Prompt Caching Is the Most Underrated Cost Optimization in LLM Systems
Last Updated on June 3, 2026 by Editorial Team
Author(s): Satyam Sahu
Originally published on Towards AI.
I cut my API spend by 70% without changing a single model call. Here’s the architectural decision that made it possible.
You’re probably doing cost optimization wrong.
After the intro, the author explains how prompt caching works (reusing KV-cache for a stable prompt prefix), why most teams miss it (they treat caching as a per-call feature rather than a call-pattern architecture problem), and the key implementation idea: place static context first and keep it deterministic while relegating dynamic content to later. They provide pricing math and real before/after figures from their document analysis workload, discuss Anthropic’s explicit cache_control approach versus OpenAI’s more automatic caching, outline where caching helps most and least, and highlight the cache invalidation pitfalls (even whitespace, timestamps, UUIDs, environment/config changes, and model-version differences). The piece concludes with a practical checklist and operational advice like monitoring cache_read_input_tokens to verify savings.
Read the full blog for free on Medium.
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