I've seen an AI writing tool with fewer than 2,000 monthly active users burning $487/month on API costs. After systematic optimization, that dropped to $52—an 89% reduction—with no noticeable quality loss.
Instead of a 500-token universal system prompt, build task-specific minimal context:
const BASE_PROMPTS = {
writing: "You are a writing assistant. Be concise and professional.",
coding: "You are a code expert. Provide runnable TypeScript.",
analysis: "You are a data analyst. Use data to support claims.",
};
Result: 500 tokens → 30-80 tokens. 85% savings per request.
Traditional exact-match cache hit rates are terrible. Use embedding similarity:
const SIMILARITY_THRESHOLD = 0.92;
// Cache hit when user asks "What is SEO?" vs "Explain search engine optimization"
Our production semantic cache hits 34% of requests—one third of all API calls eliminated.
Not every task needs GPT-4o:
| Task | Model | Cost/1K tokens |
|---|---|---|
| Translation, spell-check | GPT-4o-mini | $0.00015 |
| Article writing | GPT-4o | $0.0025 |
| Architecture design | Claude Opus | $0.015 |
An intelligent router classifier reduced costs by 70% on simple tasks.
max_tokens
limits per intent (summary=200, article=3000)
export class TokenTracker {
getHourlyCost() { /* alert if > $5/hour */ }
getDailyReport() { /* per-model breakdown */ }
}
| Metric | Before | After | Savings |
|---|---|---|---|
| System Prompt | 500 tokens | 50 tokens | 90% |
| Output length | Unlimited | max_tokens=200 | 69% |
| Cache hit rate | 0% | 34% | 34% |
| Simple task routing | All GPT-4o | 85% mini | 70% |
| Retries | 2.3 avg | 1.1 avg | 52% |
| Monthly total | |||
| $487 | |||
| $52 | |||
| 89% |
Originally published at:
[https://jayapp.cn/en/blog/ai-api-token-cost-optimization]