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What an engineer actually costs

What an engineer actually costs

Chapter 14
Part IV
4
min read

What an engineer actually costs

You cannot run a production system on a salary figure off a job board. The salary is the part you see; the loaded cost is the part you pay.

The US Bureau of Labor Statistics' Employer Costs for Employee Compensation data puts benefits at 29.9% of total compensation, a wage multiplier of roughly 1.43× before you've bought a single laptop or paid for a single training course. Apply that to a mid-level software engineer and the fully-loaded cost lands at about $10,000–$15,000 a month in the US.

Hold that number, because it's the spine of the whole decision.

A stacked bar: visible salary versus the 1.43x fully-loaded cost, with infra, tooling and LLM shown as a thin sliver on top.

The part the spreadsheet forgets: on-call

Here's where the DIY sum quietly falls apart.

A production agent that touches your ATS and sends CVs to clients can't only work during office hours. APIs drift at 2am, models get deprecated on the vendor's schedule, a queue backs up on a Saturday. Someone has to be on the hook when it breaks.

The tooling for this is trivial: PagerDuty is $21–$41 per user per month. The human is not. On-call pay runs $500–$1,200 per engineer per month on top of salary, and here is the load-bearing point: sustainable 24/7 coverage needs roughly four to six engineers so that no one person is permanently tethered to a phone.

A solo DIY build has no sustainable on-call. One engineer covering a production system around the clock isn't a rota; it's a resignation letter waiting to be written. So your real choices are: accept that the system is unmonitored outside working hours (and discover the failures from your client), or staff a rota you were never going to staff for three small agents.

"No sustainable solo on-call" isn't a caveat. It's the whole DIY problem in five words.

And on-call is just the alarm. Behind it sits the work of actually running the system: monitoring dashboards someone has to read, the guardrail gateway someone has to keep current, the evals someone has to run, the API change someone has to absorb when ~15% of library updates break backwards compatibility. None of that is a one-off. It's a standing cost that never ends.

The two columns, side by side

Put the honest version of each option next to the other. We'll model the managed option at ~$2,500 a month, and to be precise about what that number is: it's the midpoint of the prevailing $500–$5,000/month range for SMB managed-automation services (Digital Agency Network 2026; Latenode 2025; Arsum 2025; SalemWise 2025). It is a market assumption you can adopt for the maths, not a price from us.

DIY, run in-houseManaged
Infra (Cloud Run, production)~$120 low / ~$300–$350 typical / $500–$750+ heavyincluded
Observability~$0–$50/moincluded
LLM API~$20–$75 / 1,000 CVs (GPT-5-class); ~$2–$4 (mini)included
On-call tooling$21–$41/user/moincluded
Engineer (fully loaded, BLS 1.43×)~$10,000–$15,000/moincluded
Sustainable on-callneeds ~4–6 engineers; solo = unsustainablecovered by the team
Who reads the dashboards?you, or no onethem

The cheap rows are a rounding error. The expensive row is one fully-loaded engineer, and that single line item is already four to six times a $2,500 managed retainer, before you've solved on-call at all.

Look at where the weight sits. Running it yourself doesn't cost what the infra costs. It costs what a person costs, every month, forever. And one person can't actually cover it.

the-math-no-recruiter-can-win-by-hand
what-an-ai-agent-actually-is
the-leash
the-toolkit
the-model-small-capable-swappable
talking-to-your-ats
use-case-1-resume-screening-against-a-job
the-shape-of-the-loop
running-it-thought-action-observation
use-case-2-cv-formatting-redacting-for-clients
reformatting-into-your-branded-template
resume-shortlisting
that-was-easy
security-compliance
keeping-pii-out-of-the-llm
exceptions-reliability
silent-api-drift-the-ats-changes-under-you
when-it-fails-anyway-dead-letter-and-the-leash
monitoring-observability
maintenance-the-lifecycle
the-scorecard-success-metrics-kpis
build-vs-buy-vs-managed
what-an-engineer-actually-costs
what-the-wider-data-says-happens-next
conclusion-how-this-gets-run-for-you
the-promises-behind-the-service
fuller-code-listings
one-full-screening-react-loop-semantic-kernel
env-deployment-reference
secrets-in-dev-vs-production
bullhorn-jobadder-endpoint-cheat-sheets
sources-further-reading
compliance-primary-law-sources

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