Cut the 80% of your AI bill that doesn't need a frontier model.
We audit your API traffic, find the routine calls — classification, extraction, drafting — and replace them with a 3–7B model fine-tuned on your task. Deployed in your infrastructure, proven against a frozen eval set before anything switches over.
LLM-to-SLM migration replaces routine frontier-API calls with a small model fine-tuned on your task, running in your infrastructure. Published migrations cut inference costs 66–80% (Forethought, on AWS) while accuracy on the narrow task held or improved. A router escalates the genuinely hard cases, so nothing gets dumber — the bill gets smaller.
The work doesn't get harder. The invoice does.
Most production AI workloads are narrow and repetitive: tag this ticket, extract these fields, draft this reply. Frontier APIs price every one of those calls as if it were open-ended reasoning — so your bill grows linearly with usage, and most of it pays for capability you don't use.
A worked example, from public list prices. At 100M tokens a month of routine traffic on a standard frontier API, the same workload on a fine-tuned 7B model self-hosted on a single GPU instance runs at a flat infrastructure cost — and the gap widens with every step of growth. Our cost model, August 2026; The Crit replaces it with your numbers.
| Frontier API | Fine-tuned small model, yours | |
|---|---|---|
| Cost shape | Per token, linear with volume — the meter never stops | Flat: one GPU instance; marginal cost per call is a rounding error |
| Accuracy on the narrow task | General model guessing at your task | Trained on your task: 81→93% in distil labs × Knowunity (vendor-reported) |
| Data exposure | Every call processed by a third party | Nothing leaves your perimeter |
| Published result | — | 66–80% lower inference cost — Forethought, on AWS |
If your workload doesn't clear roughly 50M tokens a month and you have no privacy constraint, a frontier API is probably fine — and The Crit will tell you so. That's what the audit is for.
The whole system. Yours.
- Audit report: per-workflow cost map, migration candidates ranked by ROI
- Frozen eval set built from your real traffic — yours to keep either way
- Fine-tuned model weights — you own them, no per-token licence
- Deployment in your VPC or on-prem, delivered as infrastructure-as-code
- Hitbox routing config: escalation policy for the cases the small model shouldn't answer
- Monitoring dashboard and a re-training runbook your team can operate
How much does migrating from an LLM to a small language model save?
Published migrations report 66–80% lower inference costs (Forethought, vendor-reported, published on AWS). Actual savings depend on what share of your traffic is routine — typically 60–85% of production calls. Below roughly 50M tokens a month, migration usually isn't worth the build cost, and we'll tell you that in the audit.
Will a smaller model be less accurate than a frontier model?
Not on a narrow task it was trained for. In distil labs' published work with Knowunity, a fine-tuned small model raised task accuracy from 81% to 93% while cutting costs (vendor-reported). We gate every migration behind a frozen eval set built from your real traffic — the small model ships only if it matches or beats your current accuracy.
Which models do you use for LLM-to-SLM migration?
Open-weights models in the 3–7B range — Llama, Qwen, and Gemma-class — fine-tuned on your task via distillation from your existing pipeline's logs. You own the resulting weights and run them on your own infrastructure; there is no per-token licence back to us or anyone else.
Cut the bill. Keep the custody.
- Data stays in your VPC
- No new subprocessor
- Escalation under your rules
- Keys and logs are yours
Your traffic already knows the answer.
Two weeks inside your API logs, and you'll know exactly which calls a small model can take over — and which it can't. The numbers are yours either way.