Machine Learning Valuation Multiples
Explore private market Machine Learning valuation multiples with benchmarks structured by stage and region. Updated quarterly.
Machine Learning
Machine Learning companies build tools, platforms, and applications that enable systems to learn from data and improve over time. Private market valuations reflect training data quality, model performance benchmarks, and the maturity of MLOps infrastructure that allows models to move from experimentation to production at scale.
The category spans ML platforms, AutoML tools, model monitoring, data labelling, and domain-specific ML applications in healthcare, finance, and industrials. DealMatrix tracks valuation dynamics across 7 funding stages and all major global regions, updated every quarter.
Sector
Machine Learning
Software & Data
Sector tracked since
2000
25+ years of data
EV/SALES & EV/EBITDA ACROSS
6 Regions · 7 Stages
Modelled independently via proprietary econometric approach
UPDATE FREQUENCY
Quarterly
Data updates & model improvement
Machine Learning Valuation Multiples
Sector benchmark as of 31 March 2025 · median across 6 regions · updated quarterly
How we derive these multiples
DealMatrix multiples are derived from institutional-grade public-market index data covering ~150 GICS sub-industries across 6 regions, with quarterly history back to 2000. Regional scaling follows Damodaran (NYU Stern), and the methodology follows the IPEV Guidelines 2025. Published benchmarks are illustrative and dated; because IPEV 2025 prohibits static multiples for reporting periods from 1 April 2026, current quarterly data for valuation work is available on the platform.
The Venionaire DealMatrix Multiples Model
DealMatrix multiples are proprietary private-market benchmarks, derived through a six-step model that translates public capital-market index comparables into private-market segments and funding stages, adjusted for macroeconomic conditions.
The model produces three components: The reported public multiple, the model-predicted multiple, and the lower bound predicted multiple averaged into the DealMatrix Composite, then adjusted for region and funding stage. The methodology follows the IPEV Guidelines 2025.
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Spanish startup Multiverse Computing raises €189 million to scale technology that compresses LLMs by up to 95%
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Region
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Mistral AI raises 600 million euros in latest funding round
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Valuation
AI startup reinventing the electric motor raises £10m monumo
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Software & Data
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AI
Software & Data
Analytics
Machine Learning Valuation Multiples — FAQ
What is the average valuation multiple for Machine Learning companies?
As of 31 March 2025, the Machine Learning sector benchmark was an EV/Sales multiple of about 3.8× and an EV/EBITDA multiple of about 18.1× (median across six regions). Multiples vary by funding stage and region; stage-level and current-quarter figures are available in DealMatrix.
What is the difference between EV/Sales and EV/EBITDA for Machine Learning?
EV/Sales (enterprise value ÷ revenue) is used for high-growth Machine Learning companies that are not yet profitable, while EV/EBITDA (enterprise value ÷ operating profit) applies to mature, profitable ones. Early-stage companies are usually benchmarked on EV/Sales.
How are Machine Learning valuation multiples calculated?
Each Machine Learning multiple is a weighted blend of public-market index comparables, cleaned for outliers and gaps, then adjusted for macroeconomic conditions, region, and funding stage through a six-step model that follows the IPEV Guidelines 2025.
Do Machine Learning valuation multiples vary by region?
Yes. North America serves as the reference market and typically carries the highest multiples, while emerging markets trade at a structural discount. Region-specific figures are available in the DealMatrix platform.
How current is this Machine Learning data and how often is it updated?
The benchmark shown is an illustrative annual figure as of 31 March 2025. The underlying model is updated every quarter. Because the IPEV Guidelines 2025 prohibit static multiples for reporting periods from 1 April 2026, current quarterly data for valuations is available in the DealMatrix platform.