AI & Machine Learning Business — Business Valuation Multiple
EBITDA Valuation Guide · Small & Mid-Sized Businesses
Actual EBITDA multiples can vary materially based on EBITDA size, growth, recurring revenue, margins, customer concentration, owner dependence, geography and other company-specific factors.
Min
6×
Typical
9×
Max
15×
Revenue Model
Recurring
Capital Intensity
Varies
A typical multiple of 9× EBITDA reflects mid-market transactions in the $1–$50M revenue range.
Overview
AI and machine learning businesses typically trade at 6×–15× EBITDA, reflecting strong strategic demand for proprietary models, training datasets, and ML engineering teams. Note these multiples reflect profitable SME AI businesses with recurring enterprise revenue; pre-profit businesses are valued on revenue multiples not reflected here. Recurring SaaS revenue with enterprise clients, proprietary model performance advantages, and embedded workflow automation support premium valuations. Strategic acquisition demand from large technology companies keeps multiples elevated. Talent cost and rapid technology obsolescence are the primary risk factors.
Key Value Drivers
- Proprietary model and training dataset
- Recurring SaaS revenue with enterprise clients
- Embedded workflow automation
- Strategic acquisition interest from large tech
- Demonstrable performance advantage over open-source
Key Risks
- Talent cost and ML engineer scarcity
- Rapid technology obsolescence
- Open-source model commoditisation
- Data privacy and regulatory risk
- Customer adoption complexity and change management
Related Industries
Disclaimer: All multiples are indicative estimates only and are not formal valuations or investment advice. They should not be relied upon for financial decision-making without independent professional advice.