Prompt Caching Is Quietly Becoming the New LLM Price War

The next LLM discount is hiding in the cache For the last two years, the AI model market has been measured in a simple way: which model is smarter, which model is faster, and which model is cheaper per million tokens. That scoreboard is still useful, but it is becoming incomplete. The next pricing battle in large language models is not only about raw input and output token rates. It is about how much of your prompt the model provider can avoid processing twice. Prompt caching is becoming one of the most important, least understood features in LLM infrastructure. It changes the cost of long-context applications, reduces latency for AI agents, and creates new questions about privacy, vendor lock-in, and evaluation. As more enterprise AI systems move from one-off chats to repetitive workflows, cached tokens may become the difference between a prototype and a product that can survive real usage. What prompt caching actually means In simple terms, prompt caching lets an AI model provider reuse work it has already done for repeated or partially repeated prompts. If an application sends the same long system prompt, policy manual, product catalog, codebase summary, or agent instructio