Designing an LLM Gateway: Multi-Provider Routing and Fallback Strategies

Relying on OpenAI alone for a production LLM service is risky. When OpenAI went down in November 2023, countless services went down with it. An LLM gateway puts multiple providers behind a single interface and centrally manages routing, fallback, and cost optimization.

What an LLM Gateway Does

CODE

Client
    ↓
LLM Gateway (routing / fallback / caching / logging)
    ↓        ↓        ↓
  OpenAI  Anthropic  Google

Routing: Choose which model to send each request to
Fallback: Automatically switch to another provider on failure
Rate limiting: Manage per-provider quotas
Cost tracking: Unified cost monitoring

Getting Started Quickly with LiteLLM

LiteLLM unifies 100+ LLMs behind an OpenAI-compatible interface.

Python

from litellm import completion

# 모델명만 바꾸면 모든 프로바이더 동일하게 사용
def llm_call(model, messages):
    response = completion(model=model, messages=messages)
    return response.choices[0].message.content

llm_call("gpt-4o",              [{"role": "user", "content": "안녕"}])
llm_call("claude-sonnet-4-6",   [{"role": "user", "content": "안녕"}])
llm_call("gemini/gemini-1.5-pro",[{"role": "user", "content": "안녕"}])

Implementing a Fallback Chain

Python

from litellm import completion

FALLBACK_CHAIN = [
    "gpt-4o",
    "claude-sonnet-4-6",
    "gemini/gemini-1.5-pro"
]

def resilient_llm_call(messages, preferred_model=None):
    chain = ([preferred_model] + FALLBACK_CHAIN) if preferred_model else FALLBACK_CHAIN
    chain = list(dict.fromkeys(chain))

    last_error = None
    for model in chain:
        try:
            response = completion(model=model, messages=messages, timeout=10, num_retries=1)
            return {
                "content":      response.choices[0].message.content,
                "model_used":   model,
                "was_fallback": model != chain[0]
            }
        except Exception as e:
            last_error = e
            print(f"[WARN] {model} 실패, 다음 모델로 시도")

    raise RuntimeError(f"모든 모델 실패: {last_error}")

Cost-Based Routing

Python

ROUTING_TABLE = {
    "low":    {"primary": "gpt-4o-mini",       "fallback": "claude-haiku-4-5-20251001"},
    "medium": {"primary": "claude-sonnet-4-6", "fallback": "gpt-4o"},
    "high":   {"primary": "claude-opus-4-7",   "fallback": "gpt-4o"},
}

def classify_task(messages):
    user_text = " ".join(m["content"] for m in messages if m["role"] == "user")
    if any(kw in user_text for kw in ["분석", "설계", "아키텍처", "전략"]):
        return "high"
    if any(kw in user_text for kw in ["번역", "요약", "분류", "추출"]):
        return "low"
    return "medium"

def smart_route(messages):
    complexity = classify_task(messages)
    route = ROUTING_TABLE[complexity]
    return resilient_llm_call(messages, preferred_model=route["primary"])

Deploying a LiteLLM Proxy Server

This lets the entire team share a single endpoint.

YAML

# litellm_config.yaml
model_list:
  - model_name: gpt-4o
    litellm_params:
      model: gpt-4o
      api_key: os.environ/OPENAI_API_KEY

  - model_name: claude-sonnet
    litellm_params:
      model: claude-sonnet-4-6
      api_key: os.environ/ANTHROPIC_API_KEY

router_settings:
  routing_strategy: cost-based-routing
  fallbacks:
    - {"gpt-4o": ["claude-sonnet", "gemini-pro"]}

general_settings:
  master_key: sk-my-master-key
  database_url: os.environ/DATABASE_URL

Bash

litellm --config litellm_config.yaml --port 4000

From then on, anyone on the team can use http://localhost:4000 as if it were an OpenAI endpoint. Key management, cost tracking, and fallbacks are all centralized.

확인 정보

#LLM #게이트웨이 #아키텍처 #라우팅 #폴백 #LiteLLM

✦ ✦ ✦

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편집 책임 · Nodelog 기술 편집팀·발행 · May 20, 2026·업데이트 · Sep 17, 2026

Designing an LLM Gateway: Multi-Provider Routing and Fallback Strategies