How Caching Saved Us Hundreds of Dollars in AI Costs Every Month

A lot of developers don't realize how much money they're wasting by sending the same context to AI models over and over again. We discovered this the hard way. While building AI-powered development workflows, we noticed that agents and coding assistants were repeatedly sending massive amounts of identical data entire codebase structures, system prompts, project documentation, and dependency maps for even the smallest code changes. A one-line modification could easily trigger tens of thousands of tokens in unnecessary API costs. So we built Hawiyat Composer. Instead of connecting directly to OpenAI, Anthropic, or other providers, Hawiyat Composer acts as an intelligent gateway between developer tools and AI models. Some of the optimizations include: • Exact-match caching for repeated requests (responses returned in milliseconds with zero API cost) • Semantic caching that recognizes similar questions even when they're phrased differently • Provider-side cache optimization that restructures prompts to maximize cache hits on models that support prompt caching • Smart model routing that automatically sends simple tasks to cheaper models and reserves premium models for complex reasoning • Local routing for sensitive enterprise workloads using self-hosted models In practice, this reduced AI spending dramatically while also improving response times. The surprising part wasn't how expensive AI was. The surprising part was how much of that expense came from paying repeatedly for the exact same information. Curious how others are handling AI cost optimization at scale. Are you using caching layers, prompt caching, model routing, or something else?