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 is measured separately as upside — not the product thesis.
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 (≥25% median cost reduction on unique (non-cache) traffic in the published benchmark suite) |
| 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
Related
- How Prune works
- Prune vs LiteLLM (self-hosted vs managed)
- Security model