See your true net burn and what it means for the next 12 months. Enter MRR, churn, and monthly expenses, we'll project your cash trajectory, burn multiple, and the SaaS Magic Number that investors care about.
Burn Multiple = Net Burn ÷ Net New ARR. It measures capital efficiency, how many dollars you burn for each dollar of new recurring revenue. David Sacks popularized it in 2020. Under 1.0 is exceptional, 1-1.5 is good, 2+ is a red flag, 3+ means you're burning cash faster than you're growing.
Magic Number = (Quarterly ARR growth × 4) ÷ (Sales & Marketing spend over the prior quarter). Above 1.0 means you should pour more fuel on growth; 0.5-1.0 is healthy; below 0.5 suggests your sales engine is inefficient. This tool estimates an annualized version.
Net burn = total monthly operating expenses (gross burn) minus recognized revenue. If you spend $60k/mo and collect $25k MRR, your net burn is $35k/mo. That's the rate at which cash actually leaves the bank.
This calculator exists to be embedded in a sourcing or diligence workflow, not used once and closed. The numbers it produces are the same primitives the GitDealFlow dataset is built on: 350+ startup GitHub organizations tracked across 15 sectors, refreshed weekly. When a target company is being evaluated, run its figures here, then compare against the sector baseline in the research dataset. The disciplined pattern is signal first (breakouts surface 21 to 47 days before the round), then arithmetic (does the unit economics justify a meeting), then process (memo, checklist, decision).
A practical read-through of Burn Rate Analyzer: the dataset behind this page refreshes weekly across 350+ organizations and 15 sectors, and every figure shown traces to a public GitHub REST API pull. That matters for two reasons. Reproducibility: any number here can be re-derived from primary sources, which is the standard the published methodology sets for itself. Timeliness: engineering acceleration precedes announcements, so this page follows the data cadence rather than the news cycle, and the freshness endpoint always reports the exact pull date.