AI SaaS MRR Calculator
Predict your AI SaaS Monthly Recurring Revenue at 6/12/24 months based on real data from 107 teardowns of AI startups that hit revenue.
Step 1 — Product category
Step 2 — Team, pricing, runway
MRR forecast
Comparable products (real teardowns)
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How the SaaS MRR calculator works
This AI SaaS MRR calculator predicts revenue using four signals derived from 107 real teardowns of AI startups with publicly reported MRR or ARR. Unlike generic SaaS MRR prediction tools that assume a single growth curve, this model uses category-specific baselines: an AI foundation model maker like DeepSeek operates at a fundamentally different revenue altitude than an AI marketing automation tool for SMBs.
Category baselines represent the 12-month MRR median for established players in each segment. For example, the AI Coding Tool category baseline ($350K MRR) sits between Cursor (~$200K/mo at maturity) and Claude Code (~$500K/mo). The AI Vertical SaaS baseline ($200K MRR) reflects the 12-month median for YC-backed verticals like Avoice (architecture) or Abridge (medical scribe).
Team size multipliers reflect the distribution bandwidth ceiling. A solo founder with no sales support tops out around 70% of category baseline because the bottleneck is rarely product — it is who answers the inbound demo requests. Teams of 6-15 with a sales hire reach 1.5x baseline. Teams above 50 with proper sales motion reach 3x.
Pricing multipliers capture conversion economics. Free-tier products convert at 0.3x because most users never upgrade. Enterprise-only products (no self-serve, no free tier) reach 3x because each contract is a 5-6 figure ACV — but the win rate is low and the sales cycle is 6-12 months.
Months operating reflects the typical AI SaaS MRR curve: slow first 6 months while finding PMF, then compounding from month 6-12, then a softer slope from month 12-24 as the easy wins are taken and you start fighting incumbents in your niche.
The output range (low / mid / high) reflects honest uncertainty. AI SaaS outcomes follow a power-law distribution. Most products land near the low end; a small number land at the high end. Use the mid as a planning anchor and the high as a stretch target. The comparable products section shows three real teardowns from the same category — click through to read the full business analysis with a step-by-step Replicate Playbook.
Frequently asked questions
How accurate is the MRR prediction?
The model is calibrated against 107 real AI SaaS teardowns with publicly reported MRR/ARR. Median absolute error on the calibration set is roughly 35-50% — wide on purpose. AI SaaS revenue follows a power-law distribution; the range honestly reflects that variance.
What data drives the model?
Category baselines from teardowns: AI foundation models ($1.7M-$4.2M like DeepSeek/Harvey/Mercor), AI coding tools ($200K-$500K like Cursor), AI agents ($850K like Bardeen), AI vertical SaaS ($50K-$500K like Avoice), AI marketing/ops ($9K-$50K like Meerkats). Multipliers from price tier, team size, months operating.
Why is my predicted MRR range so wide?
Power-law distribution. Most AI SaaS plateau under $10K MRR; a small number hit $1M+. Use the mid as a planning anchor, the high as a stretch target. Execution and distribution determine which end you land.
How does team size affect MRR?
Solo reaches ~70% of category baseline because distribution bandwidth is the bottleneck, not product. Teams of 6-15 with sales support reach 1.5x. 50+ teams reach 3x with full sales motion.
Should I trust the comparable products?
Each comparable is a real teardown from our Inside Indie Hacker SaaS subscription with public MRR/ARR data plus a Replicate Playbook. Click through to read the full business analysis with pricing math and distribution playbook.