Comparison
LLM Gateway vs LLM Observability
LLM Gateway and LLM Observability 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 LLM Observability
From day one of any production LLM application. The cost of bolting it on later vastly exceeds wiring it up at the start.
A support bot logs every (user message, retrieved docs, prompt, response, faithfulness score) tuple to Arize Phoenix; engineers replay bad sessions there.
Frequently asked
What is the difference between LLM Gateway and LLM Observability?
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. LLM Observability: LLM observability is the practice of capturing, analyzing, and acting on every LLM call in a production system — inputs, outputs, latencies, costs, errors, and quality scores — so you can debug regressions and improve quality over time.
When should I use LLM Gateway vs LLM Observability?
When you use multiple providers, need per-tenant cost attribution, or want centralized observability/PII policies. From day one of any production LLM application. The cost of bolting it on later vastly exceeds wiring it up at the start.
Are LLM Gateway and LLM Observability the same thing?
No. LLM Gateway is infrastructure; LLM Observability is infrastructure. They are related but address different parts of the AI stack.