Semantic Caching vs. Prompt Caching: Measuring the Break-Even Point on Real Traffic

Semantic Caching vs. Prompt Caching: Measuring the Break-Even Point on Real Traffic

In the world of production LLM traffic, the majority of requests are far more repetitive than what humans would think. Customer support bots, document summarization pipelines, and code review assistants often receive the same or semantically similar inputs hundreds of times per day. Ignoring this repetition means paying the same price for each identical computation.

There are two main ways to leverage this redundancy: Prompt Caching (provider-provided) and Semantic Caching (self-built, e.g., Redis Semantic Cache, GPTCache).

Prompt Caching involves storing and reusing input prefixes of identical requests, such as system prompts, few-shot examples, or long documents. Semantic Caching, on the other hand, involves storing and reusing responses to nearly identical queries, using embedding vector similarity to judge if two questions are nearly the same. This approach, however, carries the risk of delivering inaccurate responses in the case of false positives.

The reason why people often debate which caching technique to use is that both reduce costs but in different conditions. The break-even point at which one technique becomes more cost-effective than the other depends on specific conditions. This article aims to find the break-even point by using reproducible data from 1,000+ repeat queries.

Naive in-prompt solutions fail because they address structural issues rather than repeating patterns. Reducing prompts does not necessarily reduce costs, as system prompts and document context sent with each request often make up the majority of the input cost. Furthermore, shortening prompts can negatively impact response quality and may lead to increased costs due to reduced accuracy.

Lastly, manually reviewing repetitive responses for each of the 5,000 daily requests is not feasible, as determining duplicates would require embedding similarity calculations, making it a rule-based problem rather than a simple duplicate detection task.

Prompt Caching and Semantic Caching are different layers. Prompt caching reuses prefixes of repeated requests within the same request, while semantic caching stores and reuses responses to nearly identical queries in the embedding space. Both techniques avoid modifying the application's prompt string, which is the fundamental difference between them.

In the production architecture and code blueprints, a reproducible design and cost measurement design is provided, without any customer secrets. The workload configuration includes 1,200 queries (300 unique queries × 4 patterns: identical repetition, paraphrase repetition, typo variations, and unique queries) with Claude Haiku 3.5-tier model, priced at $0.80/M for input tokens and $4.00/M for output tokens. The average query consists of 800 input tokens (400 system prompt + 400 user query) and 200 output tokens.

The semantic cache employs Redis, along with an embedding similarity threshold of 0.92, and measures the hit rate versus the false positive rate curve by adjusting this value. Blueprint 1 demonstrates Prompt Caching using Anthropic, where the system prompt is marked with `cache_control`. The cost functions must consider the caching layer, with Anthropic's official pricing: cache write = base × 1.25 and cache read = base × 0.1.

The cost_usd function calculates the total cost based on usage, in_price_per_m, and out_price_per_m. However, it's important to note that the cache TTL of 5 minutes means that if traffic exceeds 5-minute intervals, only the write cost (1.25×) will be paid. The break-even point for prompt caching is sensitive to traffic burst patterns.