Some links here are affiliate links — if you buy through them we may earn a commission at no extra cost to you. It never changes what we recommend. Full disclosure.
Disclosure: This article contains affiliate links. If you sign up through them, we may earn a commission at no extra cost to you — thanks for supporting the site.
If your application currently calls a single model provider's API directly, migrating to an inference router isn't a rewrite — it's a redirection. The migration is mostly about defining policies and testing routing behavior, not tearing out and replacing your existing integration.
Why This Matters
Most teams that start with a single hardcoded model end up there for a reasonable reason: it was the fastest way to ship. But that same hardcoding becomes a liability once you have multiple task types, multiple providers you'd like to compare, or a growing token bill that a single expensive model is driving.
Moving to a router means your application code stops caring which specific model handles a request. Instead, it sends a request tagged with a task, and the router — based on your configured policies — decides which model actually processes it, with automatic fallback if that model is unavailable.
- Direct API calls: your code is tightly coupled to one model and one provider
- Router-based calls: your code depends on a task/policy interface, and the model behind it can change without a code change
A Simple Framework
- Audit your current calls and group them by actual task type, not by which endpoint they happen to hit
- Define router policies mapping each task to an ordered list of candidate models
- Set required fallback models for every policy — this is not optional if you want resilience
- Run the router in parallel with your existing calls on a subset of traffic, comparing output quality and cost
- Cut over gradually, task type by task type, monitoring routing decisions as you go
Example
Before: An application makes direct calls to one frontier model for everything — customer emails, internal summaries, code review comments — with the model name hardcoded in a dozen places across the codebase.
After: The same application sends requests through an inference router configured with per-task policies. Customer emails route to a model tuned for tone and nuance; internal summaries and code comments route to smaller, cheaper models. The model name no longer appears anywhere in application code — only task labels do.
> Tip: Migrate your highest-volume, lowest-complexity task type first. It's the easiest place to prove cost savings quickly, and the lowest-risk place to validate that routing quality holds up before touching your most sensitive workloads.
Common Mistakes
- Migrating everything at once instead of task type by task type
- Skipping fallback model configuration, recreating the same single-point-of-failure risk you were trying to remove
- Writing task descriptions that don't clearly separate different kinds of work
- Not comparing output quality before and after migration, and only noticing a regression after users complain
DigitalOcean's guide on how to use the Inference Router walks through configuring policies, fallback pools, and session affinity.
> (ad) Your competitors are shipping AI features in days, not months. The difference? They're not managing infrastructure. DigitalOcean Serverless Inference = zero infrastructure, pay-per-token.
The end state of this migration is an application that's provider-agnostic by design — able to adopt a new, cheaper, or better model the moment it's available, without touching a line of application code.