Caching and Batching for LLM Cost Savings
Learn how response caching, prompt caching, and request batching dramatically cut LLM cost and latency in production applications.
Why Cost Adds Up Fast
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.
Exact-Match Response Caching
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.
const key = hash(prompt);
if (cache.has(key)) return cache.get(key);
const out = await llm(prompt);
cache.set(key, out);All lessons in this course
- Token Efficiency & Context Management
- Latency Reduction Techniques
- Output Parsing & Validation
- Caching and Batching for LLM Cost Savings