AI revenue keeps 50 to 60 cents on the dollar, and buyers price it that way

A founder with a metered AI product and $50,000 a month in revenue almost always starts from a software multiple. Software trades at some number of times revenue, the reasoning goes, and revenue is revenue. Then the buyer's model comes back about a third lower and the founder concludes the buyer does not understand AI. The buyer usually understands the cost side better than the founder does. I price private companies for a living, more than a thousand of them, and the revenue-quality question is where most AI price gaps start. Revenue is only the top line of what a buyer is buying, and for an AI product the line underneath it is a different shape.

This essay is the arithmetic of that gap. It uses two named sources for the margins and the pricing models, and my own index for what small software actually clears at. It is not a forecast of where AI multiples will land: the AI-Native Valuation Benchmark covers how wide those are. This is about what a buyer does with the revenue once it is in the model.

The margin gap, from two named sources

Bessemer Venture Partners' AI pricing and monetization playbook, published in February 2026, puts it in one line: AI companies see 50 to 60 percent gross margins against 80 to 90 percent for SaaS. Bessemer draws that from its work with dozens of AI teams rather than from a survey, so read it as a practitioner range, not a measured median. ICONIQ's State of AI 2026 report supplies the direction. Built on a Q2 2026 survey of roughly 300 executives at software companies building AI products, it puts average gross margin on AI products at 45 percent in 2025, with a projection of 53 percent for 2026 and 59 percent for 2027.

Read the two together. Margins are improving, and a buyer should say so out loud. They are also still well short of the 80 to 90 percent that software multiples were built on. A buyer today is underwriting a business whose cost of revenue is closer to half than to a tenth, and whose margin is moving. Both facts belong in the price, and neither one is visible in a revenue multiple.

Why a revenue multiple hides it

A revenue multiple is a shortcut for a gross profit multiple, and the shortcut only works when every company in the comparable set keeps roughly the same share of each dollar. SaaS comparables did. AI products do not. Take illustrative midpoints of Bessemer's ranges: 85 cents of gross profit per revenue dollar for SaaS and 55 cents for an AI product. Pay the same eight times gross profit for both and a million dollars of revenue is worth $6.8 million as SaaS and $4.4 million as AI. The AI dollar is worth 35 percent less, about a third, and nobody has yet made a judgment about growth, retention or the model. It is division.

Run it the other way. A buyer who would pay 5 times revenue for the SaaS business is paying about 5.9 times its gross profit. Apply that same 5.9 times to the AI business and the revenue multiple falls to about 3.2. A seller who quotes the 5x software number is not asking for a premium on AI. They are asking the buyer to ignore 30 points of margin. Some buyers will, in a hot market, for a business growing fast enough. Most will not, and the ones who do are paying for growth, which is a separate line from the one in this essay.

What metered revenue does to the forecast

Margin is the first discount. Predictability is the second, and it is specific to how AI products charge. ICONIQ reports 42 percent of the companies it surveyed using consumption-based pricing and 23 percent using outcome-based pricing, with the average company running 1.7 pricing models at once. Most are blending a subscription base with a metered layer. Bessemer notes the same drift, saying many vertical AI companies are adopting hybrid models. The blend is sensible for the seller. It is also harder for a buyer to underwrite, because the metered layer is the part of revenue that nobody has contracted.

Subscription revenue has a renewal date, a churn rate and a contract. Metered revenue has a usage curve. A buyer cannot tell from last quarter's invoice whether a customer's usage is a plateau, a spike from one project, or the start of a decline. So the way I split it is by layer, and each layer gets its own multiple: the committed subscription floor, metered usage with a history long enough to show a seasonal pattern, and usage that is under twelve months old. The floor earns the software multiple. The other two earn a discount that grows as the history gets shorter.

For scale on what good retention looks like at the small end of software, SaaS Capital's 2026 benchmarking of more than 1,000 private B2B SaaS companies describes its bootstrapped cohort, companies with $3 million to $20 million of ARR, at 103 percent net revenue retention and 91 percent gross revenue retention. A metered AI product that cannot show a comparable record, by customer and by month, is asking to be priced on the thin side of that cohort. The ask is not unreasonable. It just has to be evidenced, and the evidence is a cohort table, not a growth chart.

How the gap lands on earnings

Small buyers do not price on revenue or on gross profit. They price on seller's discretionary earnings, which is the number left after the inference bill is paid. So the margin gap lands directly, and it lands larger, because the fixed cost of running the business does not shrink when the margin does. Here is an illustrative pair. Both products bill $600,000 a year and carry $60,000 of operating cost that is not the owner's pay: tools, a support contractor, accounting. The first is a flat-subscription tool at an 85 percent gross margin. The second is a metered AI product at 55 percent.

The first throws off $510,000 of gross profit and $450,000 of SDE. The second throws off $330,000 of gross profit and $270,000 of SDE. In my Felpel Private Company Index, the micro and online SaaS cell holds 523 deals and clears at a median of 2.86 times SDE. At that multiple the pair prices at about $1.29 million and $772,000, which is 2.1 times and 1.3 times revenue. Gross profit fell 35 percent. SDE and price fell 40 percent, because the $60,000 of fixed cost did not move.

Two cautions on the example. The index has no AI-specific cell. Its software cell is micro and online SaaS, so 2.86 times is a reference point for the arithmetic, not a measured AI multiple. And if the AI product earns a higher multiple on growth, the margin gap still has to be overcome before the growth is paid for. The direction of the adjustment does not change. Only its size does.

Margins are moving, and a buyer has to decide what to do with that

The ICONIQ path from 45 percent to a projected 59 percent is the seller's best argument, and it is a real one. If inference costs are falling, a buyer paying for today's margin is overpaying for the discount. My answer is that a buyer pays for the margin it can verify and nothing beyond it. A projection on a slide is not verification. A monthly cost-per-unit series, tied to the model provider's invoices, showing the cost curve already bending, is. The first earns nothing. The second earns a partial credit that grows with the length of the record.

There is a second-order point sellers tend to skip. When a cost falls for one company it usually falls for its competitors too, and some of the saving gets competed away in price. Whether a given product keeps the saving depends on whether customers pay for the outcome or for the usage. That is a judgment, not a published statistic, and a careful buyer will ask for evidence of pricing power before crediting any margin expansion to the seller.

What a buyer will ask for

Five requests come up in nearly every AI product diligence, and they are the same questions a quality of earnings review asks of any business, pointed at a new cost line. First, cost per unit of usage by month, reconciled to the provider invoices. Second, gross margin by customer, because the average hides a few heavy users who are under water. Third, how much revenue is committed as minimums, and for how long. Fourth, provider concentration. One model vendor's price list is a supplier risk a classic SaaS company never carried, and a buyer will price a single-vendor dependency. Fifth, pass-through, meaning whether the product's prices can move when the cost does.

If you are selling, or buying

If you are selling, do not lead with the revenue multiple. Lead with the margin trend on its own line, the committed floor, and the cost-per-unit series. Quote revenue only after the buyer has seen those, because a buyer who finds the margin gap on their own will assume there are others. If you are buying, ask for the same three items and price the layers separately. Do not pay a software multiple for revenue that does not have software margins, and do not discount an improving margin to zero when the record shows it improving.

The takeaway

The mistake in AI pricing is rarely the multiple. It is the base. Revenue means the same thing across SaaS comparables because the margins match, and it stops meaning the same thing the moment cost of revenue moves from a tenth to nearly half. Reprice on gross profit, then on earnings, then ask how much of the revenue is committed. Do it in that order and the gap between the ask and the offer stops being a mystery and becomes a list of line items. That list is the one you can run on your own numbers in Value Alpha.

Common questions

Why is AI revenue worth less than SaaS revenue at the same multiple?

Because a revenue multiple assumes every comparable keeps about the same share of each dollar. Bessemer Venture Partners reports AI companies at 50 to 60 percent gross margins against 80 to 90 percent for SaaS. At the same multiple of gross profit, a dollar of AI revenue at 55 cents of margin is worth about 35 percent less than a SaaS dollar at 85 cents. The gap is arithmetic, before any view on growth or retention.

What gross margin do AI companies have compared with SaaS?

Bessemer's February 2026 playbook puts AI companies at 50 to 60 percent gross margins against 80 to 90 percent for SaaS. ICONIQ's State of AI 2026 report puts average AI product gross margin at 45 percent in 2025, with a projection of 53 percent for 2026 and 59 percent for 2027, based on a Q2 2026 survey of about 300 executives. Margins are improving but remain below software norms.

How do buyers value usage-based or metered revenue?

They split it into layers. The committed subscription floor earns a software multiple. Metered usage with a long enough history to show seasonality earns a discount. Usage under twelve months old earns a larger one. The reason is forecasting: subscription revenue has a renewal date and a contract, while metered revenue follows a usage curve a buyer cannot read from one quarter of invoices. Cohort tables by customer and month are the evidence that closes the gap.

Should an AI company be valued on revenue, gross profit or earnings?

All three, in that order of scrutiny. Revenue is the headline, gross profit shows what the cost of inference does to it, and earnings show the result after fixed costs. Small buyers price on seller's discretionary earnings, where the margin gap is larger than on gross profit because fixed costs do not shrink. In the illustrative example, a 35 percent gross profit gap became a 40 percent gap in SDE and price.

Will AI margins catch up to SaaS margins, and does that raise the price?

ICONIQ projects average AI product gross margin rising from 45 percent in 2025 to 59 percent in 2027, still below the 80 to 90 percent Bessemer cites for SaaS. A buyer pays for the margin it can verify, not the projection. A monthly cost-per-unit series tied to provider invoices earns partial credit that grows with the length of the record. Evidence of pricing power matters too, since savings can be competed away.

Related reading

• The AI-Native Valuation Benchmark: where comps break for AI companies.
• Micro-SaaS sells for 2.86x SDE, not 4x ARR.
• Quality of earnings: the report that moves the price.
• What acquirers really pay for when they buy your company.

Go deeper

If you are pricing, buying or selling a metered AI product and the ask and the offer are far apart, let's split the revenue into layers and run the margin math on your actual numbers before you respond.

Tomasz Felpel is an investor, founder, and advisor in private markets and healthcare, based in New York. He is a three-time founder of Value Alpha, an AI-powered private-markets valuation platform, Sonnerie VC, an early-stage healthcare venture firm, and Pond. Previously he led corporate development and M&A at Fortune 500 scale, pricing more than 1,000 private companies. Columbia Business School EMBA. Read the full story.