Semantic vs. Prompt Caching: Finding the Cost Break-Even Point for LLM Apps

A technical analysis published on DEV Community argues that rising LLM API costs are fundamentally an architecture problem rather than a prompt engineering issue. Two main caching strategies are compared: provider-side prompt caching, which reuses repeated input prefixes at 10% of normal token cost, and semantic caching, which uses embedding similarity to skip LLM calls entirely for near-duplicate queries. The study uses a reproducible benchmark of 1,200 queries across four repetition patterns to measure where each approach becomes cost-effective. Prompt caching suits workloads with long, repeated system prompts, while semantic caching eliminates redundant calls but risks returning wrong answers on false-positive matches. The analysis concludes that neither technique touches prompt content directly, making them complementary to — not substitutes for — prompt optimization.