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?
Key Takeaways
- A Developers Digest ROI guide uses a jump from $50,000 to $500,000 to illustrate the measurement gap: the bill rises, but no single dashboard shows what changed.
- Seat-based budgets break down when AI use varies widely by engineer. Leaders need per-user and per-team spend data to see who is driving the bill and whether that spending produces better results.
- A useful ROI view connects total AI spend to delivery speed, code quality, and rework over time. Adoption and usage alone can’t show whether the investment is paying off.
Why AI Coding Costs Can Rise So Fast
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.
3 Questions an AI Coding ROI View Should Answer
1. What Did We Actually Spend?
Start with the full amount, not just seat licenses.
That may include:
- Subscription fees
- Token and API charges
- Premium model use
- Overage fees
- Multiple tools used by the same engineer
- Internal time spent on rollout, governance, review, and rework
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.
2. What Did the Spend Produce?
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:
- Finishing meaningful work faster
- Shipping durable code
- Reducing or creating rework
- Keeping review and deployment bottlenecks under control
- Improving output enough to justify the added cost
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.
3. Is the Return Improving?
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:
- The baseline before rollout
- What changed during adoption
- Whether quality held up
- Whether cost per useful outcome improved
- Which teams or workflows are getting better results
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.
What a Useful ROI Dashboard Should Show
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:
- Total AI coding spend by tool, team, and time period
- Spend per engineer or use case
- Delivery and quality trends before and after adoption
- Rework and code durability
- Cost per useful outcome
- Budget trend and projected overages
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.
Frequently Asked Questions
Why is our AI coding spend higher than we budgeted?
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.
How do we calculate ROI if we didn’t measure a baseline before rollout?
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.
What does healthy AI coding tool ROI look like?
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.
Should we cap AI spend per engineer?
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.
Connect the Bill to the Work
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.