Optimizing the cost and latency of your LLM calls with Prompt Caching
The post Why Care About Prompt Caching in LLMs? appeared first on Towards Data Science.
Why Care About Prompt Caching in LLMs?
Towards Data Science / 3/14/2026
💬 OpinionTools & Practical Usage
Key Points
- Prompt caching can reduce both cost and latency for LLM calls by reusing responses to repeated prompts or prompts with similar structure.
- Designing an effective cache requires thoughtful choices about cache keys, TTLs, and eviction policies to maximize reuse while avoiding stale or incorrect results.
- Trade-offs include potential staleness, privacy concerns, and storage overhead, which must be weighed against latency and cost benefits.
- A practical approach involves measuring cache hit rate and latency improvements, plus warming the cache with representative prompts to improve initial performance.
- Integrating prompt caching into existing serving stacks and continuously monitoring impact helps ensure reliable performance gains and user experience.
