The Most Common AI Cost Mistakes in Production
4 min read
Your AI feature works brilliantly in development. Users love it. Then the invoice arrives.
This scenario plays out constantly across AI-powered SaaS companies. According to CloudZero's 2025 State of AI Costs report, average monthly AI spending will reach $85,521 in 2025—a 36% increase from 2024. Yet only 51% of organisations can confidently evaluate their AI ROI.
The problem isn't that AI is inherently expensive. It's that most teams make the same preventable mistakes once they hit production. Here are the costliest ones—and how to avoid them.
Mistake 1: No Visibility Into Per-Customer Costs
Traditional SaaS operates on near-zero marginal costs. Adding another user costs virtually nothing. AI breaks this model entirely.
Every API call incurs direct, variable costs. And usage varies dramatically between customers. One heavy user running complex multi-turn agents can consume 100x the tokens of a casual user—while paying the same subscription fee.
As Drivetrain's analysis of AI SaaS economics notes, heavy users and complex prompts create a fat-tailed usage distribution that compresses margins when pricing isn't aligned with consumption.
Without per-customer cost attribution, you're flying blind. You can't identify which customers destroy your unit economics, which features drain resources, or where optimisation would have the biggest impact.
The fix: Implement granular tracking that attributes AI costs to specific users, teams, and features from day one—not after your margins have already eroded.
Mistake 2: Using Premium Models for Every Request
Not every task needs GPT-4o or Claude Opus.
A customer support chatbot using premium models at $15 per million tokens when a lighter model at $0.15 per million would deliver identical quality is burning money for no reason. Industry analysis suggests that 70-80% of production workloads perform identically on mid-tier models compared to premium ones.
One SaaS platform reduced monthly costs from $42k to $29k simply by routing 70% of requests to smaller models—with zero user complaints.
The fix: Implement intelligent model routing. Use premium models for complex reasoning tasks and route simpler queries to cost-effective alternatives. A/B test cheaper models before committing to expensive defaults.
Mistake 3: Ignoring Output Token Economics
Most teams focus on input costs because that's what appears prominently on pricing pages. But output tokens typically cost 3-5x more than input tokens.
Consider a chatbot generating 2x more output than input. At $0.15 per million input tokens, you might expect costs around $0.15 per million total. The reality? ($0.15 × 1M input) + ($0.60 × 2M output) = $1.35 per million—9x higher than the advertised price.
This asymmetry makes controlling response length one of the most impactful cost levers available. Yet most applications set generous output limits and never revisit them.
The fix: Set explicit output token limits appropriate to each use case. Monitor your actual input/output ratios. Optimise prompts to encourage concise responses without sacrificing quality.
Mistake 4: Treating Prompts as Write-Once
Every unnecessary word in your prompts costs money at scale.
Prompt optimisation can reduce token usage by up to 35%. One travel-tech startup cut token consumption by 9% overnight simply by removing two boilerplate paragraphs from every prompt.
Yet most teams write prompts during development and never revisit them. System prompts balloon with accumulated instructions. Context windows fill with information the model doesn't need.
The fix: Audit your prompts quarterly. Strip unnecessary context. Use prompt compression tools for high-volume applications. Test whether shorter prompts maintain output quality—they often do.
Mistake 5: No Caching Strategy
If users ask similar questions repeatedly, you're paying for the same computation multiple times.
Semantic caching—matching queries by intent rather than exact wording—can eliminate 20-40% of redundant API calls. Customer support bots with 30% repetitive questions see corresponding cost reductions with basic caching implementations.
Provider-level caching offers even bigger wins. Anthropic's prompt caching can reduce input costs by up to 90% for repeated prompt prefixes.
The fix: Implement caching at the application layer for common queries. Leverage provider caching features for shared system prompts and context.
Mistake 6: Reactive Cost Discovery
CloudZero found that 15% of companies have no formal AI cost-tracking system at all. Even among those that do, many rely on vendor dashboards that only show aggregate spending—useful for knowing what you spent, but useless for understanding why.
This leaves teams discovering cost overruns days or weeks after they occur. By then, the damage is done.
Agent-based systems amplify this problem. A single agent run may involve multiple model calls, planning steps, and tool invocations. If an agent enters a loop or overuses tools, costs escalate rapidly before anyone notices.
The fix: Implement real-time cost monitoring with alerts at the application layer. Track spending by model, feature, customer, and request—not just in aggregate.
The Compound Effect
These mistakes don't exist in isolation. They compound.
Unoptimised prompts hit premium models without caching, generating verbose outputs for customers whose usage you can't track. Each inefficiency multiplies the others.
The good news? Fixes compound too. Granular tracking reveals which optimisations matter most. Model routing reduces costs across all customers. Prompt optimisation benefits every request.
Where to Start
If you're making multiple mistakes from this list, start with visibility. You can't optimise what you can't measure.
Track AI costs by customer, feature, and model from day one. Once you can see where money goes, the path to optimisation becomes clear.
tknOps provides precision token tracking for multi-tenant AI applications, helping SaaS companies understand exactly where their AI costs come from—by user, team, and feature. Learn more at tknops.io