OpenRouter Reference Architecture

Overview

OpenRouter serves as a unified LLM gateway, abstracting provider complexity. A production architecture wraps it with caching, rate limiting, cost controls, observability, and async processing. This skill provides three reference architectures: simple (single service), standard (microservice), and enterprise (event-driven).

Architecture 1: Simple (Single Service)

┌─────────────┐     ┌──────────────────────────┐     ┌──────────────┐
│  Your App   │────▶│  OpenRouter Client        │────▶│  OpenRouter  │
│             │     │  - Retry (SDK built-in)   │     │  /api/v1     │
│             │◀────│  - Cost tracking          │◀────│              │
│             │     │  - Structured logging     │     └──────────────┘
└─────────────┘     └──────────────────────────┘

import os, logging
from openai import OpenAI

log = logging.getLogger("llm")

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    max_retries=3,
    timeout=30.0,
    default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)

def complete(prompt, model="openai/gpt-4o-mini", **kwargs):
    kwargs.setdefault("max_tokens", 1024)
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        **kwargs,
    )
    log.info(f"[{response.model}] {response.usage.prompt_tokens}+{response.usage.completion_tokens} tokens")
    return response.choices[0].message.content

Architecture 2: Standard (Microservice)

┌─────────────┐     ┌─────────────────────┐     ┌──────────────┐
│  API Gateway│────▶│  AI Service          │────▶│  OpenRouter  │
│  (auth,     │     │  ┌─────────────┐    │     │  /api/v1     │
│   rate-limit│     │  │ Router      │    │     └──────────────┘
│   logging)  │     │  │ (task→model)│    │
└─────────────┘     │  └─────────────┘    │
                    │  ┌─────────────┐    │
                    │  │ Cache       │◀──▶│── Redis
                    │  │ (TTL-based) │    │
                    │  └─────────────┘    │
                    │  ┌─────────────┐    │
                    │  │ Budget      │◀──▶│── SQLite/Postgres
                    │  │ Enforcer    │    │
                    │  └─────────────┘    │
                    └─────────────────────┘

from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel

app = FastAPI()

class CompletionRequest(BaseModel):
    prompt: str
    task_type: str = "general"  # classification, code, analysis, etc.
    max_tokens: int = 1024
    user_id: str = "anonymous"

ROUTING_TABLE = {
    "classification": "openai/gpt-4o-mini",
    "code": "anthropic/claude-3.5-sonnet",
    "analysis": "anthropic/claude-3.5-sonnet",
    "general": "openai/gpt-4o-mini",
    "budget": "meta-llama/llama-3.1-8b-instruct",
}

@app.post("/v1/complete")
async def complete(req: CompletionRequest):
    model = ROUTING_TABLE.get(req.task_type, "openai/gpt-4o-mini")

    # Check cache first (for deterministic requests)
    cached = cache.get(model, req.prompt)
    if cached:
        return {"content": cached, "cached": True}

    # Check budget
    budget.check(req.user_id, model, estimate_tokens(req.prompt), req.max_tokens)

    # Call OpenRouter
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": req.prompt}],
        max_tokens=req.max_tokens,
        extra_body={
            "models": [model, "openai/gpt-4o-mini"],  # Fallback
            "route": "fallback",
        },
    )

    # Record cost and cache
    budget.record(req.user_id, response.id)
    cache.set(model, req.prompt, response.choices[0].message.content)

    return {
        "content": response.choices[0].message.content,
        "model": response.model,
        "tokens": response.usage.prompt_tokens + response.usage.completion_tokens,
    }

Architecture 3: Enterprise (Event-Driven)

┌──────────┐    ┌───────────┐    ┌──────────────┐    ┌──────────────┐
│  API     │───▶│  Queue    │───▶│  Workers     │───▶│  OpenRouter  │
│  Gateway │    │  (Redis/  │    │  (auto-scale) │    │  /api/v1     │
└──────────┘    │  SQS)     │    │  ┌──────────┐│    └──────────────┘
                └───────────┘    │  │ Router   ││
                     │           │  │ Cache    ││
                     ▼           │  │ Budget   ││
                ┌───────────┐    │  │ Audit    ││
                │  Results  │◀───│  └──────────┘│
                │  Store    │    └──────────────┘
                └───────────┘
                     │
                ┌───────────┐    ┌──────────────┐
                │  Metrics  │───▶│  Dashboard   │
                │  (OTEL)   │    │  Alerts      │
                └───────────┘    └──────────────┘

# Worker that processes queued AI requests
import json, redis

r = redis.Redis()

def worker_loop():
    """Process AI requests from the queue."""
    while True:
        _, raw = r.brpop("ai:requests")
        request = json.loads(raw)

        try:
            response = client.chat.completions.create(
                model=request["model"],
                messages=request["messages"],
                max_tokens=request.get("max_tokens", 1024),
                extra_body={
                    "models": [request["model"], "openai/gpt-4o-mini"],
                    "route": "fallback",
                },
            )
            result = {
                "id": request["id"],
                "content": response.choices[0].message.content,
                "model": response.model,
                "status": "complete",
            }
        except Exception as e:
            result = {"id": request["id"], "error": str(e), "status": "failed"}

        r.lpush(f"ai:results:{request['id']}", json.dumps(result))
        r.expire(f"ai:results:{request['id']}", 3600)

Choosing an Architecture

Factor	Simple	Standard	Enterprise
Team size	1-3	3-10	10+
Requests/day	<1K	1K-100K	100K+
Latency needs	Tolerant	Low	Mixed (sync+async)
Budget tracking	Basic	Per-user	Per-user + department
Failure handling	SDK retries	Fallback chain	Queue + retry + DLQ
Observability	Logging	Metrics + logging	Full OTEL tracing

Error Handling

Error	Cause	Fix
Single point of failure	No redundancy in AI service	Deploy 2+ instances behind load balancer
Queue backlog	Worker throughput < incoming rate	Auto-scale workers; implement backpressure
Cache stampede	Many requests for same uncached key	Use cache locking or singleflight pattern
Budget bypass	Direct calls skipping middleware	All calls must go through the AI service

Enterprise Considerations

• Start with Architecture 1 and evolve to 2/3 as scale demands
• Use the queue-based pattern for any request that can tolerate >1s latency (cost reports, batch processing)
• OpenTelemetry traces should span from API gateway through AI service to OpenRouter
• Implement dead letter queues (DLQ) for failed requests that exhaust all retries
• Run separate worker pools for different priority levels (real-time vs batch)
• All architectures should share the same OpenRouter client wrapper for consistent logging and cost tracking

References

• Examples | Errors
• API Reference | Model Routing