Your AI Product Is Probably Losing Money (You Just Don't See It Yet)

You launched an AI-powered product. Customers are signing up. Revenue is growing. Your dashboard shows healthy MRR.

But here's what your dashboard isn't showing you: some of your best customers are actually costing you more to serve than they're paying you.

Welcome to the economics of AI—where traditional SaaS metrics lie to you, and profitability can evaporate without anyone noticing until it's too late.

The Numbers That Should Terrify Every AI Founder

Let's start with some uncomfortable data from the industry giants who have billions to burn and still got this wrong.

OpenAI's $200/month ChatGPT Pro plan? CEO Sam Altman publicly admitted in January 2025 that they're losing money on it. His exact words: "People use it much more than we expected." This is a $200 monthly subscription running at a loss at the world's most well-funded AI company (Fortune).

GitHub Copilot at $10/month? Microsoft was reportedly losing an average of $20 per user per month, with heavy users costing up to $80 per month to serve (Wall Street Journal via Neowin).

The broader picture: According to Bessemer Venture Partners, AI companies average 50-60% gross margins compared to 80-90% for traditional SaaS. And fast-growing AI "Supernovas" often run at just 25% gross margins—with many operating at negative gross margins entirely (SaaStr).

If companies with this level of resources and expertise are getting unit economics wrong, what makes you think your AI product is different?

Why Traditional SaaS Metrics Fail for AI Products

Traditional SaaS was built on a beautiful economic premise: the marginal cost of serving one more customer approaches zero. You build once, deploy once, and every new customer is almost pure profit.

AI completely inverts this model.

Every query, every generation, every inference costs real money. The more your customers use your product—which is supposed to be a good thing—the more your costs scale. This creates what industry veteran Dave Kellogg describes as a shift from the "movie business model" (invest upfront, distribute for free) to something closer to manufacturing, where COGS matters again (Monetizely).

Here's what this means in practice:

A customer service platform charging $0.99 per resolved ticket found that simple questions cost them $0.04 in resources while complex issues cost $2.80. Their average margin was healthy at 60%, but their most engaged customers—the ones you'd typically celebrate—were actually generating losses (Paid.ai).

A fintech chatbot was burning $400 per day for a single enterprise client, despite feeling like a reasonable AI implementation (Pilot).

The customers who love your product the most might be the ones destroying your unit economics.

The Visibility Gap: You Can't Fix What You Can't See

Here's where it gets worse: most companies don't even know they have this problem.

According to the 2025 State of AI Cost Management report:

• 80% of enterprises miss their AI infrastructure forecasts by more than 25%
• 84% report significant gross margin erosion tied to AI workloads
• Only 15% can forecast AI costs within ±10% (Mavvrik)

Another study from CloudZero found that only 51% of organizations can confidently evaluate AI ROI, with the primary obstacle being difficulty attributing costs to the correct sources (CloudZero).

Think about what this means. You're running a business where:

• Your costs vary wildly by customer based on usage patterns
• You have no reliable way to forecast those costs
• You can't attribute costs to specific customers, features, or workflows
• Your standard financial reports treat all customers as equally profitable

This isn't a minor accounting inconvenience. It's a fundamental blind spot that can sink your company.

The $20 Customer Costing You $40

Let me paint a more concrete picture of how this plays out.

You have a customer on your $20/month plan. They seem great—they log in frequently, they use all your features, they'd probably give you a positive NPS score.

But beneath the surface:

• They're hitting your AI endpoints 10x more than average
• They're using your most expensive model for simple tasks
• Their queries tend to be longer, generating more output tokens
• They've found a workflow that triggers multiple API calls per action

Your actual cost to serve this customer? $40/month. Every month they stay subscribed, you lose $20.

Now multiply this by the percentage of your customer base exhibiting similar behavior. According to research, flat-rate or per-seat pricing structures fail specifically because they're either too expensive for casual users (who churn) or too cheap for power users (who erode margins) (Userpilot).

The companies that monetize AI successfully are 2-3x more likely to attribute costs by customer, product, or model. Those that don't? They're essentially flying blind (Mavvrik AI Cost Governance Report).

What Actually Drives AI Costs (It's Not Just Tokens)

When people think about AI costs, they usually think about token pricing. But tokens are often just the tip of the iceberg.

Here's the real cost stack for most AI products:

LLM API Costs: Yes, tokens matter. But the cost difference between models is enormous. Using GPT-4 for a task that GPT-3.5 could handle means you're overpaying by 10-20x on that inference.

Third-Party Data: One financial AI company was spending $0.50 per request on external data feeds before any AI processing even began (Pilot).

Human Verification: A healthcare AI spent $1.20 per interaction on accuracy checks alone—exceeding their entire revenue per query.

Infrastructure Overhead: GPU time, vector database queries, orchestration, monitoring, network costs. These often exceed the raw LLM costs.

Hidden Scaling Costs: Agentic workflows have caused token consumption per task to jump 10-100x since December 2023 as AI does more complex, multi-step operations (SaaStr).

If you're only tracking tokens, you're missing most of the picture.

The Path Forward: Treating AI Costs as COGS

The solution isn't complicated conceptually—it's just demanding to implement.

First, you need cost attribution at the customer level. Not aggregated monthly cloud bills. Not approximate allocations based on user count. Actual per-customer, per-feature, per-inference cost tracking that lets you calculate real unit economics.

Second, you need this data in real-time. AI costs can spike 100x overnight from a prompt change, model update, or shift in customer behavior. Monthly cost reviews don't cut it.

Third, you need to treat AI costs as COGS, not R&D. As one pricing strategist put it: "AI is no longer a side project—it's a structural cost reshaping corporate financials." Companies that continue to bury AI costs in general infrastructure spend will continue to make unprofitable pricing decisions (Mavvrik).

Fourth, you need to tie pricing to cost reality. The industry is rapidly moving toward hybrid models—base subscription plus usage-based components—precisely because pure flat-rate pricing doesn't work for AI economics. According to Growth Unhinged's 2025 report, seat-based pricing dropped from 21% to 15% of companies in just 12 months, while hybrid pricing surged from 27% to 41% (Pilot).

The Bottom Line

Your AI product probably has a margin problem you can't see. Not because you're doing anything wrong—but because traditional SaaS metrics weren't built for products where every customer interaction has meaningful cost implications.

The companies that win in AI won't just build great products. They'll understand their unit economics at a granular level. They'll know exactly which customers are profitable and which are subsidized. They'll price based on actual cost-to-serve, not assumptions from the SaaS playbook.

The question isn't whether you can afford to track this level of detail. It's whether you can afford not to.

At tknOps, we help AI companies understand what's actually happening with their token usage and AI costs—by customer, by feature, by whatever dimension matters to your business. Because you can't optimize what you can't see.