AI API Cost Control: How to Track and Reduce LLM Spend

AI API costs can spiral quickly. What starts as a $100/month experiment becomes a $10,000 problem at scale. Controlling AI costs isn't about spending less—it's about spending smart.

This guide covers practical strategies for AI API cost control: how to track spending, set effective budgets, and reduce costs without sacrificing quality.

Why AI Costs Get Out of Control

AI pricing is different from most SaaS costs. You pay per token, and usage scales with your users. A feature that costs $1/day in development can cost $1,000/day in production.

Common reasons AI costs spike:

• No visibility — You don't know which features cost what
• Wrong models — Using GPT-4o for tasks that GPT-4o-mini handles fine
• Bloated prompts — System prompts with unnecessary instructions
• No guardrails — A bug or spike can burn through budgets
• Duplicate requests — Paying multiple times for the same computation

Step 1: Get Visibility

You can't control what you can't see. The first step is tracking costs at the feature level, not just totals.

Provider dashboards (OpenAI, Anthropic, Google) show aggregate usage. They don't tell you:

• Which feature in your app costs the most
• Cost per user or customer
• Whether costs are trending up or down
• Which errors are wasting money

Set up tracking that tags every API call with context:

// Tag each API call with feature and context
const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [...],
});

// Track with context
await trackUsage({
  feature: 'customer-chat',
  model: response.model,
  tokens: response.usage.total_tokens,
  cost: calculateCost(response.usage),
  user_id: userId,
});

Or use an SDK that handles this automatically:

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

const client = orbit.wrapOpenAI(new OpenAI(), {
  feature: 'customer-chat',
  environment: 'production'
});

// All calls automatically tracked with context

Step 2: Set Budgets and Alerts

Once you have visibility, set spending limits. This prevents surprises and catches issues early.

Daily Spending Alerts

Set alerts at 50%, 75%, and 90% of your daily budget. If Tuesday hits 75% by noon, something might be wrong.

Per-Feature Budgets

Allocate budgets to individual features. If your chatbot should cost $500/month and it's trending toward $2,000, you'll know immediately.

Rate Limits

Set per-user or per-customer rate limits. This prevents any single user from running up costs:

// Simple rate limiting
const userRequests = await getRequestCount(userId, '1h');

if (userRequests > MAX_REQUESTS_PER_HOUR) {
  throw new Error('Rate limit exceeded');
}

Step 3: Optimize High-Cost Features

With visibility and alerts in place, focus optimization on features that matter. Prioritize by cost impact.

Model Selection

Not every task needs the most expensive model. Here's a simple decision framework:

Prompt Optimization

Every token costs money. Audit your prompts for bloat:

• Remove unnecessary politeness ("Please kindly...")
• Cut redundant instructions
• Use examples only when needed
• Keep system prompts lean—they're sent with every request

Caching

If users ask similar questions, cache the responses. This works well for:

• FAQ-style queries
• Static content generation
• Classification with limited categories

Step 4: Monitor Continuously

Cost control isn't a one-time project. Make it part of your regular process:

• Weekly reviews — Check cost trends and feature breakdown
• Monthly audits — Review model choices and prompt efficiency
• Alerts — Respond to anomalies immediately

Cost Control Checklist

• ✓ Track costs per feature (not just total)
• ✓ Set daily spending alerts
• ✓ Implement per-user rate limits
• ✓ Use smaller models for simple tasks
• ✓ Audit and optimize top-cost features
• ✓ Cache repeated queries where appropriate
• ✓ Review costs weekly