Multi-Model Routing in Data Pipelines: A Decision Framework

A year ago, most production pipelines that used LLMs had a single model choice baked in. Today, with the open-weight landscape as competitive as it is and multiple model families offering genuinely different capability-cost tradeoffs, building a routing layer is worth the effort. Here's the framework I actually use.

Classify Your Tasks First

Before you can route, you need to understand what your pipeline is actually asking models to do. Most LLM calls in a data pipeline fall into a few categories: classification (is this record valid?), extraction (pull these fields from this text), generation (write a SQL query for this intent), diagnosis (why did this job fail?), and planning (what's the remediation sequence?). Each has different accuracy requirements, latency tolerances, and cost sensitivities.

The Routing Table

Once you've classified your tasks, the routing table becomes fairly mechanical. High-volume classification routes to Haiku or a small open-weight model because cost dominates and accuracy is sufficient. Structured extraction also routes to Haiku for fast, consistent JSON output. Complex SQL generation routes to Sonnet or o3 because accuracy matters more than cost. Root cause diagnosis routes to Sonnet with thinking enabled because reasoning depth matters. Stakeholder reports route to Sonnet or GPT-5 because prose quality matters.

Make the Router Testable

Your routing logic should be a function you can unit test independently of the models. Hard-coding model names in individual pipeline nodes makes it painful to update when a better cheap model ships. A routing function that accepts a task type and returns a model identifier means one change propagates everywhere.

Also: log which model handled which request in your pipeline audit trail. When something goes wrong and you're diagnosing whether the model made a bad decision or the routing sent the wrong model, you want that data. I'm here to help design the routing layer.