The Cost of Doing Business: Understanding Team Economics
Software development is a capital-intensive endeavor, yet many organizations lack a fundamental understanding of the financial implications of their engineering decisions. Viktor Cæssan’s recent post, "The Economics of Software Teams," highlights this disconnect – the fact that teams rarely connect their work to concrete financial costs.
According to the analysis, a typical software engineer in Western Europe costs around €130,000 per year, factoring in salary, benefits, overhead, and more. A team of eight engineers therefore represents an annual expenditure of approximately €1,040,000, translating to roughly €87,000 per month, or €4,000 per working day.
Surprisingly, many engineers and their managers are unaware of these numbers. This lack of visibility prevents informed prioritization and leads to potentially wasteful spending. A seemingly small decision – spending three weeks on a feature used by only 2% of users – carries a significant cost of approximately €60,000.
| Team Size (Engineers) | Annual Cost (€) | Monthly Cost (€) | Daily Cost (€) |
|---|---|---|---|
| 8 | 1,040,000 | 86,667 | 4,000 |
The Break-Even Point for Internal Platform Teams
The article then focuses on the economics of internal platform teams – teams dedicated to building tools and infrastructure for other engineers. Let's consider a team of eight engineers supporting 100 other engineers. To justify its €87,000 monthly cost, the platform must deliver at least that much value to its users.
The most easily quantifiable value is time saved. Given an engineer cost of €130,000/year or roughly €65/working hour, the platform needs to save the 100 engineers a combined total of 1,340 hours per month – approximately 13.4 hours per engineer per month, or around three hours per week per person. This is a surprisingly achievable target; a well-designed platform automating tasks like deployment or environment setup could easily clear that threshold.
However, the crucial question is whether these metrics are actually tracked and used to inform development decisions. The article suggests that, in most organizations, they are not.
The Impact of LLMs
The arrival of Large Language Models (LLMs) is adding urgency to this issue. As LLMs automate more coding tasks, organizations are beginning to scrutinize the return on investment (ROI) of their engineering headcount. If engineers are not demonstrably adding value exceeding their cost, the economic justification for maintaining large teams becomes questionable.
The author argues that the historical practice of treating large engineering teams as a general “asset” is under threat. Organizations will need to become far more disciplined in tracking the value delivered by their engineering investments and making data-driven decisions about resource allocation. The post does not detail how to implement these tracking mechanisms, but emphasizes the necessity of doing so. It is uncertain how quickly organizations will adapt to this new economic reality, but the trend appears clear.