Testing a seat-to-usage pricing switch with 16 engineering buyers — before touching the billing code
A code-first API-integration platform pressure-tested a risky pricing migration against real technical buyers, in 48 hours.
Halyard's team suspected usage-based pricing fit their product better than seats — but a mispriced migration could tank revenue and anger enterprise accounts. Instead of guessing, they ran three synthetic studies against 16 engineering personas spanning PLG startups to 1000+ orgs.
16
engineering personas
3
studies in 48h
2-tier
pricing model shipped
0
billing code touched first
The challenge
The product team believed seat-based pricing under-monetised heavy users and over-charged small teams, but the finance and sales orgs feared a usage-based switch would create unforecastable bills and spook enterprise buyers. No one had strong evidence either way, and running a real pricing survey across engineering leaders would take weeks of recruiting.
Our approach
Halyard defined an audience of 16 engineering personas — platform, backend, integration and DevOps engineers plus their managers and VPs — across three company-size bands and five 'current approach' segments (in-house, Zapier, Workato, MuleSoft, none). They ran a packaging & pricing questionnaire, a jobs-to-be-done interview on how teams adopt integration tooling, and a live group conversation reacting to the proposed usage-based model.
The results
The evidence was decisive: usage-based pricing splits cleanly by company size. PLG and mid-market teams welcome it as fairer than seats, while 1000+ orgs will only accept it with a committed floor and hard caps. The team also learned the real must-have was observability and a typed SDK — not more connectors — and that a transparent, self-serve usage calculator would sell the pricing page better than a 'contact sales' wall. Halyard shipped a hybrid model: usage-based with caps for self-serve, a committed-floor plan for enterprise.
Research brief
Research goals
Hypotheses
The audience · 16 personas
37
Avg. age
44% F · 56% M
Gender
15
Countries
Backend
Role
Studies in this project
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