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July 2025 public software valuations reveal distinct market dynamics where specialization and switching costs drive premium multiples. Automotive software comps trade the highest at 5.0x NTM revenue while healthcare software trades at lower multiples (2.4x revenue), reflecting lengthy sales cycles and regulatory approval processes that slow growth rates.
Infrastructure SaaS commands the highest valuations not without a reason - infra software companies like Snowflake (16.4x revenue!), Datadog, and MongoDB are the backbone of the Internet and control the foundational layer that other software depends on.
Dive into the latest software valuation multiples below 👇
In July 2025, design and engineering software and supply chain management both command premium 5.8x revenue multiples at the high end, while mature categories like AdTech and video platforms trade at compressed 1.2x and 1.6x multiples respectively. EBITDA multiples reveal significant dispersion, with supply chain management software leading at 26.0x and enterprise-focused solutions like ERP (18.6x) and financial management (18.3x) commanding higher valuations than consumer-oriented categories, most likely showing different profitability trajectories and market saturation levels across horizontal software segments.
In July 2025, vertical software companies demonstrate strong valuation premiums across most sectors, with automotive software at 5.0x NTM revenue and industrial software at 4.7x, reflecting the specialized nature and high switching costs inherent in industry-specific solutions. The healthcare and education sectors trade at lower multiples (2.4x and 2.3x respectively), likely due to longer sales cycles and regulatory complexities. EBITDA multiples show significant variation ranging from 8.5x for travel and hospitality to 17.7x for public sector and non-profit software, indicating varying profitability profiles and growth expectations across different vertical markets.
As of July 2025,Infrastructure software commands the highest valuations across all categories not without a reason.Data infrastructure comps valued the highest at 6.9x NTM revenue and DevOps at 6.0x - strong investor confidence in foundational technologies (backbone of the Internet). EBITDA multiples reveal lots of variation, with DevOps commanding an exceptional 41.6x multiple and data infrastructure at 25.9x, suggesting significant growth expectations despite limited profitiability, more commoditized segments like cloud infrastructure show compressed multiples, indicating varying investor expectations for scaling and market positioning across different infrastructure segments.
Multiples is a proprietary valuation software database. Public markets data is powered by FactSet and Morningstar, harmonized and enhanced by us. Private transaction data is multi-sourced, aggregated from harvesting public information, agreements with 3rd party providers, proprietary sourcing and data engineering. All data is verified and provided with an extensive manual process (no AI involved). This report is built on data that is available at multiples.vc. For more information, please visit multiples.vc or contact us at hi@multiples.vc.
The foundation of our public comps are analyst estimates and historical financials, powered by FactSet (consensus analyst estimates) and Morningstar (historical data) APIs. Analyst estimates are provided by FactSet by taking input from 50+ equity research analysts (the highest amount across all financial data providers). Data points are calendarized to December where relevant: retrieved data on financial year ends (e.g. FY, FY+1 etc.) are mapped to calendar years (2024A, 2025E etc.) before the appropriate month weights are then applied to prior/future fundamentals. We emphasise valuation multiples and operational ratios, any raw figures are harmonised to USD for comparison purposes.
We source deal data from 3rd party databases, company filings, press releases and our own proprietary data sets built over years of investment banking career. Sourced data is harmonized and unified in-house, to form a unique data set of disclosed transactions. We triple-check deal data through a proprietary red-flagging system, compare with other verified online sources, and sense-check with our investment banking team. We exclude undisclosed, unrealistic or suspicious-looking data points. Our dataset is continuously monitored for accuracy. EV calculation: if data permits, we apply our own logic to get to the EV. For example, for a large M&A deal with available information on target's net debt, we might adjust a valuation to fully reflect an accurate EV. In all others cases, we take reported valuation as numerator. Financials: we source LTM revenue and LTM EBITDA data from company filings, press releases or other verified sources. If LTM data is unavailable, we take the next best-fit period (run-rate or calendar year), provided it makes sense in a given case. For example, if a deal happened in November 2024, we might take full-year revenue as a revenue benchmark.
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