How to Cut Your AI API Costs by 30% Without Changing Models

How to Cut Your AI API Costs by 30% Without Changing Models Most teams overpay for AI API calls. Not because they picked the wrong model, but because they're ignoring three optimizations that require minimal code changes: prompt caching, smart model routing, and batch processing. Here's a breakdown of each technique with real numbers. 1. Prompt Caching: The Biggest Win If your application sends the same system prompt with every request, you're paying full price for tokens the provider has already processed. How It Works OpenAI caches prompts automatically for inputs over 1,024 tokens. Cached tokens cost 50% of the standard input price. You don't need to change anything in your code. Anthropic uses explicit caching via cache_control breakpoints. The write cost is 25% higher than standard input, but reads cost 90% less. Cache TTL is 5 minutes, extended on each hit. The Math Take a typical customer support bot: System prompt: 2,000 tokens User message: 200 tokens average 5,000 requests/da