📄 中文摘要
AI驱动的应用程序在API调用上的支出通常比实际需要多出60-80%。造成这一现象的原因并非AI API本身昂贵,而是大多数开发者在使用时只依赖单一模型,忽视了缓存机制,并且从未关注其令牌计数。该指南涵盖了所有经过验证的降低AI API成本的策略,提供了2026年2月的真实定价数据、有效的代码示例,以及一个计算器,能够准确显示每种技术所节省的费用。
📄 English Summary
The Complete Guide to AI API Cost Optimization in 2026
AI-powered applications typically spend 60-80% more on API calls than necessary. This issue arises not from the high costs of AI APIs, but from developers relying on a single model for all tasks, neglecting caching, and failing to monitor token counts. The guide outlines proven strategies for reducing AI API costs, featuring real pricing data from February 2026, working code examples, and a calculator that demonstrates the savings from each technique.
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数据源: OpenAI, Google AI, DeepMind, AWS ML Blog, HuggingFace 等