Bootstrapped Software Valuation Multiples

Every credible valuation multiple has a paper trail. When a buyer offers "4x EBITDA" for a software business, that number did not appear from intuition: it is what comparable businesses of that size have sold for, which sits far below what the stock market pays for listed software companies. This article walks the full chain, from the named public benchmark to the private deal data and a defensible number, and embeds the worked range from a real engine so the math cannot drift from the prose.

1. Where a multiple actually comes from

A valuation multiple is shorthand for a discounted-cash-flow argument. "4x EBITDA" is a compressed way of saying: given this business's growth, margin, risk, and reinvestment needs, a buyer expects to recover their money over roughly four years of current earnings, plus upside. You can derive that from first principles, but in practice almost everyone anchors on observed multiples in comparable transactions, then adjusts.

The defensible version has three steps:

• Know the public benchmark. What do investors pay, per dollar of revenue or earnings, for listed companies in the sector? It is the ceiling for the asset class.
• Find the private comparables. What have businesses of your size and sector actually sold for? Broker and advisor surveys publish these multiples; they are the price, and the gap to the public figure is the private discount.
• Reconcile across bases. Compute SDE and EBITDA values separately, then blend. A single base in isolation either flatters or punishes the number.

Skipping step one leaves you unable to explain why software commands a premium. Skipping step two is how sellers quote a public-company multiple on a private business, which fails in due diligence.

2. The public benchmark: Damodaran's sector data

The standard public benchmark is Aswath Damodaran's sector-multiples dataset, published free on his NYU Stern faculty pages and refreshed annually (the current vintage is labelled as of January 2026)^[3]. It is the most frequently cited public source for sector multiples precisely because it is transparent, free, and updated on a predictable cadence.

For software, the January 2026 vintage reports (US listed firms; the EBITDA multiples are across firms with positive EBITDA):

• Software (System & Application): EV/EBITDA 24.48x, EV/EBIT 32.41x^[1]; EV/Sales 11.41x, Price/Sales 11.01x^[2].
• Software (Internet): EV/EBITDA 30.26x^[1]; EV/Sales 9.56x^[2].
• Total Market (all US firms): EV/EBITDA 19.73x^[1]; EV/Sales 3.97x^[2].

Two things matter here. First, software trades at a premium to the broad market on both bases (24.48x vs 19.73x on EBITDA; 11.41x vs 3.97x on sales) because investors price its scalability and margin structure. Second, and this is the part founders skip: these are companies that are large, liquid, audited, and run by management teams. A bootstrapped SaaS at $600k revenue is the opposite of every one of those.

So Damodaran's 24.48x is the correct ceiling, not the correct multiple. It tells you what the asset class commands at the top of the size-and-liquidity ladder.

Damodaran's software multiple by year

The ceiling moves with the stock market, so a figure is only meaningful with its edition. Each January edition of the dataset, read from Damodaran's own spreadsheets (Software (System & Application), US listed firms with positive EBITDA)^[1][10]:

Edition	Software EV/EBITDA	Total market EV/EBITDA	Software firms
January 2021	30.42x	20.02x	388
January 2022	32.72x	18.19x	375
January 2023	21.33x	14.23x	390
January 2024	28.43x	16.81x	351
January 2025	27.98x	18.60x	333
January 2026	24.48x	19.73x	309

Across these editions the software multiple ran from 21.33x (January 2023) to 32.72x (January 2022). The largest one-year move was 32.72x to 21.33x, between the January 2022 and January 2023 editions. A figure from an older edition can be several turns of EBITDA away from the January 2026 ceiling of 24.48x; the private-deal multiples in the next section are the price either way.

3. The private discount, measured

Four differences separate a public software company from a bootstrapped one, and each lowers what a buyer will pay:

• Size. A $600k-revenue business cannot be compared to a $500M one. Smaller firms carry more idiosyncratic risk per dollar of earnings and have fewer buyers.
• Illiquidity (marketability). A public-company shareholder can sell in seconds. A private-business owner runs a sale process that takes months, with no guarantee of a buyer.
• Customer concentration. Public software companies have thousands of customers. A bootstrapped SaaS often has a handful of accounts carrying a large slice of revenue, a risk a buyer prices in.
• Key-person dependence. If the founder is the product, the support, and the sales team, the business may not transfer cleanly. Buyers discount it or require a retention or earn-out structure.

How much those differences cost is not a percentage to pick; it shows up in what private businesses sell for. Two public sources report it:

• By deal size (IBBA and M&A Source Market Pulse, Q1 2026, multiples reported by business-sale advisors)^[6]: businesses selling for under $500,000 at 2.0x SDE, $500,000 to $1 million at 2.8x, $1 million to $2 million at 3.3x; from $2 million the survey reports EBITDA multiples, 4.0x for $2 million to $5 million and 4.5x for $5 million to $50 million.
• By sector (BizBuySell, businesses sold through its marketplace from 2021 through 2025)^[7][9]: software, app, and SaaS businesses averaged 3.31x SDE and IT and software services 3.10x; across all businesses sold in 2025 the average was 2.61x^[8].

Against Damodaran's 24.48x, the 4.0x to 4.5x private EBITDA multiples are 82% to 84% lower. That gap is the private discount, and it is measured, not assumed. Two more readings follow from the data. Software earns a premium in private markets too (3.31x SDE for software over 2021-2025, against 2.61x for all businesses sold in 2025). And size moves the multiple at every step of the ladder, so a business that will sell for under $1 million should expect the low end.

4. Worked example: a $600k-revenue bootstrapped SaaS

Scenario: a bootstrapped, profitable B2B SaaS. Annual revenue $600,000. Seller's Discretionary Earnings (SDE) $280,000 (the founder's all-in cash benefit). EBITDA $220,000 (lower than SDE because EBITDA does not add back the founder's salary). We apply the private multiples from section 3: 3.31x SDE (BizBuySell's 2021-2025 average for software, app, and SaaS businesses) and 4.0x EBITDA (IBBA's lowest EBITDA band; this business would sell for less than $2 million, so 4.0x is the top of what the data supports). The revenue method is left out: the private-deal data for this size prices profitable businesses on earnings, so there is no comparable revenue multiple to apply.

The engine returns two method midpoints: SDE Multiple 3.31x × $280,000 = $926,800 ($741,440 to $1,112,160); EBITDA Multiple 4.0x × $220,000 = $880,000 ($704,000 to $1,056,000). The blended midpoint is $903,400 on a $722,720 to $1,084,080 range.

Notice what the chain produced. Each midpoint is a published private-deal multiple applied to a real earnings figure, and the two agree within 6%. A buyer can argue the business sits lower in the range: at a price under $1 million, IBBA's size band is 2.8x SDE, which gives $784,000. That argument is about where in the data the business belongs, which is the negotiation you want, rather than about where a number came from.

5. Revenue multiple vs earnings multiple

Revenue multiples are the lens for high-growth or pre-profit software, where current earnings understate what the business will earn. Damodaran's public software EV/Sales of 11.41x^[2] is the listed-company version. For a profitable, owner-run business at this size, buyers price the earnings, which is why the private-deal surveys report SDE and EBITDA multiples. The blended $903,400 works out to about 1.5x revenue: a useful cross-check against a buyer quoting a revenue multiple, not a method of its own.

Between the earnings bases, SDE is the operator-buyer's lens: it adds the founder's salary back, because the buyer will do the founder's job. EBITDA prices the business after paying market-rate management, which suits a buyer who will hire an operator. Here the two land close ($926,800 against $880,000), so the blend is a fair asking number. When they diverge, lead with the base that matches the buyer in front of you; the deeper treatment by buyer profile is in How to Value a Small Business^[5].

6. How to anchor a number you can defend

A multiple you can defend in due diligence has three properties, all visible in the worked example.

It names its sources. "4.0x EBITDA" defended as "IBBA's Q1 2026 multiple for $2 million to $5 million deals, against Damodaran's 24.48x for listed software" is a position a buyer can engage with. A bare "4x" is a position they can only haggle.

It places the business in the data. Deal size and sector both move the multiple. Saying which size band and which sector average the business belongs to, and why, pre-answers the first questions a buyer will ask in diligence.

It reconciles across bases. Computing SDE and EBITDA values and blending them is what produces the $903,400. A single base is either the seller's best case or the buyer's best case; the blend is the number both can map to their preferred lens. The blended-range methodology is documented at the Business Valuation Calculator methodology page^[4].

The sources refresh on their own schedules: Damodaran each January, the IBBA survey every quarter, BizBuySell's sector averages every year. The chain does not change: know the public ceiling, find the private comparables for your size and sector, reconcile across bases, and quote the blend. A number built that way is one you can stand behind across the table.

Frequently asked questions

What EBITDA multiple should a bootstrapped software business use?

The private-deal multiple for its size, not the public one. NYU Stern's Damodaran dataset (January 2026) puts listed Software (System & Application) at 24.48x EV/EBITDA across firms with positive EBITDA. Private deals price far lower: in the IBBA and M&A Source Market Pulse for Q1 2026, advisors reported 4.0x EBITDA for businesses selling for $2 million to $5 million and 4.5x for $5 million to $50 million. Below $2 million, deals are priced on SDE instead: 3.3x for $1 million to $2 million and 2.8x for $500,000 to $1 million. Software, app, and SaaS businesses sold through BizBuySell from 2021 through 2025 averaged 3.31x SDE. In the worked example, 4.0x EBITDA on $220,000 gives an $880,000 midpoint.

Why can't I just use Damodaran's software multiple directly?

Because Damodaran's sector multiples describe publicly traded companies: liquid (the stock can be sold tomorrow), large, audited, and run by a management team that survives the founder leaving. A bootstrapped business is none of those, and the deal data shows the cost. The 4.0x to 4.5x EBITDA multiples IBBA advisors reported for private deals in Q1 2026 are 82% to 84% below the public 24.48x. Use Damodaran's figure as the ceiling for the asset class, and the private-deal data for the price.

Should a bootstrapped SaaS use a revenue multiple or an earnings multiple?

For a profitable owner-run business, an earnings multiple. The private-deal data for this size (IBBA by purchase price, BizBuySell by sector) is reported as multiples of SDE and EBITDA, because buyers of small profitable businesses price the cash they keep. Revenue multiples (Damodaran lists listed Software (System & Application) at 11.41x EV/Sales) are the lens for high-growth or pre-profit software, where current earnings understate value. In the worked example the earnings methods give a $903,400 blended midpoint, about 1.5x revenue; quote that as a cross-check, not as a method.

Does Damodaran update these multiples, and are they free?

Yes and yes. Damodaran (NYU Stern) refreshes the US sector datasets annually, typically in January, and publishes them free on his Stern faculty pages. The current vintage is labelled as of January 2026. The datasets are widely cited precisely because they are public, methodologically transparent, and updated on a predictable cadence, which is why they are the standard public benchmark even though they describe large listed firms.