Our calculators give planning-stage estimates. This page shows what they are built on: the public and private-market data behind every number, and where we have made a modelling choice rather than quoted a source.
Sources reviewed July 2026.
The calculators are built to start a conversation, not to close one. It helps to be precise about the line between the two.
Every number is grounded in public and private-market data. We keep two things separate, and we tell you which is which.
Multiple levels that a named third party publishes. For example, GF Data reports an average of 5.9x EBITDA for deals with an enterprise value between $10M and $25M. We quote these with the source and the date.
Any value we smooth, interpolate, or index to a reference point. No public source publishes a revenue-indexed size curve, an early-stage multiple by growth tier, or a single factor per country. Where we build one, we label it as an Alehar calibration and show the sourced levels it was built from.
The structure of the calculator, a smooth size curve, per-country factors, and growth tiers, is an Alehar modelling choice. The inputs to that structure are sourced. We do not imply that any third party publishes our grid cell by cell.
One caveat governs everything below. Most public multiple data is either public-company trading multiples, which sit well above what small private companies transact at, or transaction data quoted on different earnings bases: seller’s discretionary earnings for the smallest businesses, EBITDA above roughly $2M. A small private company does not receive a public-company multiple, and every reference below already accounts for that.
The single largest driver of a small company’s multiple is its size. As revenue falls, the multiple falls with it, and below roughly $2M in revenue the market prices on seller’s discretionary earnings rather than EBITDA. The curve below indexes each revenue point to a lower-middle-market anchor at $20M, where a business trades around a mid-single-digit to high-single-digit EBITDA multiple. It is an Alehar calibration built from the sourced levels in the last column.
| Revenue | Factor vs $20M anchor | What sits behind it |
|---|---|---|
| $100K | 0.35 | Micro and Main Street floor. BizBuySell reports a median near 2.6x SDE and 0.69x revenue, and little of the value transfers to a new owner. |
| $500K | 0.44 | Small owner-operated business. IBBA segments below $1M transact around 2.3x to 2.5x SDE. |
| $1M | 0.52 | BizBuySell’s median deal sits here, at about 2.6x SDE and 0.69x revenue. |
| $2M | 0.62 | The point where the market shifts from pricing SDE to pricing EBITDA, as a buyer begins to assume a hired manager rather than an owner-operator. |
| $5M | 0.78 | GF Data reports 5.5x EBITDA for deals from $1M to $5M. A $5M-revenue business typically closes closer to 4x to 5x, not the full lower-middle-market multiple. |
| $10M | 0.90 | GF Data reports 5.9x EBITDA for the $10M to $25M bucket, and IBBA puts the $5M to $50M range near 6.5x. |
| $20M | 1.00 | The lower-middle-market anchor. GF Data's $10M to $25M bucket at 5.9x and published full-year 2025 deal averages around 7.2x set the reference point. |
| $50M | 1.10 | Larger lower-middle-market deals earn a modest premium. GF Data reports 6.6x for $25M to $50M and 8.7x for $50M to $100M. |
The factor is a relative multiple, not a multiple itself. It scales the anchor up or down. The curve is smooth by construction, which avoids sharp jumps at round-number revenue thresholds.
For businesses that run at or around break-even, and for profitable businesses whose earnings are still small relative to revenue, we price on ARR or trailing revenue rather than EBITDA, because a meaningful earnings multiple needs meaningful earnings. Growth selects the tier; recurring share and retention decide where in it you land.
| Growth rate | ARR multiple | What sits behind it |
|---|---|---|
| Under 30% | 1.8-2.8x | Acquire.com reports a median near 2.6x trailing revenue, with published SaaS M&A datasets putting the first quartile at 2.4x. Slow-growth small SaaS sits at the bottom of the marketplace range. |
| 30% to 60% | 2.5-3.8x | Published SaaS M&A datasets report a current median near 3.1x and a long-run median of 4.5x. SaaS Capital predicts about 4.8x for a bootstrapped company. |
| 60% to 100% | 3.2-4.8x | Published deal data puts equity-backed high-growth SaaS at 5x to 8x, lifted by strong retention. |
| Over 100% | 4.0-6.5x | SaaS Capital reports 7x to 9x ARR for companies with a Rule of 40 above 50 and net revenue retention above 120%. |
The tier multiples cannot detach from the industry a business actually sells in: they are capped against that industry's own revenue range, so a break-even business anchors near the low end of what profitable peers trade for, with partial credit above it that grows with recurring share. Mostly non-recurring revenue is scaled down before the tiers apply.
These ranges assume genuine recurring revenue. Retention and profitability can move a business up or down a full tier.
The calculator prices a business on adjusted EBITDA and on revenue, then brings the two together the way an adviser weighs methods side by side. Three rules govern the synthesis.
Exceptional margins earn a premium, not an outlier. When the EBITDA-based price implies a revenue multiple above the industry's range, the excess earns only partial credit, and more of it the higher the recurring share, because durable revenue is what makes a rich margin believable.
A profitable business is never valued below what an identical break-even business would fetch on its revenue base. This keeps the estimate stable as earnings cross zero: a company does not lose most of its value by earning its first dollar.
When the two methods disagree, the displayed range leans toward the other method: halfway on the downside, more cautiously on the upside. The per-method figures stay visible alongside it, so you can always see what the synthesis was built from.
We apply one factor per country, flat across industries, and we are explicit about what each factor rests on. Some are informed by country risk premiums, a cost-of-capital adjustment that theory applies to every industry equally. Others reflect market liquidity and the depth of the local buyer pool, not risk. We do not claim a separate factor for each country-and-industry pair, because the public data is too noisy to support one honestly.
| Country | Factor vs United States | What the factor rests on |
|---|---|---|
| India | 0.78 | Risk-premium-informedThe country risk premium implies roughly 0.75, so the factor sits close to the theory. |
| Philippines | 0.75 | Risk-premium-informedThe country risk premium implies roughly 0.78. |
| United Kingdom | 0.95 | Risk-premium-informedThe country risk premium implies roughly 0.94. |
| Saudi Arabia | 0.83 | Cautious calibrationRisk premium alone implies roughly 0.94. We hold the factor lower as a deliberate caution, not because of country risk. |
| United Arab Emirates | 0.85 | Cautious calibrationRisk premium alone implies roughly 0.95. Held lower as a deliberate caution. |
| Singapore | 0.88 | Liquidity and market-depthRated Aaa, so country risk does not justify a discount below the United States. The factor reflects a smaller local exit market and buyer pool. |
| Netherlands | 0.91 | Liquidity and market-depthRated Aaa. Reflects buyer-pool depth and exit-market size, not country risk. |
| Germany | 0.93 | Liquidity and market-depthRated Aaa. Reflects buyer-pool depth and exit-market size, not country risk. |
| United States | 1.00 | AnchorThe reference point for every other factor. |
We flag this plainly. Singapore, the Netherlands, and Germany carry no country risk premium above the United States, so nothing about their credit risk justifies a discount. Their factors below 1.0 reflect liquidity and buyer depth, and we label them that way rather than presenting them as risk.
Renewable energy does not fit one corporate EBITDA multiple, so we split it in two.
Companies with real operating earnings: engineering, procurement and construction, operations and maintenance, and developers who build and rotate assets. Corporate EBITDA logic applies. Public renewable companies traded around 11x to 16x EV/EBITDA through 2024 to 2026. After a private and size discount, a small private services business sits around 5x to 9x, with a mid near 7x. This range is an Alehar calibration off the public band.
Operating solar or wind with a power purchase agreement, project-level debt, or an SPV structure is not valued on a business EBITDA multiple. The market values it on contracted cash flows and capacity. As an indication, operating assets with long-term PPAs transact around $1.0M to $1.8M per MW, and investors underwrite to a levered equity IRR of roughly 8% to 12% and a debt-service coverage ratio. For a project-level figure, the calculator points you to a direct conversation rather than returning a multiple.
The debt calculator returns an indicative senior-debt style capacity, expressed as a range of debt to EBITDA. It is deliberately conservative. It reflects the senior leverage a lender might extend to a stable lower-middle-market business, roughly 1.5x to 3.1x depending on country and industry, and lines up with GF Data and Calder lower-middle-market senior leverage. It is not the higher leverage seen in sponsor-backed private-credit deals, and it is not a lender commitment. Country differences are applied as a single, modest adjustment to a global benchmark, which is an internal heuristic rather than a separately sourced market figure for each country.
These outputs are indicative planning estimates, not a formal valuation, fairness opinion, lender offer, or investment recommendation. Alehar calibrates the calculator from maintained lower-middle-market industry benchmarks, public-company trading-multiple datasets such as Damodaran/NYU Stern, selected private-market M&A and SaaS datasets, and middle-market lending reports such as GF Data; we then adjust for company size, growth, profitability, business model, geography, asset base, and debt-service capacity. Actual valuation and debt capacity can move materially based on buyer appetite, revenue quality, customer concentration, working capital, management depth, lender policy, and transaction structure, so the result should be used as a conversation starter before a properly diligence-backed process.
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Perspectives on corporate finance, fundraising, and M&A, from the Alehar team.
