Prompt Caching

When does inflating the system prompt actually lower cost per call?

'Prompt caching cuts our bill by 90 percent across the board', what's overclaimed?

A teammate randomized system prompts per user and expects the prompt cache to still help, what's wrong?

Predict the break-even read count for Anthropic prompt caching given the 1.25x / 0.1x rates

Rank these chat-API cost levers from highest to lowest typical ROI

Estimate the token tax a single tool round-trip adds compared with an inline answer

Prompt caching shows up on Anthropic and OpenAI pricing pages, what does it actually do?

Why does an agent loop with 5 tool calls have ~6× the latency of a single completion and how do you mitigate?

Which mitigation has the largest impact on agent-loop latency when tool calls are independent?

How do Anthropic and OpenAI prompt-cache pricing models work and what counts as a cache hit?

Which prompt-construction pattern is most likely to actually hit the provider's prompt cache?

Decompose the cost of a single API call into its components and explain which dominates.

Predict the total cost of one Claude Sonnet API call with realistic token counts

Your team is paying $40k/month on LLM tokens: most calls share a long system prompt + 8k tokens of few-shot examples. How does prompt caching help and what's the expected savings?

In a production LLM app with a stable system prompt + long retrieved context + variable user query, which part of the prompt becomes cacheable via Anthropic/OpenAI prompt caching?

Which of the following content types belong in the SYSTEM message (not the user message) in a production chat-style prompt?