pairs
Replay with different parameters
Compare model versions

Prompt Testing

Test prompts in isolation:

Prompt playground
A/B testing framework
Version comparison

Best Practices

Instrument everything from day one
Use structured logging
Set up alerts early
Regularly review metrics
Document your observability setup

Conclusion

Comprehensive observability is essential for production AI systems. By monitoring the right metrics and implementing proper logging and tracing, you can maintain reliable, high-quality AI applications.