A Developer's Checklist for Multi-Model LLM Routing

Source: Dev.to

I wrote an intro to AI API gateways on Medium last day. This is the practical follow‑up: the checklist I wish I had before I built AllToken.

Built AllToken for all developers. Many models, one decision.

But that decision only makes sense if your routing layer doesn’t become a nightmare to maintain. After managing five different provider SDKs in production — and watching our internal abstraction layer grow into its own microservice — I realized there’s a standard checklist every team should run before they commit to a multi‑model stack.

Here’s mine

1. One Schema to Rule Them All

If your application code branches on if provider == "openai" you’ve already lost. Every new provider becomes a refactor.

The check: Your app should send one request shape regardless of the target model.

At AllToken we expose an OpenAI‑compatible endpoint, but the principle matters more than the vendor:

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.ALLTOKEN_API_KEY,
  baseURL: 'https://api.alltoken.ai/v1',
});

// Same code, any provider underneath
const completion = await client.chat.completions.create({
  model: 'minimax-m2.7',
  messages: [{ role: 'user', content: 'Hello!' }],
});

Red flag: If adding a new provider requires touching more than one line (the model string), your abstraction is leaking.

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2. Failover That Doesn’t Wake Your On‑Call

Provider outages are not edge cases. They’re Tuesday.

The check: When your primary provider returns a 500 or times out, does your app retry automatically? Or does it bubble the error to the user?

A production gateway should handle this without your application knowing it happened. That means:

• Health checks on each provider
• Circuit‑breaking logic when a provider is clearly degraded
• Automatic fallback to a secondary option

curl https://api.alltoken.ai/v1/chat/completions \
  -H "Authorization: Bearer $ALLTOKEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m2.7",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Red flag: Your failover logic lives in a 200‑line try/catch block that only you understand.

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3. Cost Routing, Not Just Cost Tracking

Tracking spend after the fact is accounting. Routing by cost in real time is engineering.

The check: Can you send a cheap query to a cheap model and a complex query to a strong model — without changing application code?

Most teams end up with an informal tiering system, whether they plan for it or not:

“How much did we spend on OpenAI last month?” – finance question.
“How much did User 8473 spend on embedding requests in the last hour?” – engineering question.

The check: Can you attribute cost, latency, and token usage down to the individual request or user?

At minimum, a production gateway should give you:

• Request‑ID propagation across the stack
• Per‑user or per‑feature cost attribution
• Provider‑specific error tracking

If your gateway doesn’t expose this, you’re flying blind at scale.

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6. Rate Limiting at the Gateway, Not the Provider

Managing rate limits across five different dashboards is not a job. It’s a punishment.

The check: Do you have one throttle layer that protects both your app and your wallet?

A proper gateway should handle:

• Global rate limits (protect your budget)
• Per‑user rate limits (prevent abuse)
• Per‑provider rate limits (respect upstream quotas)

One API key. One set of rules. Not five different UIs with different semantics.

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7. An Escape Hatch from Vendor Lock‑In

This is the one everyone claims to care about and nobody tests.

The check: If you needed to swap your primary provider next week, how many files would you touch?

With a proper gateway: Ideally zero – just change a config (maybe a model string).
Without one: Every file that touches an LLM, which for us was most of the backend.

What We Evaluated

Before we built AllToken, we looked at what was already out there. OpenRouter has an incredible model catalog and is great for experimentation, but…

(The rest of the original article continues here.)

Why a Custom Gateway Was Needed

Other teams roll their own with Nginx and Lua scripts. Some just accept the SDK sprawl.

None of them handled production failover, cost routing, and unified billing the way we needed. So we built it.

Checklist for Multi‑Model Production Deployments

If you’re running more than one model in production, you’ll eventually need to build or buy a gateway. Run this checklist first so you know exactly what you’re solving for.

• Failover handling – automatic switchover when a model instance goes down.
• Cost‑aware routing – direct traffic to the cheapest viable provider.
• Unified billing – aggregate usage across providers into a single invoice.
• Observability – centralized logs, metrics, and alerts for all models.
• Security & compliance – consistent authentication, encryption, and data‑handling policies.
• Scalability – seamless horizontal scaling without manual reconfiguration.
• Version management – easy rollout and rollback of model updates.

What’s missing from this checklist? If you’ve run multi‑model LLMs in production, you’ve probably hit edge cases I haven’t. Drop them in the comments—I read every one.

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I built alltoken.ai because I got tired of writing the same routing logic for every new project. Many models. One decision. Smart routing, transparent pricing, no platform fees.