Why Selling GenAI Feels Nothing Like SaaS

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Earlier this week, at a roundtable hosted by Chargebee, a group of GenAI (AI) founders compared notes on a shared learning: building and selling AI products to businesses is not just harder than selling SaaS — it is "fundamentally different'. Many entered the market expecting to ride the SaaS (Software as a Service) playbook to success. They are now discovering it’s written in a different language.

Here are five reasons why:

1. The Vanishing Customer Profile

In SaaS, identifying the Ideal Customer Profile (ICP) is often straightforward: a clear job to be done, a known budget holder, and measurable ROI. In GenAI, that clarity dissolves. Everyone wants to “experiment with AI.” Few want to commit. Interest is high, intent is elusive, and trial usage often masks the absence of a real buyer.

2. The False Promise of PLG

Product-Led Growth — the darling of modern SaaS — is stumbling in the AI world. While PLG drives traffic and trials, it fails to convert at scale. AI products often require guided onboarding, enterprise integration, or explainability that self-serve models can’t offer. As a result, many AI startups are turning to Sales-Led Growth (SLG) — or hybrids of both — to make meaningful progress.

GenAI vs. SaaS (Image credit: OpenAI)


3. The Cost Curve Is Upside Down

In SaaS, once a customer is acquired, the marginal cost to serve them is typically low — a server here, a support ticket there. Not so in AI. Serving a single customer may incur real-time inference costs, often higher than the cost of acquisition. Every prompt is a line item, especially when startups must pay foundation model providers (OpenAI, Google, Anthropic, etc.) by the token (the smallest unit of text data that a language model uses to understand and generate text). In some cases, scale leads not to efficiency, but to insolvency.

4. Pricing Models Don’t Fit the Mold

SaaS thrives on predictability: per-user, per-month, annual commitments. AI breaks that model. Infrastructure costs scale with usage, but revenue often does not. Subscription models can become loss-making as power users drive up inference costs. Usage-based pricing, while more sustainable, creates sticker shock and volatility — two things CFOs despise. Outcome-based pricing has been floated but rarely lands. Some startups are reverting to per-seat pricing simply because it aligns with the buyer’s mental model, not because it matches the economic reality.

5. The Innovation Overtakes the Sales Cycle

SaaS products evolve gradually. AI products are built on LLMs that improve continuously — and unpredictably. A sales cycle that begins with GPT-4 may conclude just as GPT-4.5 renders your value proposition obsolete. For enterprise buyers, this moving target lengthens evaluations. For vendors, it demands constant reinvention.

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Where This Leaves Us

The pace of innovation in generative AI is breathtaking. But until cost structures stabilize and pricing models mature, many AI startups will continue to sell with SaaS slides while bleeding cash like infrastructure companies. The tools may be cutting-edge, but the business models remain blunt instruments. Eventually, AI companies will find firmer ground — but for now, many are skating on token-thin ice.

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