Semantic Caching

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.