Are we running a large, expensive model for tasks where a smaller model would perform equally well?

Distillation trains a small model to replicate a large model's behavior on your specific tasks. If your workload is narrower than the model's full capability, a distilled version delivers equivalent quality at a fraction of the cost.

What is our per-query cost, and how does it scale with the volume of queries we process?

At high volume, small per-query cost differences compound dramatically. Distillation and compression directly reduce per-query costs that add up to thousands per month at scale.

Could we reduce our AI infrastructure costs by running a compressed model locally instead of making cloud API calls?

Local execution eliminates per-query API costs and network latency. Above a certain volume threshold, local execution is cheaper — the break-even is lower than most people assume.

The AI Industry

Model distillation & compression

By Mark Ziler · Last updated 2026-04-05

Model distillation takes a large, expensive AI model and creates a smaller version that handles specific tasks nearly as well at a fraction of the cost. Think of it as training a specialist from a generalist — the specialist doesn't know everything, but they're faster and cheaper for the job you hired them to do. This is how AI gets affordable for routine business tasks like classifying support tickets or extracting data from invoices.

Go deeper

You built an AI workflow that classifies incoming service requests into 12 categories and routes them to the right team. It works great using a frontier model, but it costs $0.05 per classification and you process 3,000 requests per day. That's $4,500 per month for a task that's honestly not that complex — the model is massively overpowered for this job. A distilled model trained specifically on your 12 categories could handle it at $0.002 per classification — dropping your cost to $180 per month for the same accuracy.

The trap most companies fall into is not knowing this option exists. They assume AI cost is fixed and that the only way to reduce it is to use AI less. Distillation lets you use AI more by making routine tasks dramatically cheaper. The expensive model trains the cheap model on your specific job; then the cheap model runs independently.

Questions to ask

Which of our AI-powered workflows are stable enough — same task, consistent input format, predictable output — that they could run on a smaller, cheaper model?
Does our AI vendor offer tiered model options, or are we paying full price for a frontier model on every task?
At our current volume, what would the annual savings be if we moved routine classification and routing tasks to distilled models?