How to Track AI Spending in Production: Complete Guide

You're shipping AI features to production. Users love them. But here's the question keeping you up at night: how much is this actually costing us?

Tracking AI spending in production is different from tracking it in development. In production, you're dealing with real users, unpredictable traffic patterns, and costs that can spiral without warning. This guide covers everything you need to know about tracking AI spending effectively.

Why Production AI Spend Tracking is Different

In development, your AI costs are predictable. You control the inputs, the frequency, and the models. Production is chaos:

• Unpredictable usage: Users don't behave like your test scripts
• Traffic spikes: A viral moment can 10x your costs overnight
• Edge cases: Users find creative ways to use (and abuse) your features
• Multiple features: Different parts of your app have different cost profiles

What You Need to Track

Effective AI spend tracking in production requires monitoring multiple dimensions:

1. Cost by Feature

Not all features are created equal. Your chatbot might cost $0.02 per conversation while your document analyzer costs $0.50 per document. Track them separately.

2. Cost by User Segment

Power users often consume 10-20x more AI resources than average users. If you're on a freemium model, this matters enormously for unit economics.

3. Cost by Time Period

Track daily, weekly, and monthly trends. Look for patterns:

• Are costs growing faster than revenue?
• Are there specific days or times with unusual spikes?
• Is a particular feature's cost growing disproportionately?

4. Cost by Model

If you use multiple models, track each separately. You might discover that 80% of your GPT-4o usage could be handled by GPT-4o-mini.

Setting Up Production Tracking

There are three approaches to tracking AI spend in production:

Option 1: Manual Logging (Not Recommended)

// This works but creates maintenance burden
async function trackAICall(feature, model, tokens, cost) {
  await db.insert('ai_usage', {
    feature,
    model,
    tokens,
    cost,
    timestamp: new Date(),
    environment: 'production'
  });
}

Problems with manual logging: inconsistent implementation, cost calculation errors, no built-in dashboards, maintenance overhead.

Option 2: Provider Dashboards (Limited)

OpenAI, Anthropic, and Google all offer usage dashboards. They're useful for total spend but lack feature-level granularity and real-time alerting.

Option 3: SDK-Based Tracking (Recommended)

import { Orbit } from '@with-orbit/sdk';

const orbit = new Orbit({
  apiKey: process.env.ORBIT_API_KEY
});

// Each feature gets its own wrapped client
const chatClient = orbit.wrapOpenAI(new OpenAI(), {
  feature: 'customer-chat',
  environment: 'production'
});

const analyzerClient = orbit.wrapOpenAI(new OpenAI(), {
  feature: 'doc-analyzer',
  environment: 'production'
});

SDK-based tracking gives you automatic cost calculation, feature-level breakdowns, real-time dashboards, and zero maintenance overhead.

Essential Alerts for Production

Don't wait until the end of the month to discover a problem. Set up these alerts:

• Daily spend threshold: Alert if daily spend exceeds 150% of average
• Hourly anomalies: Alert on sudden spikes (potential abuse or bugs)
• Feature-specific alerts: Set budgets per feature
• Error rate alerts: High errors often mean wasted spend

Common Production Pitfalls

1. The Retry Storm

A bug causes requests to fail, triggering automatic retries. Each retry costs money. One team saw a $500 spike in 2 hours from this exact issue.

2. The Long Context Trap

Conversation history grows over time. A chatbot that starts cheap gets expensive as context length increases. Consider implementing context windowing.

3. The Power User Problem

A single user making 1,000 requests/day can cost more than 100 normal users combined. Implement per-user rate limits.

Getting Started Today

1. Audit your current state: List every AI feature and estimate current costs
2. Implement tracking: Add feature-level tracking to all AI calls
3. Set up alerts: Start with daily spend alerts, then refine
4. Review weekly: Make AI spend a regular part of your metrics review