Prune vs cache-first AI gateways
If the pitch starts and ends with hit rate, you are looking at a different category.
Quick answer
Cache-first AI gateways and many observability proxies optimize for repeating prompts. Prune is a managed AI gateway control plane: unique-path savings on first-time traffic, Shield before the provider, and a receipt on every response. Cache savings show up separately on the receipt, on top of that.
The category confusion
Several products market themselves as “AI gateways” while leading with semantic cache, hit-rate dashboards, or request analytics. Those tools can be useful. They answer a different question: what happened to my traffic? or can I reuse this prompt?
Prune answers: for every model call - including ones that never repeat - is the spend lower, is risky data blocked, and can I prove it?
Side-by-side
| Dimension | Cache / observability gateway | Prune |
|---|---|---|
| Hero KPI | Cache hit rate | Unique-path $ saved + Shield actions + receipt |
| Unique traffic | Often little or no savings story | Primary path: routing, caps, compression. In our 26 Sep lab, new questions cost about 94% less than sending everything to gpt-4o, and repeats were free. Lab result, not a customer bill. |
| Security | Often analytics-first; policy varies | Shield defaults: vault, PII/secrets, spend caps before upstream |
| Proof | Dashboards / traces | prune_metadata on every response; optional signed verify at /verify |
| When prompts repeat | Core value | Extra upside on the same receipt |
When a cache gateway is enough
- Your traffic is mostly identical FAQ / template prompts
- You mainly need tracing and cost dashboards
- You already run policy elsewhere and only want reuse
When Prune is the better fit
- Most requests are unique (agents, personalization, RAG)
- You need spend caps and leak prevention before the provider
- Finance or security needs a per-call receipt, not only a chart
- You want one managed layer - not a cache product plus a separate firewall
