AI makes it easier to build a product. It can also make that product much more expensive to run.
As The Next Web recently reported, AI companies are reassessing their dependence on rented frontier models as customer usage rises. The issue is not whether a model is impressive in a demo. It is whether the economics still work when the feature becomes part of every customer workflow.
That is why more teams are reconsidering a simple assumption: every AI task should run through the same provider.
The answer is not necessarily to host your own model. For most companies, that replaces one difficult operating problem with another. The better question is whether your application can make deliberate choices about which models it uses, where they run, and how those choices evolve as usage grows.
Model choice is part of the product architecture
Different AI tasks need different things.
A high-stakes workflow may justify a more capable model. A summarization task, classification step, extraction job, or internal copilot may not. Some teams will need access to open-weight models. Others will need several commercial providers. Most will need both over time.
When an application is hardwired to one provider, every change becomes more expensive:
- A pricing change becomes a margin problem.
- A capability gap becomes a roadmap problem.
- A provider outage or policy change becomes a customer problem.
- A new model becomes an integration project.
That is not a sustainable way to run software that depends on AI.
Open weights are one option, not the whole strategy
Open-weight models can give teams more control over cost, deployment, and customization. But they are not a universal replacement for frontier models. The hardest tasks may still require the quality, reliability, or capabilities of a leading closed model.
The right architecture gives a team room to make that decision task by task, rather than forcing one answer across the entire product.
The goal is not to find a single “best” model. The goal is to build an application that can adapt as model economics, capabilities, and customer needs change.
Give your AI workflows provider flexibility
This is where OpenRouter and LiteLLM become useful building blocks.
Both are designed around access to multiple model providers through a more consistent interface. With OpenRouter and LiteLLM available in Xano, teams can connect AI workflows to a broader provider layer instead of hardwiring every workflow to one model vendor.
That makes it easier to design around the actual needs of a workflow:
- Use a more capable model where accuracy matters most.
- Use a lower-cost option for repeatable or high-volume tasks.
- Test providers without rebuilding the application around each one.
- Keep business logic, authentication, data access, and workflow rules in one backend.
- Change your model strategy as the product and its economics evolve.
Provider flexibility is not only about lowering a token bill. It is about preserving the ability to make better operating decisions later.
Running AI software requires more than a model connection
An AI feature in production has to work inside the rest of the application.
It needs to respect who can access which data. It needs reliable APIs and business logic. It needs a way to test changes before they reach customers. It needs visibility when a workflow fails, takes too long, or behaves unexpectedly. And it needs a team to understand what changed when the product evolves.
That is why model cost is ultimately a backend problem.
The model is one part of the system. The backend is where a team decides how AI interacts with customer data, when it runs, what it can do, how it is monitored, and how it moves into production.
Build for optionality before you need it
You do not need to predict which model will be cheapest, best, or most available a year from now.
You do need an application architecture that does not make every provider change a rewrite.
For AI SaaS companies, that means treating model selection as a controllable part of the product, not a permanent dependency hidden inside application code. It means being able to experiment without losing the rules, data boundaries, and operational visibility that make software safe to run.
AI is changing how quickly teams can build.
The teams that succeed will also be able to change how they run.
Explore Xano’s AI provider integrations to build AI workflows with the flexibility to adapt as your product scales.
