Build a Semantic Cache for Your LLM App in 40 Lines of Python (And Cut Costs by Half)

Semantic caching reuses LLM responses by matching prompt meaning with embedding similarity instead of exact text.

A Python setup with Redis and SentenceTransformer (all-MiniLM-L6-v2) can help cut repeated OpenAI or Anthropic API calls and lower costs.

The risk is false cache hits: prompts that look similar can still require different answers, so broad similarity can return the wrong result.

Vector databases like Qdrant and Milvus can support this pattern, but it needs similarity thresholds and guardrails to stay reliable.