How to Build Cost Awareness Into Your Product Culture
When your CFO walks into an engineering standup and asks why the AI bill spiked 40% while usage only grew 15%, that's not an accounting question. It's a product question. Was it users gaming unlimited plans? A model upgrade nobody noticed? A feature that seemed small but consumed tokens at scale?
These questions shouldn't be surprises. But in most AI companies, they are.
The challenge isn't that engineers don't care about costs—it's that engineers often feel cost isn't their responsibility. They work in environments where functionality and delivery deadlines are the primary drivers, and the culture reinforces this outlook. Meanwhile, finance sees a monthly bill with no way to attribute spend to specific features, teams, or customers.
Building cost awareness into your product culture bridges this gap. Here's how.
Why Culture Beats Tooling
You can deploy the most sophisticated cost monitoring platform in the world and still get blindsided by AI bills. That's because tooling without culture creates dashboards nobody checks and alerts nobody acts on.
The companies getting this right treat cost as another measure of product quality—like performance, reliability, or user experience. When developers see real-time feedback showing the cost implications of their designs, they naturally make different decisions. Good developers want to build cost-efficient products the same way they want to write high-quality code.
This isn't about restricting innovation. The FinOps Foundation emphasises that practitioners must balance cultural measures aimed at encouraging action through awareness with governance measures designed to control action through standardisation. Get the balance wrong, and you either demotivate engineers or lose control of spending entirely.
Shift Left on Costs
The principle of "shift left"—addressing issues earlier in the development lifecycle—has transformed how teams handle security. The same approach works for costs.
In traditional workflows, FinOps or cost optimisation policies kick in after cloud resources are provisioned. By then, expensive architectural decisions are already baked in. Shifting left means integrating cost awareness directly into the development lifecycle, catching issues at their inception rather than remediating them later.
For AI products specifically, this means:
During design: Before writing any code, model the expected token consumption for new features. What's the average prompt size? How many API calls per user session? What models will you need? These estimates don't need to be perfect—they need to exist.
During development: Give engineers visibility into the cost implications of their prompt engineering choices. When developers see that a particular prompt tweak doubled the tokens, they pay attention.
Before deployment: Review infrastructure decisions through the lens of cost. Using the latest resource types often saves money while improving performance—but only if someone checks.
The classic software engineering principle applies: for every £1 it costs to fix a problem in development, it costs £10 in staging and £100 in production. The same multiplier holds for cost inefficiencies.
Make Costs Visible to Everyone
Visibility is the foundation of accountability. Without it, teams optimise for their specific areas without understanding the broader financial impact.
The most effective approach treats AI spend like a utility meter that everyone can watch. This creates what's sometimes called the "Prius effect"—where real-time awareness of consumption prompts immediate remedial action. When engineers see their feature's cost impact in real time, behaviour changes.
What should be visible?
Per-feature costs: Break down spending by product feature, not just by model or API. This answers the question "which parts of our product are most expensive to run?"
Per-team costs: Attribute usage to the teams responsible. When multiple business units share AI infrastructure, clear allocation prevents finger-pointing and enables meaningful accountability.
Per-customer costs: This is where most AI companies lack visibility entirely. Understanding which customers consume disproportionate resources relative to their revenue is essential for sustainable unit economics.
The goal is to turn cost from a post-facto finance report into an operational signal that engineering, product, and finance teams all reference from the same lens.
Assign Clear Ownership
Visibility without ownership creates informed bystanders, not accountable actors.
Each product team should own its AI usage and manage that usage against an available budget. This doesn't mean centralising control—it means decentralising accountability. Enable teams to make cost-informed decisions during design, provisioning, and scaling without slowing down delivery.
Practical ownership looks like:
Cost owners per team: Assign someone in each team who is accountable for tracking and improving their unit's spend. This person becomes the bridge between engineering decisions and financial outcomes.
Cost discussions in sprint planning: Include AI costs in architectural decisions and sprint planning, not as an afterthought but as a standard consideration alongside performance and reliability.
Budget thresholds and alerts: Set spending caps at the team or feature level with automated alerts. Nothing focuses attention like knowing your feature will stop working if it exceeds budget.
Create Feedback Loops That Drive Improvement
One-time training doesn't create lasting culture change. Continuous feedback loops do.
Regular cost reviews: Schedule cost discussions into existing team rhythms—whether that's sprint retrospectives, monthly reviews, or architecture decision records. FinOps best practices recommend running continuous optimisation cadences to identify inefficiencies and build cost awareness into day-to-day operations.
Recognise efficiency wins: Celebrate teams that meet or exceed efficiency targets. Recognition reinforces positive behaviours more effectively than criticism reinforces compliance.
Share learnings across teams: When one team discovers a cost optimisation technique, spread it organisation-wide. A prompt engineering improvement that reduces token consumption in one feature might apply everywhere.
Track metrics that matter: Cost per user, cost per feature, cost per transaction—whatever unit metrics align with your business model. FinOps culture is best expressed in terms of unit metrics because they translate technology spend into meaningful business language.
Bridge the Engineering-Finance Divide
The fundamental challenge in AI cost management is that the people who control costs (engineers) and the people who pay for costs (finance) speak different languages and operate on different timelines.
Engineers think in tokens, models, and architecture. Finance thinks in budgets, forecasts, and variance analysis. Neither perspective is wrong—they're just incomplete without the other.
Building a cost-aware culture requires cross-functional collaboration where engineering, finance, and product teams discuss upcoming deployments, budgets, and efficiency targets together. This isn't about finance dictating to engineering or engineering ignoring finance. It's about shared understanding and shared goals.
Some organisations create formal FinOps teams—Prudential's three-person team of experienced cloud engineers wrote guides, solutions, and code to empower development teams while driving significant cost avoidance. Others embed cost awareness into existing platform engineering functions.
The specific structure matters less than the outcome: engineers understand the financial implications of their decisions, and finance understands the technical constraints that shape those decisions.
Start Where You Are
You don't need to overhaul your entire organisation overnight. Cultural change happens incrementally.
Week one: Pick one high-cost feature and instrument it with per-user cost tracking. Share the results with the team that owns it.
Month one: Establish baseline unit economics for your top three features. Set improvement targets.
Quarter one: Include cost metrics in your standard team dashboards and sprint retrospectives. Begin regular cost reviews.
Year one: Make cost awareness a standard part of your product development lifecycle, from design through deployment.
The companies winning in AI aren't necessarily the ones with the biggest budgets. They're the ones who understand exactly where their money goes and why—and who've built organisations that keep improving that understanding over time.
tknOps helps AI-powered SaaS companies build cost-aware product cultures by providing granular visibility into AI spending—per-user, per-team, and per-feature. When everyone can see where the money goes, better decisions follow.