Healthtech · Life Sciences Software · 50–200 · Europe #b034_17

Context

We run large-scale cell simulations that let pharma partners test therapeutic hypotheses in software before committing to laboratory work.

Industry

Healthtech · Life Sciences Software

Size

50–200 employees

Region

Europe

Tools budget

$1K–5K per month

Needs

Cloud cost optimization. Each partner campaign launches enormous numbers of simulations, and compute spend is hard to forecast or attribute.

Today: compute spend for large model runs tracked manually by research engineers · Urgency: this year

Analytics & BI. Partners want interactive views of simulation outcomes rather than static reports we assemble manually.

Today: scientists build their own dashboards from experiment outputs · Urgency: later

Data quality & observability. Our models depend on public and partner omics data whose inconsistencies we catch too late.

Today: biologists curate training data by hand before model training · Urgency: this year

Who decides

head of data · team trial

Expects from suppliers

Forecasting for bursty scientific compute; Shareable analytics with external partners; Data checks for omics formats

Not a fit

Business intelligence tools needing heavy licensing per viewer; Cost tools ignoring batch workloads

Stack

cloud: AWS-class hyperscaler, Kubernetes, data warehouse: cloud

Buying triggers

cost pressure, scaling transaction volume