How to Monitor AI API Usage and Billing

Your AI features are live. Users are happy. But is your infrastructure telling you what you need to know? This guide covers how to set up comprehensive AI API monitoring and track your billing effectively.

Effective monitoring isn't just about cost—it's about understanding the health of your AI features. Let's build a monitoring system that keeps you informed without overwhelming you.

What to Monitor in AI APIs

AI API monitoring has unique requirements compared to traditional API monitoring:

1. Cost Metrics

• Total spend: Hourly, daily, weekly, monthly
• Cost per request: Average and distribution
• Cost by feature: Which features drive spend
• Cost trends: Is spend growing faster than usage?

2. Usage Metrics

• Request volume: Total API calls over time
• Token usage: Input and output tokens
• Model distribution: Which models are being used
• Feature usage: Requests per feature

3. Performance Metrics

• Latency: Time to first token, total response time
• Error rates: Failed requests by type
• Rate limit hits: Are you being throttled?

Setting Up AI API Monitoring

Step 1: Instrument Your Code

Every AI API call should be tracked. Here's the data to capture:

// Essential tracking data
{
  timestamp: Date.now(),
  feature: 'chat-assistant',
  model: 'gpt-4o',
  inputTokens: 150,
  outputTokens: 280,
  latencyMs: 1240,
  status: 'success', // or 'error'
  cost: 0.0065,
  environment: 'production'
}

Step 2: Build Your Dashboard

A good AI monitoring dashboard answers these questions:

• What's happening right now?
• Is anything broken?
• How does today compare to yesterday?
• Which features need attention?

Step 3: Define Key Thresholds

Establish baseline metrics and thresholds to watch for. These help you quickly identify when something needs attention:

Key Thresholds to Monitor

Budget Planning and Monitoring

Setting budgets helps you stay in control of AI costs. Here's how to think about budget thresholds:

Monthly Budget Milestones

• 50% of budget: Check if you're on track or over pace
• 75% of budget: Review spending patterns, consider optimizations
• 90% of budget: Evaluate if current spend is expected

Identifying Cost Anomalies

Look for these patterns when reviewing your dashboard:

• Daily spend that's 2x or more above your typical average
• A single feature consuming disproportionate resources
• Unexpected spikes during off-peak hours
• Gradual cost creep without corresponding usage growth

Feature-Level Budgets

Track costs per feature to understand what's driving your bill:

• Chat feature: Typically your highest-volume use case
• Document analyzer: High token counts per request
• Code assistant: Variable based on context window size

Monitoring Across Multiple Providers

If you use OpenAI, Anthropic, and Gemini, you need unified monitoring:

• Aggregate view: Total spend across all providers
• Provider comparison: Cost efficiency by provider
• Feature routing: Which providers power which features

Incident Response for AI API Issues

When you spot issues in your monitoring dashboard, have a playbook ready:

Cost Spike Playbook

1. Identify the affected feature(s)
2. Check for traffic anomalies or abuse
3. Review recent deployments
4. Consider temporary rate limits if needed
5. Investigate and resolve root cause

Error Rate Spike Playbook

1. Check provider status pages
2. Review error types and messages
3. Check for rate limiting
4. Test with reduced load if needed
5. Implement fallbacks if issue persists