You Can't Scale What You Can't Attribute

Why AI cost visibility is the hidden prerequisite for sustainable growth

The math used to be simple. In traditional SaaS, your cost to serve the hundredth customer was virtually identical to serving the first. Build once, deploy infinitely, enjoy 80% margins. That equation broke the moment AI entered your product.

Now you're watching two customers on identical $99/month plans. One costs you $12 to serve. The other costs you $140. Without granular attribution, you can't tell them apart until your margin report arrives—and by then, you've already scaled the problem.

The Return of Variable Costs

AI has fundamentally changed SaaS economics. Every prompt processed, every token generated, every inference run carries a real, measurable cost. According to Mavvrik's 2025 AI Cost Governance Report, 84% of enterprises now report gross margin erosion exceeding 6% due to unmetered AI infrastructure costs.

This isn't a minor adjustment—it's a structural shift. Traditional SaaS companies typically achieve gross margins between 75-90%. AI-powered SaaS companies frequently operate at 50-60%, with some heavy-usage features dipping into negative territory for power users.

The challenge isn't that AI is expensive. Costs are actually falling rapidly. The challenge is that you can't see where the expense goes.

The Attribution Gap

Most companies track AI spending at the aggregate level. They know their monthly OpenAI bill or their total GPU hours. What they don't know is which customer, feature, or workflow consumed those resources.

CloudZero's State of AI Costs report found that only 51% of teams feel confident in their ability to measure AI ROI. The FinOps Foundation's research reveals an even starker picture: just 15% of companies can forecast AI costs within plus or minus 10%.

Without attribution, you're flying blind on three critical fronts.

Pricing decisions become guesswork. If you don't know what each customer costs to serve, you can't set sustainable prices. You might be undercharging power users while losing budget-conscious customers who would be profitable at a lower tier.

Margin optimization is impossible. You can't fix what you can't see. When AI costs live in a single line item, you can't identify which prompts are inefficient, which models are overkill, or which features are disproportionately expensive.

Scaling amplifies the problem. Growth should improve unit economics through economies of scale. Without attribution, growth simply multiplies hidden inefficiencies. The $40,000 monthly bill becomes $400,000, and you still don't know why.

Why Traditional Tools Miss AI Costs

Cloud cost management platforms excel at tracking compute, storage, and network. But AI workloads introduce cost drivers these tools weren't designed to capture.

The FinOps Foundation's AI Overview notes that AI services often lack native tagging capabilities and use inconsistent pricing units—tokens for one provider, compute-seconds for another, credits for a third. Traditional tools see API calls as a single expense category, not the granular customer-level consumption you need for real attribution.

Multi-tenant architectures compound the challenge. When ten customers share the same inference endpoint, the cloud bill shows one charge. Splitting that accurately requires instrumentation your infrastructure monitoring wasn't built to provide.

The Attribution Imperative

Companies that monetize AI show 2-3x stronger cost governance discipline, according to Mavvrik's research. The correlation is straightforward: when you charge for AI usage, you're forced to measure it. That measurement creates accountability.

The inverse is equally true. When AI features are bundled into flat-rate subscriptions, no one owns the cost. Engineering optimizes for performance, not efficiency. Finance sees an escalating line item but lacks the granularity to take action.

The path forward requires treating AI costs as a first-class operational metric—tracked per customer, per feature, per request. This isn't about cutting costs; it's about understanding them well enough to make informed decisions about pricing, architecture, and growth.

From Visibility to Scale

Attribution transforms AI costs from a constraint into a strategic lever.

With per-customer visibility, you can implement tiered pricing that reflects actual resource consumption. Heavy users pay proportionally more, protecting your margins without penalizing light users. According to Drivetrain's analysis of AI SaaS economics, two accounts on identical plans can generate dramatically different costs to serve based on how intensively they engage with AI features.

With per-feature visibility, you can identify optimization opportunities. Perhaps your summarization feature costs three times more than similar features at competitors because of inefficient prompting. Perhaps certain models are overkill for routine tasks. You can't know without the data.

With per-request visibility, you can forecast accurately. When you understand the relationship between usage patterns and costs, you can predict what next quarter's AI bill will look like—and plan accordingly.

The Bottom Line

AI-powered products can scale efficiently and profitably. But sustainable scale requires visibility into where costs actually originate. Companies that invest in attribution infrastructure now will build more accurate pricing, healthier margins, and more predictable growth trajectories than those continuing to fly blind.

The question isn't whether to track AI costs at a granular level. The question is how quickly you can build the visibility you need before the attribution gap becomes a scaling crisis.

Building AI-powered products and struggling with cost visibility? tknOps provides granular token tracking for multi-tenant AI applications, helping you understand exactly what each customer, feature, and request costs to serve.