# Semantic Caching

> AI caching pattern that stores vector embeddings of LLM queries and responses, serving cached results when new queries are semantically similar. Cuts LLM costs by 50%+ with millisecond response times versus seconds for fresh calls.

- **URL:** https://redis.io/blog/what-is-semantic-caching/
- **Category:** Concepts & Definitions
- **Tags:** Caching, optimization, Llm
- **Updated:** 2026-03-11 13:24
- **Canonical page:** https://vectordb.ever.works/items/semantic-caching

## Details

## Overview

Semantic caching is an advanced caching pattern for LLM applications that matches queries based on semantic similarity rather than exact string matching. It dramatically reduces costs and latency.

## How It Works

1. **Query Embedding**: Convert user query to vector embedding
2. **Similarity Search**: Search cache for semantically similar queries
3. **Cache Hit**: If similar query found, return cached response
4. **Cache Miss**: Call LLM, cache embedding and response

## Performance Benefits

- **Cost Reduction**: Teams typically cut LLM costs by 50%+ 
- **Latency**: Cache hits return in milliseconds vs seconds for fresh LLM calls
- **Savings Scale**: More repetitive query patterns = bigger savings

## Implementation (2026)

Redis LangCache stores vector embeddings of queries and responses, then serves cached results when new queries are semantically similar.

## Similarity Threshold

Typical threshold: 0.85-0.95 cosine similarity
- Higher threshold: More exact matches, fewer false positives
- Lower threshold: More cache hits, potential relevance issues

## Use Cases

- Customer support chatbots
- FAQ systems
- Repetitive query patterns
- Documentation assistants
- Educational AI tutors

## Comparison

- **vs Exact Caching**: Semantic handles paraphrasing and variations
- **vs No Caching**: 50%+ cost savings, millisecond latencies
- **vs Traditional Cache**: Understands meaning, not just strings

## Infrastructure Options

- **Redis**: Exact matching + vector DB for semantic matching
- **Valkey**: Expanding semantic caching capabilities in 2026
- **Dedicated Vector DBs**: Qdrant, Pinecone for semantic cache

## Best Practices

- Monitor cache hit rates
- Tune similarity thresholds
- Implement cache invalidation policies
- Track cost savings
- Consider TTL for time-sensitive responses

## 2026 Trend

Semantic caching has become standard practice for production LLM applications, with most platforms offering built-in support.

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