Prompt Caching Strategies to Cut LLM Costs by 70%

Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical

<p><strong>TL;DR: We turned on vLLM's prefix cache for our agent workloads at Nexus Labs and watched TTFT drop from 480ms to 110ms on one tenant and stay exactly the same on

<p>AI coding assistants are useful. They're also expensive if you're not paying attention. I was spending $120/month before I started tracking. Now I spend under $50 for the same

<p>Prompt caching is not a setting you enable. It is a property your prompt either has or does not have, and one timestamp in the wrong place removes it from every request you

<p>Imagine you have a super-smart assistant. This assistant can write stories, create images, or even help plan your day. All you have to do is tell it what you want. You don't

<h1> Static Analysis for LLM Prompt Security: A Methodology for Pre-Deploy Vulnerability Detection </h1> <p><em>How applying SAST principles to LLM prompt strings catches security

<p><em>Originally published at <a href="https://theculprit.ai/blog/anthropic-prompt-caching-90-percent" rel="noopener

<p>If you're calling an LLM API for every single user request, you're almost certainly paying for the same question more than once. Not because your users are dumb because human