Public Software Valuation Multiples — February 2026

Software multiples in February 2026 show clear segmentation across infrastructure, vertical, and horizontal categories, with significant dispersion in both revenue and EBITDA multiples reflecting varying market maturity and profitability profiles.

Public investors seem to currently value software companies based on AI application (or death risk due to AI disruption), technical complexity, market position, and specialization depth - rather than TAM size alone.

Scroll below for valuation multiples across SaaS sectors.

Horizontal SaaS public comps in February 2026 show wide valuation dispersion (a sign of AI's "creative destruction"). Design and engineering software commands premium multiples, as companies like Autodesk and Adobe successfully integrate AI features that enhance rather than cannibalize their core products. Vertical AI-native applications trade at similarly strong levels, reinforcing that the market is rewarding AI-driven value creation across the board.

By contrast, sales automation sits well below the broader SaaS average (gen AI threatens to fundamentally replace traditional CRM workflows rather than augment them). AdTech and video streaming (as usual) trade at even steeper discounts, though this reflects longer-standing structural issues rather than recent AI disruption: massive platform dependency risk for MarTech players, and unsustainable infrastructure costs that can consume the vast majority of revenues in streaming.

Vertical software comps slightly outperform horizontal SaaS on average - not surprising given that industry-specific software naturally creates deeper customer relationships and higher switching costs than vanilla horizontal alternatives.

In February 2026, within verticals, automotive software trades high, benefiting from the sector's ongoing digital transformation and mission-critical workflows. Travel and hospitality anchors the bottom of the group (thinner margins and more cyclical demand patterns typical of the space).

Infrastructure SaaS is (as usual) pulling ahead of everything else. Data infrastructure commands the highest multiples across all categories, driven by the AI data boom. Every enterprise AI initiative begins with the data layer, and companies like Snowflake and Databricks have made themselves indispensable to that buildout. DevOps trades at a similar premium, underpinned by some of the stickiest contracts in enterprise software and a market projected to roughly double over the next decade.

Cloud infrastructure, however, trades at a notable discount to its peers, despite being the foundation everything else runs on. The implication is clear: investors are starting to treat cloud compute as a commodity. Data and DevOps have that moat. Cloud, increasingly, does not.

Underlying data

Multiples is a valuation analytics platform. 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.

Public companies

The foundation of our public comps are analyst estimates and historical financials, powered by FactSet (consensus analyst estimates) and Morningstar (historical data). 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.

Private transactions

We source deal data in-house through company filings, press releases, and proprietary data sets built over years of investment banking careers. We harmonize and unify data in-house to form a unique data set of disclosed transactions. We triple-check deal data through a proprietary red-flagging system, compare with verified online sources, and sense-check with our investment banking team. We exclude undisclosed and 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 the target's net debt, we might adjust a valuation to fully reflect an accurate EV. In all other cases, we take the reported valuation as the 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 closed in November 2025, we might take full-year 2025 revenue as a revenue benchmark.