AI API bills surprise teams: a demo that costs pennies becomes thousands per month in production. Most of that spend is recoverable. Many 123API users cut costs by up to 70% using the seven strategies below, roughly ordered by impact-to-effort.
1. Right-size the model #
Route classification and extraction to a small model; reserve frontier models for hard reasoning. See the model catalog for low-cost picks.
2. Use prompt caching #
Cache the stable system prompt so repeat tokens are billed at a discount. Detailed in the API Reference.
3. Route requests across models #
Send easy traffic to cheap models automatically — the cost optimization guide explains routing policy.
4. Trim prompts #
Remove redundant instructions; send only needed fields. Fewer input tokens is the most direct lever.
5. Batch non-real-time work #
Nightly summarization and bulk classification can use async batch endpoints (often ~50% cheaper).
6. Cache at the app layer #
Hash repeated prompts and serve from Redis before calling the API.
7. Use an aggregator with volume pricing #
123API pools demand to pass savings through — one key, many models, lower blended cost. Start with the quickstart guide.
FAQ
Will cheaper models hurt quality? Not if you route by task. Is caching worth it? Yes — the discount applies to your largest repeated tokens.
Suggested internal links (positions)
- “See the [model catalog]” → model catalog (anchor: “model catalog”).
- “Detailed in the [API Reference]” → API Reference (anchor: “API Reference”).
- “[cost optimization guide]” (item 3) → cost optimization guide (anchor: “cost optimization guide”).
- “[quickstart guide]” → quickstart guide (anchor: “quickstart guide”).
- Optional: link “rate limits” context to advanced ops if a monitoring section is added.