Model routing is the practice of directing each request to the cheapest model capable of handling it, rather than sending every request to a single default model, so cost and latency track task difficulty.
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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Routing turns model choice from a procurement decision made once into an engineering variable evaluated per request. A document classification and a multi-step legal analysis have very different difficulty, and paying frontier prices for the former subsidizes nothing.
Routing can be static — rules mapping task types to models — or dynamic, where a small classifier estimates difficulty and selects accordingly. Static routing captures most of the available saving with far less operational complexity, and is where nearly every deployment should start.
The prerequisite is portability. Routing is only possible if the surrounding platform can address multiple providers and local models through one interface, which makes it a property of the runtime rather than of any model.
Model routing is where the largest inference savings usually live, because model prices span orders of magnitude while a large share of enterprise requests are routine. It is also the mechanism that makes a fast-moving model landscape an advantage rather than a migration risk.
Routine classification and extraction go to small or local models; hard reasoning goes to frontier models. Spend tracks the work rather than a single default choice.
Rules mapping task type to model deliver the bulk of the available saving without the operational complexity and failure modes of dynamic difficulty estimation.
Routing is only possible when the platform addresses multiple providers and locally hosted models through one interface, which is a property of the runtime, not of any model.
Beyond cost, routing can send anything touching regulated data to a local model while non-sensitive work uses a hosted frontier model — a compliance control, not only an economic one.
When a better or cheaper model ships, adoption is a configuration change. Without routing it is a revalidation project on the vendor's timetable.
Routing without measurement is guessing. A held-out evaluation set per task type is what makes 'cheapest capable' a claim you can defend rather than a hope.
The high-volume tail runs at near-zero marginal cost while the low-volume, high-stakes work keeps frontier quality.
Protected health information never leaves the network, and the routing rule is the enforcement mechanism rather than a policy staff must remember.
It is adopted for the matching task types the same week by changing a routing rule, with no change to any agent definition.
ibl.ai is model-agnostic by design: one interface addresses commercial providers and locally hosted open-weight models alike, so each request can be routed to the cheapest capable model, or to a local model whenever it touches regulated data. Because you own all the code and the data, every routing improvement reduces your own cost permanently instead of a vendor's cost of goods, and the routing logic is source you can read and change. The platform carries no per-seat pricing, so a cheaper route shows up directly on the bill, and you can deploy anywhere. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
Learn about ibl.aiibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
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1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
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