AI is bringing cost-to-serve to SaaS

AI changes the math

Flagsmith, a feature management SaaS company, published their real infrastructure numbers last year. When they were small, one new B2B2C customer didn't just add one user to their system — it added all of that customer's customers. One signature could mean 10x the API load overnight.

Their infrastructure went from $40 a month serving 20 million requests to $1,200 a month serving billions. The per-customer cost wasn't flat at all. It varied by orders of magnitude depending on what that customer actually did with the product.

That was before AI.

The 80% gross margin that investors expect from SaaS is breaking down for any company with meaningful AI workloads. SaaS companies are discovering variable cost-to-serve for the first time. The distribution industry has been living with it since the 1980s.

Distribution knew first

In distribution, every customer has always had a different cost-to-serve. One customer places a single large order monthly, pays on time, and picks up at the warehouse. Another places 50 small orders, demands next-day delivery, returns 10% of what they buy, calls support weekly, and pays 60 days late. They might generate the same gross margin on paper. One is profitable. The other is destroying money.

In 1989, a Swedish manufacturer called Kanthal hired consultants to calculate this for the first time. They found that only 40% of their customers were profitable, and that their single largest customer — the one the sales team considered the crown jewel — was the biggest money-loser in the company.

Harvard professor Robert Kaplan wrote the case. It launched an entire field of research. Thirty-seven years of data since then, across hundreds of companies, consistently shows the same pattern: the top 20% of customers generate 150-300% of total profits. The middle 60% break even. The bottom 20% destroy 50-200%.

The math is straightforward: when cost-to-serve varies by customer and pricing doesn't reflect it, some customers silently subsidize others. Your best customers pay for your worst ones and nobody sees it happening.

The cost-side whale curve

SaaS was protected from this because the cost side was flat. AI changed that.

When one customer's usage triggers 100x the inference cost of another, but both pay the same monthly subscription, you have a whale curve forming on the cost side. When enterprise deals stack discounts (8% list discount + 2% volume rebate + extended payment terms + free premium support + custom integrations), a deal that looks like 24% gross margin on paper can drop to single digits once you account for the real cost of serving that account.

A SaaS founder on Reddit described the math in practice. He had been charging $49 a month for three years. When he finally calculated the support cost per customer, his $49 customers generated the same ticket volume as customers paying $149 at competitors. Same onboarding time. Same feature requests. Same everything except revenue.

He raised prices to $79. Lost 12% of customers over three months. Profit increased 67%.

The "affordable positioning" I'd been protecting was actually just fear of rejection dressed up as strategy.— SaaS founder, Reddit r/SaaS

The customers who left were the most price-sensitive, highest-support, lowest-expansion segment. The customers who stayed were more serious about outcomes and less likely to churn.

Same trap, different vocabulary

The distribution industry learned a version of this lesson decades ago. Most of them still haven't fully acted on it, even with 37 years of data.

A 2025 survey by Baringa of 100+ PE-backed B2B SaaS companies found that 68% haven't adjusted SME product prices since 2020. Net price variation of 30-50% is common due to uncontrolled discounting. Sales teams prioritize volume over profitability. The pattern is identical.

In distribution, SPARXiQ found that most companies sacrifice one-third to one-half of their operating profits to customers they can't even identify as unprofitable. Not because they don't care, but because the data isn't visible. The ERP shows gross margin. Gross margin is a lie when cost-to-serve varies by customer.

SaaS is heading into the same trap. Most SaaS companies know their overall gross margin. Very few know it per customer. CloudZero found that spreading costs evenly across all customers — what they call "peanut butter allocation" — is the norm, not the exception. Finance teams can see total spend on EC2 or S3, but they can't see how much it costs to support each customer. That means unprofitable contracts and draining usage patterns go unnoticed.

Without per-customer cost visibility, you can't price accurately, you can't identify which accounts are destroying margin, and you can't have an honest conversation about which customers to invest in and which to reprice or let go.

The profitability illusion

TSIA, the technology services research association, has been studying SaaS profitability for over a decade. Their finding: a generation of SaaS executives has never managed a GAAP-profitable business. They grew up in an era where investors rewarded growth over profit, where customer success and support were provided for free and buried in subscription COGS, where professional services ran at a loss to accelerate adoption.

Thomas Lah, TSIA's executive director, documented how the largest SaaS companies generate operating incomes equivalent to low-margin retailers — despite selling software. Salesforce, with $30 billion in revenue, still struggled to post positive net operating income. The structural problem: SaaS collapsed three revenue streams (license, maintenance, professional services) into one subscription line, losing the ability to monetize each component appropriately.

Jason Cohen, founder of WP Engine, did the per-customer math in a widely-cited analysis. At typical enterprise SaaS metrics (18-month CAC payback, 75% annual retention, 70% gross margin, 15% R&D, 15% admin), a customer generates $4 of revenue over their lifetime. After acquisition costs, cost-to-serve, R&D, and admin: $0.10 in profit. One-fortieth of revenue. And that's without any growth.

The bottleneck

AI makes per-customer cost-to-serve unavoidable for SaaS. The costs are real, variable, and large enough to matter.

Distribution's experience with the same problem is worth keeping in mind: knowing and fixing are completely different things.

In 1979, a distribution executive named Bruce Merrifield asked 150 executives at an industry conference: "How many of you have customers that cost more to serve than the margin they generate?" Every hand went up. "How many of you will do something about it?" Not a single hand.

In 2026, Distribution Strategy Group surveyed 84 distributors and found the same pattern: most measure profitability, far fewer act on what they learn.

SaaS has the advantage of better data infrastructure. Most companies already track usage, support tickets, infrastructure consumption. The raw material for per-customer cost-to-serve is there. What's missing is the same thing that's been missing in distribution for 37 years: someone inside the company who decides this is their problem and stays on it.

That SaaS founder who raised prices 40% and saw profit jump 67% didn't need better analytics. He needed to stop being afraid of the answer.

Most companies won't look. The ones who do tend to find money they didn't know they were losing. The rest keep selling jackets at cost.