Layer 4 · Inference gateway

LiteLLM

A gateway presenting one consistent interface across many hosted model providers and your own self-hosted models.

Desk Research · September 2026

Evaluated from public documentation, architecture material and source code. No vendor contact.

What it solves

Puts routing, API keys, spend limits, fallbacks and logging in one place, so changing model or provider does not mean touching every call site in the application.

Who it suits

Teams exposed to a single model provider, teams that need per-team spend controls, and anyone moving between hosted APIs and self-hosted models.

What to watch out for

A gateway is on the critical path of every request. Its availability and its added latency are production concerns from day one, and it needs to be sized and monitored accordingly.

Every product here gets one of these. A recommendation without a trade-off is not a recommendation.

Problems this comes up for

Our inference costs rise faster than our usage It works in the pilot but not in production We cannot see where our AI spend actually goes We are locked into one model provider

Worth comparing against

vLLM

L4 · Inference serving engine

Desk Research

An open-source engine for serving large language models, with memory handling built for high concurrent throughput.

Fit: Teams self-hosting an open-weight model for production inference.
Catch: Serves models well; does not manage your fleet, routing or tenancy.

Read the notes

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