Centralizing LLM access through a gateway reduces cost, improves reliability, and gives platform teams control over model selection, rate limits, and observability.
Table of Contents
Why a Gateway
Without a gateway, every team embeds API keys, duplicates retry logic, and creates uncontrolled spend. A gateway becomes the single control plane.
Core Components
// Simplified gateway request flow
async function handleRequest(req: LLMRequest) {
const route = router.select(req.model, req.priority);
const cached = await cache.get(req.hash);
if (cached) return cached;
const response = await provider.call(route, req);
await cache.set(req.hash, response, ttl);
await metrics.record(req, response);
return response;
}
Routing Strategies
- Cost-optimized: route simple queries to smaller models
- Latency-optimized: prefer fastest available provider
- Quality-optimized: use flagship models for complex tasks
Caching
Semantic caching (embedding similarity) can reduce costs 30–60% for repetitive enterprise queries.
Cost Controls
Implement per-team budgets, token quotas, and automatic model downgrade when limits approach.
Summary
An LLM gateway is infrastructure—not a nice-to-have—for any organization running AI at scale.
