PART 6 — Caching: The Economics of AI at Scale
Caching is not a performance trick — it is what makes production AI systems economically viable.
The Four-Layer Stack
Every production AI request passes through up to four caching layers:
1
Semantic Cache (Vector DB)Meaning-level match. Fastest when similar queries repeat. Must include context window in cache key.
↓ cache miss
2
Exact Prompt Cache (Redis / API-level)Byte-exact prefix match. ~10% of input token price on hit. Free via Claude/Gemini API — just structure your prompt right.
↓ cache miss
3
LLM InferenceActual model call. Most expensive. Unavoidable on a cold miss.
↓ inside the inference server (transparent to you)
4
KV Cache (GPU HBM → CPU RAM → S3)Token-level prefix reuse. vLLM paged attention for single node. LMCache + tiered storage for multi-node.
Why This Matters
"Training made the headlines. Inference pays the power bill." — NetApp/HPCWire 2026
Each layer cuts cost at a different level:
- KV Cache (Layer 4): GPU compute you did not pay for twice
- Prompt Cache (Layer 2): ~10% of input token price on cache hits
- Semantic Cache (Layer 1): Full LLM call avoided for similar queries
- Together: 80-90% cost reduction on repeated workloads at scale
Which Layer to Implement First
| Your situation | Start here |
|---|---|
| Single inference server, running vLLM | Layer 4 (KV) — it's automatic |
| Multiple inference nodes behind a load balancer | Layer 4 with LMCache |
| Long system prompts sent on every request (Claude/Gemini) | Layer 2 (Prompt caching) |
| FAQ-style queries with many users asking similar questions | Layer 1 (Semantic cache) |
The Full Guide
- KV Cache — GPU-Level Inference Caching
- Prompt Caching — Provider-Level Caching (Claude + Gemini)
- Semantic Caching — Meaning-Level Cache + LiteLLM Stack
Sources
- Training made the headlines. Inference pays the power bill. — NetApp/HPCWire 2026
Seeing caching numbers from production? Add them here — real cost data is rare and valuable.