The Real Cost of AI Coding Tools: $2,664 Across 10 Production Systems Over 28 Months

Key Takeaways

GitHub Copilot pricing as of early 2026: Individual at $10/month, Business at $19/user/month, Enterprise at $39/user/month.
The $2,664 figure is low not because the operator used AI sparingly, but because the CEM model changes how AI tools are consumed.
If you are allocating $500 to $2,000 per developer per year for AI tooling, you are budgeting correctly for the per-seat model.

Published: February 2026 | Stealth Labz — CEM Validation Portfolio Keywords: AI coding tools total cost, cost of AI development tools, AI tool spend production development

The Setup

AI coding tools are everywhere. GitHub Copilot, Cursor, Claude, ChatGPT — the market has shifted from "should we use AI for development?" to "which AI tools and how much should we budget?" Every engineering team is now running a line item for AI subscriptions, and most are guessing at the number.

The conventional approach to budgeting AI development tools follows the per-seat model inherited from SaaS: count your developers, multiply by the subscription cost, add 20% for overages or premium tiers, and submit the budget request. A 10-person engineering team using GitHub Copilot Business ($19/user/month), Claude Pro ($20/user/month), and ChatGPT Plus ($20/user/month) runs $7,080 per year in AI tool costs alone — before any API usage, enterprise tiers, or specialized tools like Cursor ($20/user/month).

This per-seat model fails to answer the question that matters: what did the AI tools actually produce? A team spending $7,080/year on AI subscriptions might see a 20-30% productivity gain according to GitHub's internal studies. But that gain is measured against the team's existing output — it does not tell you the total cost of AI tooling per unit of production software delivered.

What the Data Shows

External: What Organizations Spend on AI Development Tools

GitHub Copilot pricing as of early 2026: Individual at $10/month, Business at $19/user/month, Enterprise at $39/user/month. Cursor Pro runs $20/month. Claude Pro is $20/month, with API pricing at variable per-token rates. ChatGPT Plus is $20/month, with Team plans at $25/user/month.

The 2024 Stack Overflow Developer Survey found that 76% of developers are using or planning to use AI coding tools, with median monthly spend per developer between $20 and $60 depending on tool combination. Retool's "State of AI in Engineering" report (2024) found that engineering teams allocate between $500 and $2,000 per developer per year on AI tooling, with larger teams skewing toward the higher end due to enterprise licensing and API costs.

For a typical 5-person team, that translates to $2,500 to $10,000 per year in AI tool spend. For a 10-person team, $5,000 to $20,000 per year.

These figures represent the cost of augmenting an existing team. The AI tools make the team faster, but the team still exists. The AI budget sits on top of the $960,000+ engineering payroll, adding 0.3% to 2% to the total cost.

Internal: $2,664 Total — Not Per Year, Not Per Seat. Total.

The PRJ-02 portfolio spent $2,664 on AI tooling across the entire 28-month operating period (February 2024 through January 2026). This figure covers all AI tools used to build 10 production systems comprising 596,903 lines of code.

AI Tool	Total Spend (28 Months)	Monthly Average
Anthropic / Claude	$1,333	$47.61
OpenAI / ChatGPT	$1,301	$46.46
Leonardo.AI	$30	$1.07
Total AI tooling	$2,664	$95.14

Source: 28_month_financial_locked_values, QB-verified vendor transactions.

Breaking Down the Unit Economics

Metric	Value
Total AI spend	$2,664
Total LOC produced	596,903
Cost per line of code (AI tools)	$0.004
Total commits	2,561
Cost per commit (AI tools)	$1.04
Total systems built	10
AI cost per system	$266.40
AI spend as % of total build cost ($67,895)	3.9%

For comparison: a single month of GitHub Copilot Enterprise for a 5-person team costs $195 ($39 x 5). The entire PRJ-02 AI tool spend over 28 months — which contributed to building 10 production systems — was 13.7x that single monthly charge.

The AI Spend in Context

Cost Category	Amount	% of Total Build ($67,895)
CON-02 (primary contractor)	$40,700	59.9%
CON-03 (secondary contractor)	$21,854	32.2%
Initial website build	$2,500	3.7%
AI tools	$2,634	3.9%
Other software	$207	0.3%

AI tooling was 3.9% of the total build cost. The remaining 96.1% went to human contractors and conventional software. This is not a story about AI replacing developers — it is a story about AI as a low-cost force multiplier for a human operator.

What the AI Tools Actually Did

The AI tools served three functions in the CEM workflow:

Scaffolding. Generating boilerplate code from templates — authentication flows, CRUD controllers, admin interfaces, Blade templates. This is the high-volume, low-complexity work that traditionally consumes junior developer time.
Debugging. Pattern-matching errors against known solutions. When the operator hit an issue, Claude or ChatGPT could diagnose it against the codebase context faster than manual Stack Overflow searches.
Code generation from specifications. The operator described the desired behavior; the AI produced the initial implementation. The operator then reviewed, corrected, and integrated. Claude Code contributed 2.5% of commits on PRJ-01 directly.

The AI did not design the architecture. It did not make product decisions. It did not prioritize features. It executed within the operator's direction — and it did so for $95 per month.

How It Works

The $2,664 figure is low not because the operator used AI sparingly, but because the CEM model changes how AI tools are consumed.

One seat, not ten. There is one operator. One Claude subscription. One ChatGPT subscription. No per-seat multiplication. The organizational model that makes AI tools expensive (scaling across a team) does not exist. The operator uses both tools at professional-tier pricing and switches between them based on which performs better for the specific task.

API usage, not enterprise licensing. The Anthropic spend ($1,333) includes both subscription and API usage. Claude Code (API-based) was used for direct code scaffolding during the PRJ-01 build. API pricing scales with actual usage, not with seats. When the operator is not generating code, the cost drops to zero — unlike a per-seat subscription that charges whether the tool is used or not.

Compounding reduces AI dependency over time. As the template library grows, less scaffolding is needed. The ninth product in the portfolio cost $0 to build because the foundation was already established. AI tools are most valuable during the early phases of the portfolio when new patterns are being created. By the later phases, the operator is assembling known components, and AI usage naturally decreases.

The monthly AI tool cost trajectory mirrors the overall cost curve:

Month	AI Tool Spend
Jul 2025	$90
Aug 2025	$90
Sep 2025	$110
Oct 2025	$723
Nov 2025	$765
Dec 2025	$135
Jan 2026	$0

October and November 2025 — the peak build months for PRJ-01 — show elevated AI spend ($723 and $765). By December, as the build stabilized and the operator transitioned to acceleration mode, the spend dropped to $135. By January, it hit $0.

What This Means for Engineering Leaders Budgeting AI Tools

If you are allocating $500 to $2,000 per developer per year for AI tooling, you are budgeting correctly for the per-seat model. Stack Overflow and Retool data validate this range. For a 10-person team, that is $5,000 to $20,000 annually — a rounding error on the $960K+ payroll, but a real line item that procurement will question.

The PRJ-02 data reveals a different model: $2,664 total over 28 months, producing 596,903 lines of code across 10 systems. The cost per line of code for AI tools was $0.004. The cost per production system was $266.40.

The difference is not the AI tools — the tools are the same (Claude, ChatGPT). The difference is the organizational model consuming them. Per-seat pricing multiplied across a team produces a predictable budget. Per-operator pricing consumed by a single practitioner using CEM produces a total AI cost that most teams spend in a single quarter.

The question for budget holders is not "how much should we spend on AI tools?" It is "how many seats are actually producing output, and could fewer seats with a different execution model produce the same result at a fraction of the cost?"

References

GitHub (2026). "Copilot Pricing." AI coding assistant subscription tiers.
Cursor (2026). "Cursor Pro Pricing." AI-first code editor subscription data.
Anthropic (2026). "Claude Pricing." AI model subscription and API pricing.
OpenAI (2026). "ChatGPT Pricing." AI model subscription and API pricing.
Stack Overflow (2024). "Developer Survey." AI coding tool adoption and spending data.
Retool (2024). "State of AI in Engineering." Engineering team AI tooling allocation data.