The CFO knows what the AI coding tools cost. The CTO knows what engineering shipped. What they often can’t answer is how those two numbers connect. That’s where tokens per feature comes in.
Vantage says the tokens per feature metric is starting to appear in R&D planning. They give a simple example: if a team spends $3,000 in tokens to ship a feature, that is meaningful information for the budget conversation.
At its simplest, tokens per feature connects AI resource consumption to a defined unit of engineering output.
The unit doesn’t have to be a feature. Depending on how an organization plans and measures work, it could be a PR, release, story, or another consistently defined deliverable. Vantage similarly points to cost per PR and cost per release as useful ways to connect AI spending to engineering output.
The important part is consistency. If one team defines a “feature” as a small UI update and another uses the term for a six-month platform project, comparing their ratios won’t tell you much.
The metric is most useful as unit economics, not a leaderboard. It can show whether AI consumption is growing faster or slower than the engineering output associated with it and whether that relationship changes as tools, models, and workflows change.
Tokens per feature tells you about consumption. Cost per feature translates that consumption into dollars.
The distinction matters because tokens prices vary across different models or workloads. Vantage notes that model choice, context-window depth, session length, and agent behavior can all affect token costs.
So a useful view puts the numbers together:
How many tokens did this work consume? What did those tokens cost? What did engineering deliver?
That lets a CTO see whether token use is becoming more efficient while giving the CFO the dollar-based view it needs for planning and budgeting.
The CTO and CFO naturally approach AI spending from different directions.
Engineering wants to know whether AI is helping teams deliver useful work. Finance wants to know whether rapidly growing AI costs are producing enough value to justify the investment.
The FinOps Foundation’s 2026 research shows how quickly those questions are converging. AI management is now part of the FinOps scope for 98% of respondents, while practitioners cite visibility, cost allocation, and determining AI value and ROI as major challenges.
Tokens per feature gives both sides a common unit of analysis. Instead of stopping at “AI spend increased 30%,” leaders can ask whether the amount of AI consumption required to produce comparable engineering work is rising, falling, or staying flat.
Choose a unit that engineering already uses and can apply consistently: feature, PR, release, story, or another meaningful deliverable.
Don’t pick the unit because it makes the metric easy to calculate. It needs to represent real work closely enough that changes in the ratio mean something.
You need to know which AI consumption belongs to which team, workflow, use case, or outcome.
Larridin’s Token Spend & Insights traces tokens to teams, agents, and use cases and connects spend to output. Its stated goal is cost per unit of work, not cost per seat.
That attribution is what turns an aggregate token bill into data that can be compared with engineering delivery.
Don’t stop at the number of tokens. Record the associated spend as well. A change in model mix or context usage can make the cost of similar token volumes materially different.
That gives leaders two useful views: tokens per feature for consumption efficiency and AI cost per feature for financial efficiency.
A rising tokens-per-feature ratio isn’t automatically bad. A team may be tackling more complex work, using longer-context models, or shifting more of the development process to agents. Likewise, a falling ratio is not automatically evidence of higher productivity.
The useful question is whether the ratio changes without a corresponding change in complexity, quality, or value delivered.
There’s no universal benchmark. Vantage describes tokens per feature as an emerging R&D metric, not an industry standard with a healthy target range.
Start with your own baseline for comparable work. Track whether token consumption and cost per unit improve over time, and compare teams only when the work is similar enough for the comparison to be meaningful.
Yes. Any unit-based productivity metric can become misleading if teams change the unit rather than improve the work.
That’s why tokens per feature should be paired with context such as feature complexity, delivery time, code quality, and business value. A lower ratio doesn’t matter much if the team simply changed what it calls a feature.
Use recent, comparable work to establish a range rather than assuming every future feature will consume the same amount.
If similar features historically required a certain range of AI consumption and spend, that range can become one input to planning and forecasting. It should not replace engineering judgment about complexity or uncertainty.
Possibly, but don’t assume that it will.
More experienced AI users may become more efficient in some workflows, while greater use of agents, deeper context, or more complex tasks can push token consumption higher. Track AI Fluency alongside tokens per feature and test whether greater proficiency is actually translating into better unit economics in your environment.
Knowing what AI costs is only the first step. Leaders also need to know what that spending produced.
Larridin’s Token Spend & Insights connects AI consumption to teams, agents, use cases, and outcomes, giving engineering and finance the data foundation for moving from total spend to cost per unit of work.
Book a discovery call to connect your AI token spend to engineering output.