AI Is Not Free Marginal Cost Software Anymore

For two decades, software economics followed a beautiful formula: build once, serve infinitely. Every new customer you added cost you virtually nothing to serve. That near-zero marginal cost was the magic behind SaaS—and why investors fell in love with 80% gross margins.

AI just broke that formula.

The Old Model: Ship Once, Profit Forever

Traditional SaaS had an elegant cost structure. You built the software, you hosted it cheaply, and the marginal cost of each new customer approached zero. Best-in-class SaaS companies consistently achieved 75-80% gross margins. Some hit 90%.

This wasn't just impressive—it was foundational to how the entire SaaS business model worked. With each new customer, your unit economics improved. Fixed costs spread across a larger base. Variable costs barely moved. The math got better the more you grew.

Enterprise budgets of $63,000 per month for software infrastructure seemed perfectly reasonable because those costs were predictable, contained, and scale-advantaged.

The New Reality: Every Request Has a Price Tag

AI flipped this on its head. Every prompt, every completion, every inference burns actual compute. GPUs aren't cheap. Neither is electricity. And when you're processing thousands of requests per customer, the math changes fundamentally.

Here's what the data shows:

• 84% of companies now report at least 6% gross margin erosion tied directly to AI infrastructure costs (Mavvrik AI Cost Governance Report 2025)
• Only 15% can forecast their AI costs within ±10% accuracy (Mavvrik)
• Average enterprise AI budgets have jumped from $63,000 to $85,000 monthly—a 36% increase in just one year (Medium)

The shift in unit economics is material. In conventional SaaS, high margins were built on fixed costs amortized across many users, with marginal cost per user approaching zero. With AI, marginal cost per user is non-zero, variable, and usage-dependent.

The Multi-Tenant Problem Gets Worse

For companies running multi-tenant AI applications, the challenge compounds. Traditional SaaS let you treat customers more or less equally—the heavy user of your CRM didn't cost meaningfully more to serve than the light user.

AI doesn't work that way. Two accounts on the same plan can generate dramatically different costs to serve. Heavy users, long prompts, multi-turn agents, and complex queries create a fat-tailed usage distribution. Some customers consume orders of magnitude more resources than others.

This creates a situation where your $20/month customer might actually be costing you $40 to serve—while you have no visibility into which customers are profitable and which are destroying your margins.

The Numbers Don't Lie

The margin compression is real. Consider the evidence:

AI-native companies are struggling to achieve traditional SaaS margins. Bessemer's State of AI report divides AI companies into "Supernovas" running at roughly 25% gross margins and "Shooting Stars" hitting 60%—both well below the 75-80% benchmark that traditional SaaS commanded.

Even the giants feel the squeeze. GitHub Copilot reportedly cost Microsoft $20+ per user per month at its $10 price point in early stages. Anthropic ran negative 94-109% gross margins in 2024—losing more on infrastructure than they made in revenue.

Inference costs have become strategic. The AI inference market sits at approximately $97 billion in 2024, projected to grow to $254 billion by 2030. This isn't a minor operational line item anymore. It's cost of goods sold.

COGS Is Usage-Linked, Not User-Linked

This is the fundamental shift that many companies haven't fully internalized. Instead of cost being largely fixed once a seat is provisioned, inference spend rises with prompt length, response length, and usage concurrency.

The marginal cost of the next request is never zero.

AI introduces variable COGS into businesses built for fixed-cost economics. SaaS historically had a beautiful cost structure: ship once, serve many times, mostly fixed costs, gross margin stability. AI features drag you into a fundamentally different game—variable inference costs that scale with usage.

If product pricing fails to track compute cost, margins erode. It's that simple.

What This Means for Your Business

The winners in 2026 won't be those with the flashiest AI features. They'll be the enterprises that can see, forecast, and govern the true cost of AI.

This requires a paradigm shift in how you think about unit economics:

1. Treat AI costs as COGS, not operating expenses. Every token, every inference belongs on your cost of goods sold line. Burying these costs in general overhead creates a false picture of profitability.

2. Track cost per customer, not just total spend. Aggregate AI costs tell you nothing about where you're making money and where you're losing it. You need customer-level visibility.

3. Understand usage patterns before they destroy margins. Power users can consume orders of magnitude more resources than anticipated. Flat pricing models become unsustainable without visibility into who's consuming what.

4. Build forecasting models that capture AI's volatility. Even minor changes in a prompt, model version, or agent workflow can spike consumption by 100x overnight. Traditional budgeting tools built for cloud workloads fail in the AI era.

The Path Forward

AI isn't going away, and the companies that ignore these economics won't survive. But the solution isn't to avoid AI—it's to understand what you're actually spending.

The shift from near-zero marginal cost to variable, usage-dependent costs requires new tools and new thinking. You need to know which customers are profitable. You need to understand which features are margin-positive. You need visibility into the true cost of serving each request.

The era of building a business model around the assumption that AI costs don't matter is over. The companies that recognize this and build the infrastructure to manage it will thrive. The ones that don't will discover the hard way that you can't lose money on every inference and make it up in volume.

AI has changed the economics of software. tknOps gives you the visibility to adapt. Track token costs, attribute spend to customers, and protect your margins—because in the age of AI, what you can't measure will cost you.