Caching
Prompt, prefix, semantic, and KV caching — the techniques that cut repeated LLM work and the cost that comes with it.
Prefix Caching vs Semantic Caching: Which Fits Your App?
Prefix caching and semantic caching both cut LLM costs and latency - but they work at completely different layers. Here's how to choose, and when to run both.
Prompt Caching Break-Even: How Many Reads to Save Money?
Prompt caching advertises a 90% discount on cache hits - but the write premium means a low cache hit rate costs you more than no caching at all. Here's the exact break-even math and the architecture decisions that determine whether you capture the savings.
OpenAI vs Anthropic Prompt Caching: Key Differences
A direct, data-driven comparison of OpenAI and Anthropic prompt caching - covering activation, TTL, cost savings, hit rates, and a decision framework for choosing the right one.
Multi-Tier LLM Cache: Semantic, Prefix & Inference Layers
A multi-tier LLM cache stacks semantic, prefix, and inference caching layers to slash costs by up to 80% and cut latency by 78%. Here's exactly how each layer works and when to use them.
LLM Cache Pre-Warming for Off-Peak Customer Service Bots
Your customer service bot is recomputing the same system prompt thousands of times a day. LLM cache pre-warming stops that waste - and the benchmarks are staggering: 57x faster TTFT, 2x throughput, up to 90% cost reduction.
vLLM KV Cache Reuse: A Guide to Cutting Inference Costs
vLLM KV cache reuse cuts Time to First Token by 78% and triples throughput. This guide covers how Automatic Prefix Caching works, how to enable it, and how to extend it across distributed clusters.
Category-Aware Semantic Caching for LLM Workloads
Traditional caching misses 85–90% of LLM queries. Category-aware semantic caching fixes that - delivering 250× faster responses, 40–80% cost cuts, and cache coverage across your entire workload distribution.
Anthropic Prompt Caching: How It Works + When to Use It
Anthropic prompt caching lets you reuse a stable prefix of your Claude prompts instead of reprocessing them from scratch - slashing costs by up to 90% and cutting latency by more than 2x.