How I Cut My LLM API Costs by 70% Without Touching My Code

Описание и применение

Сокращение расходов на LLM API на 70 % без изменения кода: использование цепочек моделей, семантического кэша, пакетных запросов и выбора поставщика.

Задача

High monthly LLM API costs ($214/month) for a side project with no revenue, unsustainable expense.

Результат

Reduced monthly LLM API costs from $214 to $60 (~70% savings) while maintaining response quality and unchanged architecture.

Рабочий процесс

• Identify that most calls are simple tasks suitable for cheaper models.
• Implement a model fallback chain: try gpt-3.5-turbo, then gpt-4o-mini, then gpt-4o, with quality checks and cost limits.
• Add semantic caching: embed prompts with text-embedding-3-small, compare similarity, return cached response if similarity >0.95.
• Switch background jobs to batch API endpoints (e.g., gpt-4o-mini batch) instead of streaming.
• Compare provider pricing and switch to cheaper or multi-provider gateways for non-critical workloads.

Доказательства

This cut my costs by nearly 40% in the first week.

Источник

I went from paying for 100 repetitive answers a day to paying for about 10. That was another 25% off my bill.

Источник