Engineering Decisions That Quietly Destroy Margins
Your SaaS hit 85% gross margins for years. Then you shipped AI features. Six months later, you're staring at 55% and wondering what happened.
The culprit isn't your pricing. It's a series of engineering decisions that seemed reasonable at the time but are now bleeding your company dry.
The Model Selection Trap
When your team built that first AI feature, someone asked: "Which model should we use?"
The answer was probably GPT-4 or Claude Opus. The reasoning? "We need the best performance for our customers."
That decision alone can cost you 100x more than necessary. Token pricing varies dramatically between models—Gemini Flash costs $0.15 per million input tokens while Claude Opus runs $15 per million. Same API call, vastly different economics.
Most tasks don't need frontier models. A tiered approach using cheaper models for routine tasks and reserving premium models for complex reasoning can reduce API costs by 40-60% without degrading user experience.
The Agentic Architecture Explosion
Remember when AI meant single-turn chat completions? Those days are gone.
Modern agentic workflows involving planning, tool use, retrieval, and memory have multiplied token consumption per task by 10x-100x since December 2023. Your AI feature that once cost pennies per interaction now burns through dollars.
One mid-sized e-commerce company saw their monthly LLM costs jump from $1,200 to $4,800 after enabling order-tracking workflows—a 300% increase from a single feature addition.
The engineering team celebrated the improved user experience. Finance discovered the margin erosion three quarters later.
The "Ship Fast" Technical Debt
Speed is valuable. But speed without cost visibility creates a different kind of debt.
Research shows 84% of companies report margin erosion from AI costs, and only 15% can forecast AI costs within 10% accuracy. Nearly one in four miss by more than 50%.
The problem compounds. AI-generated code itself is accelerating technical debt at unprecedented rates. GitClear tracked an 8x increase in duplicated code blocks between 2020 and 2024. This flooded code increases cloud storage costs, multiplies bugs across cloned blocks, and makes testing a logistical nightmare.
Every shortcut today becomes tomorrow's maintenance burden—and AI is making those shortcuts easier to take and harder to detect.
The Visibility Vacuum
Here's the uncomfortable truth: most engineering teams have no idea which features, users, or interactions are driving AI costs.
When engineering lacks visibility into the cost impact of their work, they operate in the dark. COGS creep up unnoticed until a surprise bill lands—and by then, it's already too late.
AI workloads are becoming a hidden margin killer. Many teams spin them up without clear ROI tracking or cost containment policies. Without those guardrails, AI-related costs quietly balloon and eat into margins that took years to build.
What Actually Works
The fix isn't slowing innovation. It's building cost awareness into your engineering culture.
Implement model routing. Not every request needs your most powerful model. Intelligent routing that matches model complexity to task requirements can dramatically reduce costs while maintaining quality.
Track cost per feature. Surface AI spend by service, endpoint, and user segment. When engineers see the cost impact of their decisions in real-time, behaviour changes.
Set guardrails before launch. Rate limits, token budgets, and automated alerts prevent runaway costs from becoming quarterly surprises.
Allocate capacity for debt. Industry best practice recommends 20-30% of development capacity for technical debt management. This isn't overhead—it's margin protection.
The Path Forward
Traditional SaaS achieved near-zero marginal cost per user. AI changes that equation fundamentally. Every interaction has a real, variable cost that scales with usage.
The companies that thrive won't be those with the flashiest AI features. They'll be the ones who can see, forecast, and govern the true cost of AI.
Your engineering decisions are already shaping next quarter's margins. The question is whether you're making them with visibility—or in the dark.
tknOps helps AI-powered SaaS companies track AI costs at the user, team, and feature level. Because you can't optimise what you can't measure. Learn more at tknops.io.