Are we sending all our AI queries to one model regardless of complexity?

Sending every query to your most capable model is like flying first class for every trip. Route simple queries to cheap models and complex ones to premium models — the cost difference at scale is dramatic.

What percentage of our AI interactions are simple enough for a smaller, cheaper model?

Most organizations find 50-70 percent of interactions are routine enough for a cheaper model. Routing those saves dramatically with zero quality loss on the routine queries.

Do we have visibility into per-query costs and quality across different model options?

Without per-query visibility, you cannot optimize. Instrument your pipeline to track cost and quality per query, then use that data to set routing rules matching capability to complexity.

The AI Industry

Multi-model strategy

By Mark Ziler · Last updated 2026-04-05

A multi-model strategy means using different AI models for different tasks based on their strengths — one model for analyzing service data, another for customer-facing conversation, a smaller one for quick classification tasks that need to be cheap and fast. This is like having different specialists on your team instead of asking one generalist to do everything. The key infrastructure requirement: your platform needs to be model-agnostic so you can swap without rewiring.

Go deeper

Your customer service team uses a powerful model that costs $0.03 per interaction to handle patient inquiries. But 60% of those inquiries are simple appointment confirmations that a model costing $0.001 per interaction handles equally well. At 10,000 interactions per month, routing those routine questions to a cheaper model saves $1,740 per month — $20,880 per year — with zero quality loss on those interactions. The expensive model still handles the complex cases that need its capability.

The trap most companies fall into is using their most powerful model for everything because it was easiest to set up that way. It's like sending a senior technician to change air filters. The work gets done, but you're paying specialist rates for routine tasks while your complex jobs queue up. Multi-model isn't about having the best AI — it's about matching capability to the task.

Questions to ask

Can we categorize our AI use cases into tiers based on complexity, and are we paying frontier-model prices for tasks that a simpler model handles fine?
Does our AI platform allow us to route different types of requests to different models, or are we locked into one?
What's our monthly AI compute cost breakdown by use case, and where is the biggest mismatch between cost and task complexity?