Paying for Claude Opus every time someone asks "what time is our meeting tomorrow" is overspending by 10 to 50x. Most AI deployments treat every query the same, sending every question to the same model at the same cost. A simple lookup costs as much as a complex financial analysis.
Model routing fixes this.
Send each task to the right model
Model routing sends each task to the model that fits it. A simple lookup goes to a fast, cheap model. A complex analysis goes to a more capable one. Sensitive data goes only to approved models. The user does not need to think about any of it. They ask a question and the system decides which model handles it.
Three dimensions determine the route.
Complexity. "What is our company holiday policy?" does not need a frontier model, and a fast model handles it in milliseconds at a fraction of the cost. "Analyze our Q4 pipeline and identify the three deals most likely to slip" needs something more capable. The complexity of the task determines the model.
Cost. Every model has a different price per token, and routing lets you set budgets by team, by user, or by use case. The system optimizes within those constraints automatically, preventing surprise bills and keeping individual users from running up costs on tasks that do not require expensive models.
Data sensitivity. Some queries involve customer PII, some involve financial data, and some are just "rewrite this email to sound more professional." Data classification determines which models are eligible, and sensitive data can be restricted to specific providers or kept on approved models only.
The cost problem without routing
Without routing, companies face two options. Give everyone the most capable model and pay 10x more than necessary, or give everyone the cheapest model and deal with complaints about quality on complex tasks. Neither works. The first burns budget, the second burns trust.
With routing, the median query goes to a fast model and the 10% of queries that need more power get it automatically. In our deployments, routing typically reduces AI costs by 40 to 60% compared to single model setups while maintaining the same output quality as rated by users.
No vendor lock-in in practice
Admins define which models are approved and set routing rules based on complexity, sensitivity, and budget. Users see one interface. They do not pick models. They ask questions and get answers from whatever model fits the task.
A new model launches that is better or cheaper. The admin adds it to the approved list and adjusts routing rules, and every user benefits immediately. No retraining, no workflow changes, no migration project.
That is what no vendor lock-in actually looks like in practice. Not a promise on a pricing page, but a routing layer that makes switching models a configuration change instead of a project.