การแคชและการรวมคำขอเพื่อลดต้นทุน LLM

Every LLM call costs tokens for both input and output. At scale, repeated and redundant calls quietly dominate your bill. Caching and batching are the two biggest levers to cut cost without hurting quality.

If the same prompt is sent again, return the stored answer instead of calling the model. Use a hash of the full prompt as the cache key.

Exact-match caching shines for deterministic, repeated queries: FAQ answers, classification of identical inputs, or cached embeddings. Set temperature: 0 so the same input reliably maps to the same output.

Many questions mean the same thing in different words. Semantic caching embeds the query and returns a cached answer if a previous query is close enough in vector space.

Major providers offer prompt caching: a large, stable prefix (system prompt, docs) is cached on their side, so repeat calls only pay full price for the changing part. This can cut input cost by most of the prefix.

Put the stable content first (instructions, reference docs) and the variable user input last. Cache hits depend on an identical prefix, so order matters.

For non-urgent jobs, providers offer a batch API that processes many requests asynchronously at roughly half price. Great for offline tasks like summarizing a backlog.

Even for live traffic you can group requests that arrive within a short window into one call, amortizing fixed overhead. Balance the wait against added latency.

Stale answers are dangerous. Invalidate cached responses when the underlying data or prompt template changes, and set a TTL for anything time-sensitive.

Track cache hit rate and cost per request. A 40% hit rate cuts roughly 40% of those calls. Without measurement you cannot tell if caching is helping.

• Exact cache for identical prompts.
• Semantic cache for paraphrases.
• Prompt caching for stable prefixes.
• Batch API for offline jobs.

Layered together they slash both cost and latency.

Test your understanding of LLM cost optimization.

Cut LLM cost with exact and semantic response caching, provider prompt caching of stable prefixes, and the batch API for offline work. Order prompts for cache hits, invalidate stale entries, and measure your hit rate.