Comparison
LLM Gateway vs Prompt Caching
LLM Gateway and Prompt Caching are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for LLM Gateway
When you use multiple providers, need per-tenant cost attribution, or want centralized observability/PII policies.
LiteLLM proxy fronting OpenAI, Anthropic, and Bedrock with unified billing dashboards and automatic retry-on-fallback.
When you would reach for Prompt Caching
Whenever a prefix is reused across calls and exceeds ~1K tokens. The break-even point is low; the upside is large.
A long-context RAG app caches the system prompt + few-shot examples; per-call latency drops from 6s to 1.5s, cost drops ~80%.
Frequently asked
What is the difference between LLM Gateway and Prompt Caching?
LLM Gateway: An LLM gateway is a proxy layer that sits between application code and one or more LLM providers — handling auth, rate-limit retries, cost tracking, observability, prompt caching, model routing, and PII redaction. Prompt Caching: Prompt caching stores the KV-cache state of a long prefix (system prompt, large document, tool definitions) so subsequent calls that reuse it skip the prefill compute — cutting TTFT and cost by 50-90%.
When should I use LLM Gateway vs Prompt Caching?
When you use multiple providers, need per-tenant cost attribution, or want centralized observability/PII policies. Whenever a prefix is reused across calls and exceeds ~1K tokens. The break-even point is low; the upside is large.
Are LLM Gateway and Prompt Caching the same thing?
No. LLM Gateway is infrastructure; Prompt Caching is inference. They are related but address different parts of the AI stack.