Your CRM is not someone else's training data.
We build small, fine-tuned models for SaaS companies twice over: in the product, where frontier-API calls are a COGS line — and in the revenue stack, where scoring, extraction, and personalization should run on infrastructure you control.
For SaaS companies, the small-model pattern pays twice. In the product, a fine-tuned 3–7B model takes over the routine calls behind your AI features and turns per-token COGS into flat infrastructure — published migrations cut inference costs 66–80% (Forethought, on AWS). In the revenue stack, lead scoring, CRM extraction, and outbound personalization run inside your VPC instead of a vendor's API, so your most sensitive commercial data stays home.
SaaS companies meet the small-model argument in two places. First, the product: if your features call a frontier API on every request, inference is a cost of goods that scales with your success — and most of those calls are narrow, repetitive work the API is overqualified for. Classification, extraction, formatting, scoring: a fine-tuned 3–7B model handles them at a fraction of the cost, with a router escalating the genuinely hard cases.
Second, the revenue stack. Your CRM, call transcripts, and enrichment pipelines hold the most commercially sensitive data you own — and most AI-for-GTM tooling works by shipping that data to someone else's model. Lead scoring, intent classification, email-to-CRM extraction, and outbound personalization all run well on small models deployed in your own VPC. At outbound scale the arithmetic is stark: our cost model, from published per-token list prices (August 2026), puts a locally-served 7B at roughly $0.0001 per personalization request, against $0.025–0.15 through a frontier API.
This is the vertical we operate in ourselves. Crit Studio's founder runs GTM engineering on Salesforce, Clay, and Lemlist daily. The systems we build here are the systems we use.
Product COGS on one side. Revenue stack on the other.
SOC 2 commitments, DPAs, and subprocessor lists constrain where customer data can flow — and adding a frontier API to your product or your CRM tooling usually means updating all three. A model deployed in your VPC adds no new subprocessor and no new data flow to disclose. For EU and UK prospect data, processing on your own infrastructure also shortens the GDPR story your sales-ops team has to tell. We're engineers, not your compliance advisers — we build the systems; your counsel signs off on the paperwork.
| Published result | Source |
|---|---|
| Inference costs fell 66–80% after moving routine support workloads to fine-tuned small models | Forethought, published on AWS |
| Task accuracy rose from 81% to 93% while inference costs fell 50–68% | distil labs × Knowunity, vendor-reported |
| Serving costs fell roughly 50% on dedicated small-model infrastructure | distil labs on Cerebrium, vendor-reported |
| Task-specific small models to see 3× the adoption of general-purpose LLMs by 2027 | Gartner forecast |
The outbound cost figures above are our own estimates from published per-token pricing — we show the arithmetic in The Crit, with your traffic in the spreadsheet instead of ours.
Can a small model really replace frontier-API calls in our product?
For the routine share, yes — classification, extraction, formatting, and scoring are narrow tasks a fine-tuned 3–7B model handles well, and published migrations report 66–80% lower inference costs (Forethought, on AWS). A router escalates the genuinely hard cases to your existing frontier API, and we gate every migration behind a frozen eval set built from your real traffic.
Does this add a subprocessor to our DPA?
No. The model runs in your VPC, so there is no new external processor in the data flow, no new subprocessor-list entry, and no new disclosure to make. That is usually the difference between shipping an AI feature this quarter and renegotiating customer agreements first.
What does outbound personalization cost on a local model?
Our cost model, from published per-token list prices (August 2026): roughly $0.0001 per personalization request on a locally-served 7B model, against $0.025–0.15 through a frontier API. At 100k-email scale the difference decides whether personalization is a line item or a rounding error. We show the arithmetic in The Crit, with your numbers.
Your CRM never feeds Big AI.
- Pipeline data stays in your VPC
- No third-party enrichment APIs
- Your DPA stays unchanged
- Prospect data never resold
See if the numbers hold in your stack.
The Crit is a teardown of your AI spend and workflows: where a small model wins, where an API is fine, and where AI shouldn't be used at all. You get the numbers either way.
If your workload doesn't clear roughly 50M tokens a month and you have no privacy constraint, a frontier API is probably fine — and we'll tell you so.