Prompt Caching

The fastest path to cutting your LLM API bill by 70% is stacking five to six optimization levers simultaneously—no single strategy gets you there alone. Model routing alone saves 40–70%. Prompt caching alone saves 50–90% on cached tokens. Combine them with batch processing, semantic caching, and token compression, and the compound effect easily clears 70% total reduction. This guide walks through all ten strategies with concrete implementation steps, real savings numbers, and guidance on sequencing them for maximum impact. ...

Enterprise token costs fell 67% year-over-year in 2025–2026 — not because models got dramatically cheaper overnight, but because engineering teams finally learned to route intelligently, cache aggressively, and set hard budget limits on every agentic step. The average enterprise account now runs 4.7 distinct models (up from 2.1 in Q1 2025), open-source models captured 38% of enterprise token volume for the first time ever, and teams that adopted these nine strategies are seeing cost reductions that outpace every model pricing cut combined. ...

Prompt caching is the single highest-ROI optimization available for production LLM applications. If you run 10,000 requests per day with an 8K-token cached system prompt on Anthropic Claude, you save roughly $576/month — with a few lines of code change. OpenAI’s automatic caching requires zero code changes and gives you a 50% discount on repeated input tokens. Anthropic’s explicit caching offers up to 90% savings. This guide covers both, plus Gemini, with production code examples, real cost numbers, and the anti-patterns that silently destroy your cache hit rate. ...