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What does a SaaS valuation multiple actually measure?
A multiple is a ratio, not a valuation formula. Before using one, identify what sits above and below the “x,” which companies or transactions are included, what statistic is reported, and when it was measured. A public-market multiple also does not price the same thing as a private control sale: public investors trade shares, while an acquisition may transfer control and involve deal-specific terms.
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Public market capitalization divided by run-rate revenue
SaaS Capital’s public index calculates a revenue multiple by dividing market capitalization by annualized current run-rate revenue. It annualizes the latest monthly revenue by multiplying it by twelve. The index covers 63 publicly traded companies on U.S. exchanges that SaaS Capital selected as primarily valued on B2B recurring software revenue; the page says primary data come from company-reported SEC filings, with data processing through Tiingo. This is an equity market-capitalization measure, not automatically enterprise value divided by trailing revenue. The index page says its data are current through September 30, 2026, but that date alone does not establish a specific September multiple.
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Private transaction reports commonly express deal value as enterprise value (EV) divided by trailing-twelve-month revenue (TTM revenue). EBITDA multiples use earnings before interest, taxes, depreciation, and amortization instead. Neither denominator is interchangeable with annualized run-rate revenue. A reported 11.7x EV/EBITDA, for example, cannot be compared to a 4.0x EV/TTM revenue as though both price the same measure.
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What do the 2026 figures show—and why do they differ?
The figures below describe distinct markets, periods, and measurements. They are useful reference points, not prices that every SaaS business should expect.
| Figure | Market and population | Metric and statistic | Observation period |
|---|---|---|---|
| 3.21x, down from 5.58x | Public SaaS companies; iMerge says the calculation uses SaaS Capital Index constituents | Median revenue multiple; iMerge labels the public measure as revenue, so do not silently treat it as identical to every other revenue denominator | June 30, 2026, compared with December 31, 2025; iMerge Advisors, Q2 2026 analysis |
| 4.0x | Disclosed private SaaS M&A transactions; iMerge cites Software Equity Group’s 698 announced transactions | Median EV/TTM revenue among disclosed transactions | Q2 2026; iMerge Advisors |
| 11.8x for 20–30% growth; 8.5x for 30%+ growth | Public SaaS company growth cohorts | Median EV/TTM revenue | 2025 data in Software Equity Group’s 2026 report |
| 6.3x above 20% EBITDA margin; roughly 4.4x–4.6x below 20% | Public SaaS company profitability cohorts | Median EV/TTM revenue for the above-20% cohort; reported multiples for groups below 20% | 2025 data in Software Equity Group’s 2026 report |
| 11.7x, down from 20.4x | SaaS private-equity market | Median EV/EBITDA | H1 2026; Forvis Mazars and PitchBook |
Public trading is not a private-company sale
The 3.21x public median in iMerge’s Q2 analysis and its 4.0x disclosed private M&A median are different observations, not competing estimates of one universal price. The first describes public trading; the second describes disclosed private transactions and has a transaction-selection limitation. Private deals can reflect buyer fit, control, financing, and terms that do not apply to ordinary public share trading.
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Statistics also matter. A median identifies the middle observation; a mean can be pulled upward or downward by extreme companies. iMerge cautions that a cloud-index headline may report a mean rather than a median. Check the statistic before using a market figure to describe a “typical” company.
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Why a high growth cohort multiple is not a contradiction
Software Equity Group’s 2026 report uses 2025 public-company data and cohorts grouped by growth or EBITDA margin. Its 11.8x median for companies growing 20–30% and 8.5x for the 30%+ cohort are cohort observations, not a rule that faster growth reduces value. A cohort’s size, composition, and other company characteristics matter; the figures do not isolate growth as the cause of the difference. The same caution applies to the reported profitability cohorts.
Why are founders scrambling in 2026?
The evidence points to an uneven repricing and greater selectivity, not a uniform freeze. In iMerge’s analysis, the public median it reports fell from 5.58x at year-end 2025 to 3.21x on June 30, 2026. Forvis Mazars and PitchBook report a drop in median SaaS PE EV/EBITDA from 20.4x to 11.7x in H1 2026. Those declines concern different markets and denominators, but both illustrate pressure on benchmarks.
At the same time, Forvis Mazars and PitchBook report $439.7 billion in global SaaS M&A value in H1 2026, a decade-high aggregate that the release attributes to AI-driven strategic acquisitions. That total is not a typical deal size, proof that deal value was widely distributed, or evidence that every founder can find a buyer at a premium. Strategic interest in differentiated assets can coexist with tighter financing and lower broad-market multiples. The release also cites AI disruption risk alongside cost of capital and other forces; it does not establish AI as the sole cause of valuation changes.
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Forvis Mazars partner and national industry leader for technology and software Ricardo Martinez summarized the shift: “Investors are placing greater emphasis on profitability, cash flow, and competitive differentiation.” The comment appears in the Forvis Mazars release covering the H1 2026 State of SaaS report.
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What is my SaaS company worth?
The market figures cannot determine an individual company’s value. A defensible range starts with a clearly defined transaction or financing scenario, then tests relevant company and deal factors against appropriately matched evidence. Growth and profitability show up in the cited cohorts, but no single metric guarantees a multiple.
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Build a company-specific range, not a one-number answer
- Define the event: distinguish a public-market reference, a minority investment, and a control sale. They price different rights, liquidity, and deal circumstances.
- Choose the denominator: state whether the comparison uses annualized current run-rate revenue, TTM revenue, ARR, or EBITDA. Do not substitute one for another without a justified bridge.
- Match the comparison set: consider company scale, geography, recurring-revenue quality, growth, retention, margins, buyer type, and reporting period. The sources substantiate the importance of growth, scale, cash flow, and differentiation, but do not establish fixed premiums for individual traits.
- Use more than one reference carefully: public indices offer a market-trading lens; disclosed M&A data reflect selected completed or announced transactions; survey data describe respondents rather than all private SaaS firms. Treat each as context, not a plug-in answer.
- Stress-test the assumptions: show how the range changes under different growth, margin, and buyer scenarios, and state which assumptions are observed evidence versus company-specific judgment.
Keep operating benchmarks separate from valuation
SaaS Capital’s 2026 survey reports a 22% median growth rate among surveyed private B2B SaaS companies, down from 25% in 2024. It also reports that 7.3% of surveyed companies had flat or negative growth in 2025, compared with 6.9% in the prior year and 13% in 2020. These are operating benchmarks, not valuation multiples; the surveyed companies are not a universal census of private SaaS businesses.
What the available benchmarks cannot tell you
- They do not establish one all-stage, all-geography private SaaS median.
- The 4.0x private M&A figure applies to disclosed transactions cited by iMerge, not every private SaaS company or every deal.
- The available SaaS Capital 2026 research page provides survey snippets and describes valuation methodology, but does not establish a private-company valuation formula or private multiple in the material cited here.
- The public growth and profitability cohorts show relationships within specific datasets; they do not prove that changing one metric alone will produce the cohort’s multiple.
For a founder, the useful conclusion is not to pick whichever headline multiple looks most attractive. It is to compare like with like, make the denominator and transaction type explicit, and treat market data as evidence for a range—not a promise about the outcome.
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