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Eighteen months ago, most teams building with AI picked one model and wired it into everything. That made sense when there was a clear leader. It doesn't anymore. Today, different models win at different jobs — one writes better long-form copy, another is faster and cheaper for classification, a third handles code or structured output more reliably. Prices move constantly, and so does quality.
If your product is hard-wired to one provider, every one of those shifts becomes a migration project. If it's built on a routing layer, they become a configuration change.
What a routing layer actually does
A routing layer sits between your application and the model providers. Your product sends a request with some context — the task type, the user's plan, latency requirements — and the router decides which model handles it. In the multi-model AI workspace we built, routing decisions weigh three things:
- Quality for the task. We maintain small evaluation sets per task type and re-run them whenever a provider ships a new model.
- Latency. Interactive features get fast models; background jobs can wait for slower, stronger ones.
- Cost. Every request is priced before it's sent, and per-team budgets can cap or downgrade it.
Fallbacks are not optional
Every provider has outages, rate limits and the occasional bad deploy. A router should treat failure as normal: retry once, then fall back to the next-best model for that task, and log it. Users rarely notice. Without fallbacks, one provider's bad afternoon becomes your support queue.
Make cost visible early
AI costs creep. A feature that costs a fraction of a rupee per use in testing can become a serious line item at scale. We meter usage at the token level and surface it per user, per team and per model from day one — not as an afterthought when finance starts asking questions.
Evaluate continuously, not once
The biggest mistake we see is evaluating models once, at the start of a project, and never again. Treat evaluations like tests: version them, run them on every model change, and fail the deploy if quality drops below your threshold.
Where to start
You don't need a complex system on day one. Start with a thin abstraction over your model calls, log every request with its cost and latency, and add a second provider for one task. Once that works, routing becomes a natural next step instead of a rewrite.
If you're planning an AI feature and want to avoid vendor lock-in from the start, talk to our AI team.
