During a recent Series B negotiation, the term sheet tripled its "tech-risk discount" in a single day. Why? The startup's slide deck showed a 3-day deploy lead time, but Git logs revealed a three-week median. Hm... Spreadsheets are stories, telemetry is truth.
Lesson: numbers - the right numbers - short-circuit debate and protect valuation.
Five signals investors track first
Investors care about speed, stability, quality, reliability and unit-economics.
- Speed: Deployment frequency and lead time reveal how quickly a team can ship fixes and features. Daily deploys with sub-24-hour lead time are the current gold standard for growth-stage SaaS.
- Stability: Mean Time to Recover (MTTR) measures incident resilience; anything longer than an hour for a Sev-1 outage suggests hidden operational debt that could spur customer churn.
- Quality: Change Failure Rate (CFR) balances speed with safety. A rate above ~15% says each release is a coin-flip and that engineering quality gates need attention.
- Reliability: Uptime against stated Service Level Objective (SLO) or contractual Service Level Agreement (SLA) turns availability into real money. Missing a 99.9% target can trigger credits or churn and therefore feeds straight into valuation models.
- Unit-economics: Cloud Cost of Goods (COGS) per Euro of Annual Recurring Revenue (ARR) links architecture choices to gross margin. FinOps data that shows ~20 cents or less of cloud spend for every revenue Euro gives you and the investor confidence the unit economics will scale.
How stage changes the lens
At Seed or early Series A, directional trends like weekly deploy cadence, or a high-level AWS bill are often all that exist, and that's acceptable. By Series B, investors expect a quarterly dashboard with the five signals above and at least six months of history. By Series C or any control buy-out, granularity might jump to hour-level graphs, audited uptime reports, and FinOps views broken down by product line or customer cohort.
Turning numbers into narrative
- Show the live board, not a slide. Read-only Grafana access lets investors slice the data themselves and builds trust.
- Always pair metrics with context. 99.99% uptime means little without mentioning a yardstick you measure against, like SLO or SLA.
- Tie every metric to money or time. Trimming lead time from 5 days to just 1 releases roughly two developer-weeks of capacity every month, worth ~Xk Euros in annual engineering time you can redirect to new features or margin.
Traps that still trip teams
Metrics hidden behind optimistic filters, snapshots taken during a lucky (carefully selected) week, or cloud-spend charts with no allocation by service - all of these erode confidence fast. The fix is simple: show rolling six-month graphs and normalize cost data against revenue growth to show efficiency.
A habit worth adopting
Create a standing Tech Due Diligence Metrics Pack: one Grafana board, a CSV export, and a two-page narrative that explains what the numbers mean. Update it monthly and drop it in the data room before anyone asks. Radical transparency is the fastest way to shorten tech diligence cycles.
Mini-Glossary
- Lead time: the span from commit to production.
- Mean Time to Resolution (MTTR): the average restore time after an incident.
- Change Failure Rate (CFR): the percentage of releases that result in a rollback or hotfix.
- Service Level Objective (SLO): the internal reliability target that supports the contractual SLA.
Your turn
Which metric bit (or saved) you during diligence? Share the story; real numbers teach better than theory.
If you need help setting up the most relevant benchmarking metrics for your stack, let's talk.
Next in the Playbook
Edition 4 will dive into technical debt - when to price it, when to pay it down, and when to walk away. Subscribe so it lands in your inbox.
Originally published on the Tech Due Diligence Playbook newsletter on LinkedIn.