Semantic Caching for LLMs: Make Repeat R…

Semantic caching stores vector embeddings of LLM responses and returns them instantly when similar prompts arrive. Here's how it works and what savings to expect.

The Hidden Cost of Repeated Prompts

In most production AI applications, users ask similar questions repeatedly. Customer support bots see the same 200 questions 80% of the time. Search assistants get near-identical queries. FAQ systems process thousands of variations of a dozen core questions.

Without caching, every one of these pays full inference cost. With semantic caching, the second — and hundredth — similar request costs essentially nothing.

How Semantic Caching Works

Step 1: Embed the Prompt

When a new request arrives, it's converted to a vector embedding using a lightweight embedding model (text-embedding-3-small at $0.02/M tokens). This takes ~20ms and costs a fraction of a cent.

Step 2: Search the Cache

The embedding is compared against previously stored embeddings using cosine similarity. If similarity exceeds the threshold (typically 0.92), the cached response is returned immediately.

Metric	Without Cache	With Cache (30% hit rate)
100k requests/mo	$500	$350
500k requests/mo	$2,500	$1,050
1M requests/mo	$5,000	$1,600

Semantic Caching for LLMs: Make Repeat Requests Cost Zero

The Hidden Cost of Repeated Prompts

How Semantic Caching Works

Step 1: Embed the Prompt

Step 2: Search the Cache

Step 3: Store New Responses

Two-Level Cache Architecture

The Economics

Setting the Similarity Threshold

NeuralRouting's Semantic Cache