Leanroute

Posted on Aug 14 • Originally published at leanroute.dev

Stop Building Your Own LLM Router

#ai #llm #apigateway #programming

TL;DR

Model routing looks simple until you have to maintain it. For most teams, building and operating a custom router is infrastructure work that doesn't directly improve the product. Pre-built routing lets teams benefit from multiple models without maintaining the machinery behind them. AI infrastructure should disappear behind a simple interface so developers can focus on building the actual product.

It Starts With One Model

You start here:

Application
     │
     ▼
   GPT

Then you need a fallback.

Then a cheaper model.

Then Claude.

Then Gemini.

Then you start thinking about latency and model quality.

Eventually:

                 Router
              /    |    \
            GPT  Claude  Gemini

And now you've built a router.

What started as a simple application decision has quietly become another piece of infrastructure your team has to operate.

Routing Isn't Just Picking a Model

A production router eventually needs to consider:

Cost
Latency
Model capability
Context limits
Provider availability
Rate limits
Errors and retries
Model quality
Regional requirements

And these variables keep changing.

A model that is the best choice today might not be the best choice next month.

Providers change pricing.

New models appear.

Existing models improve.

Availability changes.

Traffic patterns change.

Your router has to keep up with all of it.

Your Team Has Better Things to Build

There's an important distinction between building an AI product and building infrastructure for AI products.

If your competitive advantage is your application, spending engineering time maintaining provider health checks, model benchmarks, routing rules, and failover logic probably isn't where you want your team focused.

You want to build:

Your Product
     │
     ▼
 AI Gateway
     │
     ▼
Best Model for the Job

The infrastructure handles the complexity.

Your application stays focused on the product.

When Should You Build Your Own?

There are legitimate reasons to build custom routing.

If you're operating at enormous scale, have highly specialized workloads, proprietary evaluation systems, or unusual latency and compliance requirements, custom routing can make sense.

But for most application teams, routing is infrastructure, not the product.

You probably don't need another internal system to maintain.

You need a reliable way to access the models your application needs.

Let Infrastructure Disappear

The best infrastructure is the infrastructure developers don't have to think about.

You shouldn't need to constantly ask:

Which provider is healthy?

Which model is cheapest?

Should I retry somewhere else?

Did a new model just become better?

Your application should express what it needs.

The infrastructure should handle the rest.

That's what a good AI Gateway should do.

The Bigger Picture

This is also why we think AI Gateways are evolving beyond model routing.

AI applications need models.

They also need tools.

They shouldn't need separate infrastructure for both.

             Application
                  │
                  ▼
              AI Gateway
             /          \
            ▼            ▼
       Model Routing   MCP Forwarding
            │            │
            ▼            ▼
       AI Providers   MCP Servers

The goal isn't to add another layer to your stack.

It's to remove the layers you shouldn't have to build yourself.

That's the idea behind Leanroute:

One Gateway for Models and Tools.

Because ultimately:

AI infrastructure should disappear behind a simple interface so developers can focus on building the actual product.

Key Takeaways

Building a model router is easy. Maintaining one is not.
Models, providers, pricing, latency, and availability constantly change.
Most application teams should consume routing infrastructure rather than build it.
Developers should focus on their product, not provider infrastructure.
AI Gateways can provide a unified layer for both models and tools.
The goal is simple: AI infrastructure should disappear.