engineering · 1 min read
Prompt Caching at the Gateway: 80% Cost Cuts on Repetitive Workloads
Repetitive prompts are 60-90% of most production LLM workloads. Gateway-level caching turns that into immediate, measurable savings.
BABhogar AI TeamProduct & Engineering
Most production LLM workloads have heavy repetition: same system prompt, same few-shot examples, same retrieved context. Caching at the gateway turns that repetition into 50-80% cost reduction.
Why it matters
Provider-level prompt caching is great where supported but inconsistent across providers. Gateway-level caching gives you the same benefit uniformly and adds semantic caching on top.
How Bhogar AI approaches it
Bhogar AI gateway supports exact-match prompt caching, provider-native cache passthrough and optional semantic caching. Cache scope is per tenant; TTL and invalidation are configurable.
- Exact-match prompt caching with per-tenant scope
- Provider-native cache passthrough where supported
- Optional semantic caching with similarity threshold
- Configurable TTL and invalidation
- Cache hit-rate dashboards
What you get
Customers enabling caching commonly cut LLM spend 30-80% on cache-friendly workloads with negligible quality impact.