Build vs Buy: Should You Build Your Own AI Agent or Hire One?
Building your own AI agent trades a vendor’s per-outcome price for your own engineering and maintenance cost — and US BLS OEWS May 2024 puts the median Software Developer (SOC 15-1252) at $133,080 a year, which is the real, loaded cost of the "build" side that most comparisons leave out.
Every founder evaluating AI eventually asks: do we build our own agent or hire one off the shelf? The honest answer is a cost-and-control trade, and most "build is cheaper" math quietly omits the largest line item — the engineers. This post is a decision framework: the real cost of the build side (anchored to public BLS wage data), the real cost of the buy side (a per-outcome price you can benchmark), and the non-cost factors that usually decide it. It cites real wage data and invents no figures.
The build side: the cost everyone underestimates
Building an agent is not a one-time project; it is a product you now own. The dominant cost is engineering time. US Bureau of Labor Statistics Occupational Employment and Wage Statistics (OEWS, May 2024) reports the median annual wage for Software Developers (SOC 15-1252) at $133,080 — about $63.98 an hour at 2,080 hours a year, before the employer-side multiplier of benefits, taxes, tooling, and management. That is the base rate of the people who will build and then keep running the thing.
And running it is the part that surprises teams. Beyond the initial build you carry ongoing maintenance: model upgrades and regressions, prompt and eval drift, monitoring, incident response, and the data/infra bill. A meaningful share of an internal agent’s lifetime cost arrives after launch, which is why "we already have engineers" is not the same as "it is free."
The buy side: a price you can benchmark
Buying converts that engineering and maintenance burden into a per-outcome price — dollars per resolved ticket, per reviewed PR, per processed invoice — that someone else underwrites. The advantage is that this price is comparable and benchmarkable: convert any vendor quote to dollars-per-outcome, read it against the public human-labor baseline for the role (the relevant BLS OEWS median), and, as it publishes, against the WorkForce Labor Index market rate for the category. You trade control for a number you can sanity-check.
Where a vendor publishes its own list price, record it as that vendor’s own published figure and convert it to dollars-per-outcome before comparing — do not generalize it into a market rate.
The honest comparison
Put both sides in the same unit. For build, sum loaded engineering cost (initial plus ongoing maintenance) plus infra and model spend, then divide by the outcomes the agent produces over a realistic horizon — your internal dollars-per-outcome. For buy, take the vendor’s converted dollars-per-outcome. Now they are comparable. Crucially, amortize the build cost over real volume: at low volume, a fixed engineering investment makes home-built outcomes very expensive per unit; at high volume, it can win. There is no universal answer — only your volume, your horizon, and your loaded costs.
The factors that usually decide it
Cost rarely decides alone. Build when the agent is core differentiation, touches proprietary data or workflows you cannot expose, and you have the volume to amortize the investment and the team to maintain it indefinitely. Buy when the task is commodifiable (the same work many companies need), when speed to value matters more than control, and when a vendor can show an independent quality score that beats what you could ship quickly. A third path — buy now to learn the real outcome economics, build later if volume justifies it — is often the lowest-regret move.
Either way, demand a quality number
Whether you build or buy, the deciding comparison is quality-per-dollar, not price alone. A buy option should come with a third-party quality score and a confidence interval; a build option should be held to the same bar via an independent eval, not graded on your own homework. A cheaper agent that produces worse outcomes is not cheaper once rework is counted.
Methodology note
This article cites real BLS OEWS May 2024 figures (SOC 15-1252, median $133,080/yr; ~$63.98/hr is that annual figure divided by 2,080 hours; public domain) for the build-side labor cost. All "dollars-per-outcome" figures are framework arithmetic from your own inputs — not measured WorkForce transaction data — and no AI market rate is stated as a live figure. WLI category rates publish with their confidence intervals and sample sizes once transaction coverage exists; see /methodology for how the index is calculated and governed.
Next steps
Whichever way you lean, get a free, independent quality score for the agent at /eval, and benchmark the per-outcome economics against the live rates at /wli as transaction coverage accrues.