Let me guess. You want AI solutions that cost less, perform faster, and are more reliable?
Then you should probably be using an AI model router.
Probably is doing some work there. A router is not a magic setting that makes every model cheaper and smarter. But if your application sends every request to the same model, you may be paying for intelligence the task does not need … or asking a model to do a job it is not particularly good at.
I tried a Q&A format for this on LinkedIn. Let’s keep it here, because the questions are the ones that actually matter when you move from an interesting architecture diagram to something the business uses.
What is an AI model router?
Think of it as an air traffic controller for AI. A request comes in. The router decides which model should handle it.
Some routers use explicit rules. If the task is classification, send it here. If it requires more complex analysis, send it there. If the data has restrictions, narrow the list of eligible destinations before anything leaves your environment.
Others use a model or a learned scoring system to help choose. You can also combine the approaches: enforce the non-negotiable rules first, then select among the models that are allowed to do the job.
The point is to use enough intelligence for the task without automatically buying the most expensive seat on every flight.
When would I use one?
When you have enough AI usage, or enough different kinds of work, that sending everything to one model stops making sense.
Take a customer-service application. Identifying whether a message is about billing, delivery, or a cancellation may be a good job for a small, fast model. Working through an unusual dispute with a long conversation history may need something more capable. A request containing restricted information may have to stay with a model approved for that data.
Same application. Different jobs. Different models.
Without a shared routing layer, those decisions tend to get built into each application. Now changing a provider or revising a policy means finding every place somebody hardcoded that choice. Centralizing it gives you one place to manage the decision, while still letting applications specify what they need.
If you have one low-volume workflow and one model that handles it well, start there. A routing layer should earn its place. It adds its own latency, cost, and operational work.
Does routing automatically make AI more reliable?
No. Sending a request somewhere else is not the same as knowing that the answer is correct.
A gateway can retry a failed call or switch providers during an outage. That helps availability. A model-selection system can choose a model that performs better on a particular task. That can help answer quality. Those are different problems, and it is worth knowing which one you are solving.
You still need checks on the result. Did it return the required fields? Did it follow the business rules? Did it use the approved information? If it cannot produce a valid result, what happens next?
And a fallback must obey the original restrictions. If sensitive data can only go to two approved models, an outage does not suddenly make a third provider acceptable.
Should I build or buy?
There is no clean answer without understanding your requirements.
Buying generally means adopting someone else’s gateway or routing service. That can save you work on integrations, operational tooling, and provider changes. But you need to understand which decisions you control, where requests travel, what gets logged, and how the service handles failures.
Building means owning the routing architecture and decision logic. You may still use AWS, Azure, Google Cloud, model-provider APIs, or existing gateway components. Owning the decision does not mean writing every piece of infrastructure from scratch.
The useful question is: which part is specific to your business, and which part is plumbing?
You might buy the plumbing and keep your task classifications, evaluation data, and selection rules under your own control. That can be a better answer than treating build versus buy as an all-or-nothing decision.
What can I buy today?
There are several kinds of products to evaluate. OpenRouter, Not Diamond, and Martian are names to investigate for model selection and routing. Portkey, Cloudflare, and Vercel are also worth looking at for gateway-style capabilities, routing rules, and fallbacks.
Do not treat that as a list of interchangeable products. Check the current offering against the job you need done. A service that gives you access to multiple models is not necessarily deciding which model will produce the best result for your specific workload.
Ask whether you can define allowed destinations, inspect why a route was chosen, set spending limits, and export the evidence you need to evaluate performance. Then try it on your actual work, not just a few prompts that make a demo look good.
Why build your own routing logic?
Because at scale, the knowledge behind routing can become proprietary.
Over time, you learn which models handle your documents, your terminology, your exceptions, and your business processes well. You learn where a smaller model is sufficient and where trying to save a little money creates more rework.
That knowledge is more valuable than a generic leaderboard. It describes what works inside your company.
To make it useful, capture the task type, model and version, latency, cost, validation outcome, and whether a person had to intervene. Compare the cost of completing the job successfully, not just the cost of the first model call. A cheaper route that needs three retries and a human cleanup may not be cheaper at all.
Then revisit the decisions. Models change. Your workload changes. A good route today is not a permanent fact.
Where should I start?
Pick one workflow with enough repeatable work to compare results. Define what a valid answer looks like. Test a small set of eligible models on representative requests, including the awkward ones. Establish a baseline before introducing routing, then check whether the router actually improves cost, speed, or successful completion.
You do not need an elaborate AI traffic-control tower on day one. You need evidence that a different route produces a better outcome.
And if you want help figuring out which parts to build, which to buy, and how to make those decisions measurable … ALKU Intelligence, of course. There are others, but are there really?
The serious point underneath the joke: model choice should become a managed business capability, not a decision copied into every application and forgotten.
