Comparison
Inference vs LLM Gateway
Inference and LLM Gateway are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
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.
Frequently asked
What is the difference between Inference and LLM Gateway?
Inference: Inference is what happens when you actually run a trained model on new input. For LLMs that means generating tokens one at a time, with sampling and a KV cache. 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.
When should I use Inference vs LLM Gateway?
Inference is the right concept when you are focused on inference. When you use multiple providers, need per-tenant cost attribution, or want centralized observability/PII policies.
Are Inference and LLM Gateway the same thing?
No. Inference is inference; LLM Gateway is infrastructure. They are related but address different parts of the AI stack.