What AI Coding Assistants Actually Cost Per Engineer (Nobody Tells You This)

Every engineering manager I talk to quotes the same number. Twenty bucks a month. Maybe forty for the premium tier. That is the cost of AI coding assistants, right? I have been tracking the real numbers across six client teams for the past year. The actual figure made our CFO do a double-take.

What does an AI coding assistant actually cost per month?

Let us start with what everyone knows. The subscription fees are straightforward.

For a 10-person engineering team, that is $200-2,000 per month. Manageable. Most teams stop the calculation here. That is the mistake.

What are the hidden costs nobody includes in the budget?

I tracked time allocation across three client engagements -- two SaaS products, one fintech platform -- from September 2025 through March 2026. Every team used either Copilot or Cursor as their primary assistant. Here is what I found.

The Code Review Tax

AI-generated code passes lint. It compiles. It often works on the happy path. But it introduces a review burden that did not exist before. GitClear's 2024 analysis of 153 million lines of changed code found that code churn -- code rewritten within two weeks of being authored -- increased 39% year-over-year as AI adoption grew. In my experience across client teams, senior engineers now spend 25-35% more time in code review than they did pre-AI tooling.

The math: a senior engineer billing at $85-120/hour spending an extra 6-8 hours per week reviewing AI-assisted PRs. That is $510-960 per month in review overhead -- per senior reviewer, not per junior who generated the code.

The Technical Debt Accelerator

Sonar's 2025 survey of 20,000 developers found that only 24% of AI-generated code suggestions were accepted without modification. But here is the number that matters: of the code that was accepted, teams reported 15% more maintenance burden in the following quarter compared to human-written code in the same codebase.

AI assistants are excellent at generating code that works now. They are mediocre at generating code that works with your existing abstractions, follows your architectural patterns, or respects your team conventions -- even with good context files. The result is a slow drift toward inconsistency that compounds quarterly.

In my experience, teams accumulate roughly $800-1,500 per engineer per quarter in additional maintenance cost from AI-generated architectural drift. That is $270-500 per engineer per month.

The Security Surface Expansion

METR's 2025 study on AI-assisted development found that developers using AI tools spent 19% more time on tasks than those without -- counterintuitive until you realize the time went into debugging and fixing issues the AI introduced. Separately, Snyk's 2024 report found that AI-generated code contained security vulnerabilities at roughly the same rate as human code, but the volume increase meant more total vulnerabilities to triage.

For teams in regulated industries -- fintech, healthcare, insurance -- every new code path needs security review. More code means more surface area. In my experience, security review overhead increases by $150-300 per engineer per month when AI tools are in heavy use. Not because the code is worse per line, but because there are more lines to review.

What does the real fully-loaded cost look like?

That $20/month Copilot seat is actually $310-750/month when fully loaded. For a 10-person team, you are looking at $3,100-7,500/month, not $200-400.

Is it still worth paying for AI coding assistants?

Yes. But the ROI calculation is different from what vendors tell you.

The productivity gains are real. GitHub's own research claims 55% faster task completion with Copilot. Even if you discount that by half -- it is vendor research -- a 25-30% speed improvement on initial code generation is consistent with what I see in practice. For a team of 10, that translates to roughly 2-3 engineer-months of output gained per year.

At a fully-loaded engineer cost of $12,000-18,000/month -- salary, benefits, equipment, overhead -- those 2-3 months of recaptured output are worth $24,000-54,000/year. Compare that to the $37,200-90,000/year in fully-loaded AI tool costs for the same 10-person team.

The spread is wide. Teams that get positive ROI share three traits: they have strong code review culture, they invest in context engineering -- CLAUDE.md, .cursorrules, architecture docs -- and they treat AI output as a draft, not a deliverable.

Teams that rubber-stamp AI PRs, skip architectural review, or let juniors use AI without senior oversight consistently land on the negative side.

How should engineering teams budget for AI coding tools?

The teams getting the best ROI are not the ones spending the most on tool subscriptions. They are the ones investing in the support structure around the tools. Context engineering alone -- maintaining good project documentation, architecture decision records, and AI instruction files -- accounts for more ROI improvement than upgrading from a $20 to a $200 tier.

What is the bottom line on AI coding assistant economics?

AI coding assistants are not $20/month tools. They are $310-750/month investments per engineer that return $250-600/month in productivity -- if you operate them correctly. The margin between positive and negative ROI is thin, and it depends almost entirely on your team's review discipline and context engineering maturity.

Stop budgeting for the sticker price. Start budgeting for the system.

• The subscription is 6-15% of real cost. Budget for the full stack.
• Code review overhead is the largest hidden cost. Staff accordingly.
• Context engineering -- AI instruction files, architecture docs -- has the highest ROI per hour invested.
• Quarterly debt audits are not optional. AI accelerates drift even in disciplined teams.
• Track rework rate, not just output volume. Velocity without quality is negative ROI.