Route each AI task to the right model
Classification, extraction and long-form reasoning ask different things of a model. We route each workload to a tested choice and keep the model name, fallback and spend limit in configuration.
- 01Identify the task
- 02Choose a model
- 03Check the output
- 04Track cost and errors
One model rarely fits the whole queue
Routing matters once a product runs several AI tasks or enough volume that cost and failure rates need their own controls.
Mixed workloads
Send high-volume tagging to a fast model and reserve a stronger reasoning pass for the jobs that need it.
Provider changes
Swap a model after an evaluation without rewriting every feature that calls it.
Operational limits
Set per-run and monthly ceilings, timeouts and a documented fallback when a provider is unavailable.
Make model choice a measured setting
Test representative tasks
We use real examples, including failures, to compare output quality, latency and price before assigning a workload.
Put routing in one place
Features call a stable interface. Provider keys stay server-side and the chosen model lives in configuration.
Watch after the switch
A model change runs against the same evaluation set, then production logs show cost, errors and correction rates.
We already change models across our own stack.
The AI overview documents OpenRouter as the routing layer used across our products. Site Structure uses DeepSeek for page clustering, CloudHosting uses Claude for plan checks, and PlexWyn uses GLM and GPT for different jobs. The split follows the workload.
See how our AI stack works Read where AI earns its keepWhat stays in your hands
We decide the handoffs before the first run, then make them visible in the product.
Fallbacks are tested
A fallback model may write differently or miss a field. We test it before it becomes the live recovery path.
Spend has a ceiling
Run and monthly caps stop an expensive loop. The failure remains visible so someone can decide what to do next.
Credentials stay server-side
The browser never receives provider keys. The team owns access and can change a provider without changing the page.
Where is AI spend getting away from you?
Show us the workloads, providers and usage pattern. We will map which calls need a stronger model and which calls are paying for one already.
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