What is an LLM gateway?
LLM gateway — a single API endpoint that proxies large-language-model requests to multiple underlying LLM providers (OpenAI, Anthropic, Google, Mistral, Groq, DeepSeek, xAI, Amazon Bedrock), normalizing the wire format and exposing per-request cost, latency, and routing decisions in response headers.
The shape of the problem
Production LLM applications rarely commit to a single provider. Cost varies by 10× to 100× across vendors for similar quality. Provider rate-limits and outages happen weekly. Different prompts have different "best model" answers — a reasoning-heavy step and a one-line extraction step shouldn't run through the same flagship model. And the OpenAI SDK shape is the de facto industry standard, so any non-OpenAI provider integrates via its own SDK adapter.
An LLM gateway collapses these problems into one surface. Send one OpenAI-format request, get one OpenAI-format response, and the gateway handles provider selection, fallback, cost accounting, and observability on your behalf.
What an LLM gateway typically does
- Wire-format normalization. Accept the OpenAI-compatible
/v1/chat/completionsrequest shape, translate to each provider's native format, translate the response back. Streaming SSE works identically across providers. - Provider authentication. One API key to the gateway. The gateway holds the provider keys (or your BYOK keys). Caller never manages individual provider credentials.
- Routing. Pick which provider and which model to dispatch each request to. Can be rule-based (task classification), ML-based (a learned router), or caller-driven (direct pin via model name).
- Fallback cascading. When the primary provider returns a 5xx, 429, or times out, fall through to the next provider in a chain. Caller sees one successful response; the cascade is reported in a header.
- Per-request observability. Cost in USD, the resolved
provider/model, the fallback chain if one fired, the router version, the rate-limit headroom. All in response headers, no separate accounting plane. - Caller-side budgets. Max latency per request, cost-quality preference dial, region constraints, provider allow/deny filters.
- Billing. Pass-through token rates or per-request margin, top-up fees, prepaid balances. The gateway handles the unified bill across N providers.
How GammaInfra implements an LLM gateway
GammaInfra is a managed LLM gateway. Send any OpenAI-format chat completion request to https://api.gammainfra.com/v1/chat/completions and the gateway:
- Authenticates the
sk-gammainfra-*API key and checks the credit balance. - Classifies the prompt into one of 8 task labels (reasoning, code, creative, rewrite, chat, extraction, summarize, translation) using a MiniLM-based classifier.
- Picks the best-fit endpoint within the task's fallback chain using live p50 latency (refreshed every 30 seconds, 5-minute window) and the caller's
X-GammaInfra-PreferenceorX-GammaInfra-Cost-Qualityheader. - Dispatches to the upstream provider. If it fails or times out (or exceeds a
X-GammaInfra-Max-Latency-Msbudget), cascades to the next chain entry. - Returns the response with
X-GammaInfra-Cost-USD,X-GammaInfra-Endpoint,X-GammaInfra-Fallback-Chain, andX-GammaInfra-Router-Versionheaders.
The wire format is OpenAI-compatible. base_url = "https://api.gammainfra.com/v1" in your existing OpenAI SDK code is the entire integration.
Common questions
What is the difference between an LLM gateway and an LLM proxy?
Why use an LLM gateway instead of calling each provider directly?
Is an LLM gateway the same as a model router?
Does an LLM gateway add latency?
What features should an LLM gateway expose?
Try the gateway
$3 free trial credit on signup, $10 minimum top-up. Pass-through provider rates plus 3% top-up fee during the launch window (5% after 2026-06-23).
Last updated 2026-05-15.