Why LLM Routing Matters More Than Model Selection

When teams first adopt LLMs they often pick a single flagship model, Claude Opus or GPT-4o, and route everything through it. It feels safe. The model is capable and the results are good.

But this approach is expensive and brittle.

The Hidden Cost of One Size Fits All

A simple task like extracting a date from a user message does not need a 200 billion parameter model. It needs a fast, cheap inference call. Routing that through Opus or GPT-4o is the equivalent of hiring a senior architect to fix a typo.

Flagship models cost $15 to $75 per million output tokens. Smaller, task-specific models cost $0.10 to $2 per million output tokens. That is a 10 to 100 times cost difference for equivalent task quality on simple work.

What Intelligent Routing Looks Like

AiPipe, our LLM router built into Mission Control, classifies each inference request by task complexity, context size, latency requirements, and minimum output quality. Based on those signals it routes to the optimal provider: Anthropic or OpenAI, whichever delivers the best result at the lowest cost for that specific task.

The router runs at the proxy layer, which means no changes to agent prompts, no provider lock-in, and no refactoring when you add a new model.

Why This Matters at Scale

At low volume, overpaying per token is annoying. At production scale with multiple agents running continuously, it becomes a significant monthly line item. The savings compound because the cheapest model for simple tasks frees up budget for the work that genuinely needs frontier-model capability: deep research, long context reasoning, and multi-step planning chains.

Getting Started

AiPipe is built into Mission Control on all plans. Connect your provider keys in Settings and the router handles model selection from there.

The best model is the right model at the right time, not the biggest one.