# How We Cut Inference Costs by 60% with Prompt Caching
When we first deployed our AI pipeline to production, inference costs were our biggest line item. Every API call to our LLM provider carried a per-token cost that scaled linearly with traffic. As our user base grew, so did our bill — at a rate that wasn't sustainable.
The Problem
Our system processes thousands of requests per hour, each requiring context about our domain — sports analytics, historical match data, and betting market trends. This context was being sent with every single request, consuming tokens (and dollars) redundantly.
The Solution: Prompt Caching
We implemented a multi-tier caching strategy:
1. System prompt caching — Our base instructions rarely change. By caching the system prompt at the provider level, we eliminated ~2,000 tokens per request. 2. Context window deduplication — For users in the same session, we hash the conversation context and reuse cached completions for identical prefixes. 3. Semantic caching — For common queries (e.g., "What are today's top picks?"), we cache responses and serve them within a freshness window.
Results
After rolling out prompt caching across our pipeline:
- 60% reduction in inference costs
- 40% improvement in p50 latency (cached responses return in ~200ms vs ~800ms)
- Zero degradation in output quality (validated via our eval suite)
Key Takeaways
Prompt caching isn't just about saving money — it's about building a sustainable AI infrastructure. The tokens you don't send are the cheapest tokens of all.