Finance sees AI costs rising, while engineering sees people using the tools every day. Neither view answers the question that matters: what did the company get for the money?
The first wave of AI coding tools fit familiar software budgets. A company bought a set number of seats, paid a predictable monthly price, and reviewed the contract at renewal.
Agentic tools changed that model. A subscription may still cover access, but token use, premium models, overages, and long-running agent sessions can add variable costs on top. Two engineers with the same tool can generate very different bills.
Uber reportedly used its full 2026 AI coding budget within four months as adoption grew across its engineering organization. Average monthly costs ran from $150 to $250 per engineer, while heavy users reached $500 to $2,000.
Agentic workflows can also cost more because they do more work behind the scenes. EY compared a $0.04 linear chatbot interaction with a $1.20 orchestrated workflow that used tools, reasoning, and repeated steps. The example isn’t specific to coding, but it shows why the price of one prompt doesn’t capture the cost of an agent completing a task.
The problem is that the bill changed faster than the way many companies measure value.
Start with the full amount, not just seat licenses.
That may include:
Provider invoices only show part of the cost. They don’t always include the total by engineer, team, project, or use case.
Larridin’s Token Spend and Insights brings AI spending across tools and models into one view and attributes it to teams, agents, and use cases. That gives finance and engineering a shared starting number.
More AI use isn’t the same as more productivity. Neither is more generated code.
The value side of the equation should show whether the team is:
Larridin’s Developer Productivity platform tracks measures such as cycle time, AI code share, code durability, and ROI. That helps leaders compare the money going in with the work and quality coming out.
The number from just one month can be misleading. A team may still be learning a tool, changing its review process, or figuring out which work is a good fit for AI.
Track the same cost, delivery, and quality measures over time. A useful view shows:
Larridin’s AI Fluency shows how AI use develops across teams. Paired with delivery and quality data, it helps leaders see whether greater AI fluency is turning into better results.
A CFO or CTO shouldn’t need five provider portals and a spreadsheet to answer a basic budget question.
At a minimum, the dashboard should show:
The goal isn’t to punish the people who use AI the most. High spend may be justified when it produces strong, durable work. The dashboard should make the difference between productive use and expensive activity visible.
One common reason is that the budget covered seat licenses while actual use added token charges, overages, premium models, and agent sessions. Tool sprawl can add another layer when engineers use several products for different tasks. A full AI coding tool cost view helps uncover expenses that weren’t included in the original software estimate.
Start now. Record current spend, delivery, quality, and rework, then track those measures consistently. You may not be able to build a perfect before-and-after comparison, but a clear current baseline is better than continuing without one.
Larridin’s 2026 Developer Productivity Benchmarks put healthy ROI at 2.5–3.5x for average performers and 4–6x for top-quartile organizations. Those figures use total tool and token costs, not seat licenses alone.
A cap can prevent a surprise bill, but the same limit for everyone may block heavy users who are getting good results. It may do little to reduce low-value use. A better approach is to compare spend with output and quality, then set limits or review points based on the work. Our per-developer AI budget guide covers several ways to structure that policy.
Larridin connects AI coding spend with delivery, quality, and ROI so finance and engineering can answer the same question with the same data: what did we get for the money?
Book a discovery call to build a clearer AI coding ROI picture.