How Pricing Puzzle Exposes AI Companies to Revenue Risks

Many businesses are jumping on the AI wave, using tools like OpenAI, Cursor, and other GenAI platforms. The promise is faster work, higher productivity, and automation at a fraction of the cost of human labour. But behind the excitement, there is a growing issue.AI is not as cheap or predictable as it first appears.

At first, these tools look like a bargain. Some even seem free, with generous trial plans or “unlimited” claims. But once real business use begins, the hidden costs show up. Companies face strict usage limits, unpredictable bills, and sudden slowdowns unless they pay more. What seems like an affordable tool turns into a financial burden for some firms.

Business Insider reported that AI coding platforms like Anthropic’s Claude Code have encountered a phenomenon known as “inference whales” — users who execute massive volumes of automated code inference under flat-rate subscriptions intended for average usage. One striking case involved a single user generating $35,000 worth of inference usage, while paying only $200 per month.

The Pricing Paradox

In SaaS, revenue grows faster than costs. In GenAI, it’s the opposite. Usage can scale costs faster than revenue if pricing is misaligned.

According to a blog post by Orb.,“In SaaS, high usage is a victory; in GenAI, high usage without the right pricing model can become a liability.”

This paradox exposes companies to hidden risks, the blog states. Many restructure workflows, automate jobs, or even downsize staff around these tools, only to discover rising, volatile AI bills. Once locked in, switching becomes costly, giving AI vendors pricing power as they move away from VC-subsidised models toward profitability.

Similar cycles have also been observed in cloud infrastructure, CRM, and digital ads where early affordability was followed by dependence, then rising costs as platforms consolidate power.

A blog by EdwardsSchoen, a marketing agency that focuses on digital media and consulting services, mentioned that digital ad platforms like Google and Meta once offered unparalleled reach at low cost. Now, costs are skyrocketing. For example, Meta/Facebook’s CPM rose by 61% year over year; TikTok’s CPM surged by 185%, and Google search CPC climbed 22% from 2022 to late 2024.

The blog claimed that GenAI is on the same trajectory.

Furthermore, traditional SaaS relied on per-seat or flat-rate pricing for predictability. But AI workloads are compute-heavy and scale dynamically. Vendors offering unlimited-use plans often lose money on high-volume customers, forcing a shift toward usage-based or outcome-based pricing.

While more sustainable for vendors, these models introduce billing volatility for customers.

The Investor’s Lens

For founders and investors, pricing sustainability is existential. 

Ranjeet Shetye, venture partner at Yournest Venture Capital and MD at Everstream Analytics, told AIM that he doesn’t look at pricing sustainability as a line in a spreadsheet, but as a survival metric. “In GenAI, your infrastructure bill starts the day you spin up your first model, and it scales faster than revenue if you don’t get it right. I’ve seen companies with great technology stumble because they underestimated what cloud GPU hours or data pipelines would do to their gross margins.”

He added that the scale of infrastructure investment underscores the stakes. Between 2025 and 2030, AI-related data centre capacity investments are projected to reach $3.7 to $7.9 trillion. In 2025 alone, Amazon plans to spend $100 billion on cloud and data centres, with other hyperscalers collectively adding over $300 billion. These costs will flow downstream into enterprise contracts.

“When we evaluate GenAI founders, it’s about more than flashy demos; we want to see hard questions being asked early: How will costs change at 10x scale? Can you benchmark against current GPU pricing? Do you have strategies for tiering workloads, fine-tuning smaller models, or negotiating enterprise cloud terms? These aren’t theoretical — they determine whether you can survive the next funding cycle,” Shetye explained.

Deflationary Costs, Rising Dependencies

Not everyone sees cost escalation as a threat. Many point to rapid cost deflation in AI infrastructure.

Arjun Gandhi, technology investor at Nexus Venture Partners, told AIM,“In most cases, we don’t think about infrastructure costs a whole lot since it is deflationary and will come down with scale and time.” 

However, they do investigate when AI startups are selling at negative margins and price is a key differentiator, he said. “Everyone is betting that computing costs will come down over time, and with that, margins will increase.”

The data supports this. LLM inference costs have fallen 10x since 2023, according to Epoch AI. While still more expensive than SaaS-era workloads, AI is approaching cost levels that can be justified by the outcomes it enables.

Talking about the same to AIM, Himanshu Gahlot, VP of engineering, Apollo.io explained, “Even traditional SaaS usage was based on API calls and infrastructure costs. With AI, these APIs are more expensive due to LLM processing, but the outcomes are also magical — things that weren’t possible before. Users will be willing to pay for that, even if it sounds costly now. If your competitor is extracting more value from AI, you don’t want to be left behind.”

Who will Thrive?

For strategy and investment leaders, the challenge with AI is not its affordability today but the fragility of that affordability. Subsidised pricing and falling unit costs hide the real risks of dependency and pricing volatility. 

The companies that succeed will be those that model AI costs at scale rather than at the pilot stage, benchmark against infrastructure pricing trends to plan for multiple futures, diversify vendors and architectures to avoid lock-in, and tie AI usage to mission-critical outcomes instead of discretionary features. 

Shetye noted that pricing isn’t just a budgeting concern — it’s a matter of survival. And as GenAI moves from subsidised growth to sustainable economics, survival will depend on disciplined strategy, not magical thinking

The post How Pricing Puzzle Exposes AI Companies to Revenue Risks  appeared first on Analytics India Magazine.

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