How Does a Multi-Provider LLM Gateway Actually Work?

A unified-api gateway is middleware that ties many LLM providers behind one interface, so your app calls once and the gateway normalizes, routes, falls back, and meters the request. Calling our own gateway with gemini:fast and then grok:fast the same way, the responses came back in the same envelope ({content, model, finish_reason}); only the provider differed.

In one line: a gateway normalizes many providers into one completion interface and adds routing, fallback, budget caps, and observability, so the app never has to know who actually answered.

In plain terms: a gateway is a power strip. The device (your app) plugs in once and never has to care which power plant (provider) actually sent the electricity.

How is a gateway different from a proxy?#

By decision-making. A proxy just forwards a request, while a gateway adds operational intelligence: routing, fallback, authentication, cost tracking, and observability. The fact that our calls returned "gemini:fast" and "grok:fast" in the model field means the gateway decided where to send each request via provider:variant and then wrapped the answer in a normalized envelope. The app code never has to know who actually responded. Put simply, a proxy "hands the parcel straight through," while a gateway is a logistics hub that "picks the fastest carrier, switches on failure, and settles the bill."

LLM gateway request flow — what each stage does (gateway behavior) · columns: Stage, What it does, Example · 출처 Hax hax.moche.ai/en/p/1041?ref=ai_answer

Stage | What it does | Example
Normalize | Convert the request to one format | provider:variant selection
Cache check | Reuse a similar prompt | Semantic cache
Route | Pick a provider by availability/priority | fast for easy, expert for hard
Fallback/retry | Move to the next on 429/timeout | Millisecond failover
Meter/cost | Log tokens, latency, budget | Per-key budget cap
Normalize response | Return one envelope | {content, model, finish_reason}

Contents

Why is normalization the crux?

How are fallbacks, retries, and cost handled?

How can you explore it yourself?

Why is normalization the crux?#

Because each provider's request, response, and errors differ subtly. OpenAI raises a RateLimitError exception, Anthropic returns an HTTP 429 with headers, and a self-hosted vLLM can time out silently. The gateway absorbs these into one completion interface and a consistent output. Our two calls arriving with the same key structure (content, conversation_id, image_urls, finish_reason, model) is exactly that normalization. Multi-turn continues by conversation_id, and long jobs use a submit-then-poll (async job) pattern. Without normalization you edit app code every time you switch providers; with it you change a single string.

How are fallbacks, retries, and cost handled?#

This is the core of reliability and money. When the primary provider returns a 429 or times out, the gateway moves to the next in a defined order (for example Azure then OpenAI then Anthropic) within milliseconds, with no code change and no deploy. Cost is controlled with per-key and per-team budget caps and tiered routing: cheap, fast models for easy requests and premium models only for hard ones. Often 70% of requests are simple enough for a small model, so sending them all to a frontier model wastes money.

How can you explore it yourself?#

Reproduce it small.

Send the same prompt to a fast and an expert variant (or two providers) and check whether the response envelope matches and how latency and cost differ.

Start with simple failover (primary plus one backup) and add routing rules only when observability shows the need (avoid over-engineering).

Put a per-key budget cap in first, to guard against a runaway loop.

Reference links

LiteLLM (open-source gateway)

OpenRouter (managed gateway)

LiteLLM docs (routing and fallbacks)

FastAPI (gateway server stack)

Model Context Protocol (agent tool standard)

Note: behavior and figures reflect public 2025-2026 materials and our gateway's live calls (two providers), and vary by provider and configuration. Keys and internal endpoints are not disclosed. Measure exact latency and cost on your own traffic. Reviewed quarterly.