What 212 Engineers Actually Spend on AI Tooling Each Month

AI tooling costs can become opaque quickly. A company may know its GitHub Copilot or Cursor invoice, while API usage sits in cloud billing, team subscriptions appear on expense cards, and experimental agents are charged to project budgets.

That makes "AI spend per engineer" difficult to compare — and easy to overstate.

Read this first. Figures are an illustrative cost model built for editorial framing, not audited finance data. Replace with exported billing records, seat counts and token usage before treating any number here as a measurement.

Short answer: In this illustrative cost model covering 212 engineers, total AI tooling spend is $18,900 per month, or about $89 per engineer. Broad seat licences account for most of the budget, while token and API usage is concentrated among backend, platform, and AI engineering roles.

This model reflects a practical delivery organisation that uses AI for code assistance, architecture research, documentation, testing, internal knowledge retrieval, agent development, and cloud operations — not a blanket policy of assigning every employee the most expensive plan.

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Monthly spend by engineering role

Role	Headcount	Seat spend	Token/API spend	Total monthly spend	Cost per engineer
Product engineers	92	$3,680	$2,760	$6,440	$70
Full-stack engineers	41	$1,640	$1,230	$2,870	$70
Mobile engineers	24	$960	$480	$1,440	$60
Backend engineers	18	$720	$1,260	$1,980	$110
DevOps and platform	9	$540	$630	$1,170	$130
QA and test automation	8	$240	$160	$400	$50
Data and AI engineers	8	$320	$2,080	$2,400	$300
Engineering managers	7	$140	$35	$175	$25
Architects and tech leads	5	$250	$275	$525	$105
Attributed total	212	$8,490	$8,910	$17,400	$82

Role-attributed spend is $17,400. A further $1,500 is shared org-level spend — self-hosted model infrastructure, shared API keys and smaller utilities — that is not allocated to any single role, bringing the monthly total to $18,900, or about $89 per engineer.

Figures are an illustrative cost model built for editorial framing, not audited finance data. Replace with exported billing records, seat counts and token usage before treating any number here as a measurement.

The model deliberately shows a much higher per-person cost for data and AI engineers. Those roles are more likely to use model APIs, retrieval systems, evaluation workflows, agent orchestration, and prototype environments. In contrast, mobile and QA teams may gain value mainly from targeted code assistance and test-generation tools.

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Where the budget goes

Tool category	Monthly spend	Share of total spend
GitHub Copilot	$5,400	28.6%
Cursor	$4,100	21.7%
Claude Code	$2,700	14.3%
OpenAI API	$2,000	10.6%
ChatGPT Team	$1,500	7.9%
Anthropic API	$1,250	6.6%
Perplexity Enterprise	$850	4.5%
Self-hosted model infrastructure	$700	3.7%
Other AI utilities	$400	2.1%
Total	$18,900	100%

Figure 1. Illustrative AI tooling model across 212 engineers: total monthly spend is $18,900, equivalent to about $89 per engineer. GitHub Copilot and Cursor represent 50.3% of total spend.

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What this cost model reveals

The first insight is that AI spend is not distributed evenly. A universal per-seat plan may appear inexpensive, but API-intensive groups can create a disproportionate share of variable cost. Data and AI engineers run about $300 a month each against $25 for engineering managers — a 12x spread, driven by token consumption rather than seat cost.

The second is that seat spend and usage spend should be governed differently. Seat spend is predictable and can be managed through provisioning, renewal checks, adoption reporting, and role-based eligibility. Token and API spend needs budgets, model routing, prompt and context optimisation, caching, rate limits, alerts, and project-level tags.

The third is that cost alone is not an ROI measure. An $89 monthly allocation is justified only when it produces measurable value: reduced cycle time, more test coverage, lower defect escape, faster research, reduced support workload, improved documentation, or increased delivery capacity.

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A practical AI tooling budget policy

1. Standard tools. Provide approved coding, research, and productivity tools to roles that can demonstrate recurring use.
2. Metered tools. Separate API budgets by client, project, environment, and team.
3. Governance. Protect client data, prohibit unapproved secret sharing, and define model and vendor approval rules.
4. Value reviews. Reassess adoption, spend, and evidence of engineering value every quarter.

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Working out what AI tooling should cost across your engineering org, or building the systems that consume those tokens? Ortem Technologies' AI and ML solutions practice works on both sides of that. See our LLM cost optimization guide, or talk to our team →.