New blogMaking an AI Agent You Can Trust With Money
logo

★ Should this be a person or an agent?

AI Agent vs Hire Calculator

Compare the fully loaded cost of people doing a task with an AI agent doing it, including the human review it still needs, the build cost and the payback period.

  • Runs in your browser
  • No signup

Presets are starting assumptions for a small team, not industry statistics. Change every number to match your reality.

The work today
min
The person doing it
$

Example, replace with yours.

%

Benefits, taxes, equipment, management.

Example, replace with yours.

What the agent takes
%

Measure this on your own tasks before trusting it.

%
min

Prices from the LLM cost calculator, verified 2026-11-07.

All model calls in one task, including retrieved context.

Building and running it
$

Example, replace with your quote.

$

Monitoring, evals, fixes, hosting.

The agent saves

$5,812 / month

Pays back the build cost in 2.6 months.

People only

$11.1K/mo

With agent

$5.3K/mo

Hours freed

152 h/mo

12 months, side by side

Cumulative cost. The agent line starts at the build cost.

$0$72K$144KNowM2M4M6M8M10M12Breaks even · M3
Agent (incl. build) People only
Cumulative cost by month
MonthPeople onlyAgent
0$0$15K
1$11.1K$20.3K
2$22.2K$25.6K
3$33.3K$30.9K
4$44.4K$36.2K
5$55.6K$41.5K
6$66.7K$46.8K
7$77.8K$52.1K
8$88.9K$57.4K
9$100K$62.7K
10$111.1K$68K
11$122.2K$73.3K
12$133.3K$78.6K

Where the agent cost goes

  • Work people still do$4,444
  • Reviewing the agent$333
  • Model usage$22
  • Maintenance$500

Model cost per task: $0.0180 on Claude Sonnet 5.5, for 1,200 agent tasks a month.

An agent that pays back in 2.6 months.

I build production AI agents with human review, guardrails and logging.

Talk agents
01

How the comparison works

The calculator compares two ways of getting the same tasks done each month: people doing all of them, or an agent doing a share while people do the rest and review the agent's work.

hourly cost  = salary × (1 + overhead) ÷ working hours per year
people only  = tasks × minutes ÷ 60 × hourly cost

with agent   = remaining human work + review time + model cost + maintenance
model cost   = agent tasks × (input tokens × input price + output tokens × output price)

savings      = people only − with agent
payback      = build cost ÷ savings (never, if savings ≤ 0)

It only counts the share of someone's time spent on these tasks, so it answers “what does this work cost”, not “can I remove a job”. Model prices come from the LLM API Cost Calculator.

02

Worked example

The support agent preset with the example salary of $60,000 plus 25% overhead, 2,000 tickets a month at 8 minutes each, the agent handling 60% and a person reviewing 20% of those:

Worked example
LinePer month
People only$11,111
Remaining human work$4,444
Reviewing the agent$333
Model usage (Claude Sonnet 5.5)$22
Maintenance$500
With the agent$5,299
Saving$5,812

With a $15,000 example build cost, payback is 2.6 months. Notice how small the model cost is next to the human time that remains: review time and the share the agent can't finish usually decide the answer, not token prices.

03

The costs people forget

Review

Someone has to check the agent's work, at least at first. If review takes as long as doing the task, there is no saving.

Evals

A test set of real tasks with known answers, run on every prompt or model change. Without it, every update is a gamble.

Maintenance

APIs change, models get updated, edge cases appear. Budget monthly time to keep the agent working.

Edge cases

The last 20% of tasks are where agents fail loudly. Decide in advance how they reach a person.

04

When to hire a person instead

  • The volume is low. A few hundred tasks a month rarely justifies building and maintaining an agent.
  • The work is mostly judgement, relationships or negotiation, with little repetition.
  • A single mistake is expensive, legally risky or hard to undo, so review would be close to 100%.
  • The process isn't written down yet. Automating an unclear process makes it fail faster.
  • You need someone who will improve the process, not just run it.
05

Questions founders actually ask

Is an AI agent cheaper than hiring someone?
Sometimes. It depends on volume, how much of the work the agent can actually finish, and how much human review it needs. Low-volume or judgement-heavy work rarely pays back. Put your own numbers in and look at the payback period, not just the monthly saving.
Where do the model prices come from?
From the LLM API Cost Calculator on this site, which lists each provider's published per-token prices with a source link and a last-verified date. Prices change often, so check them before you decide.
Why do the defaults say 'example, replace with yours'?
Salaries, build quotes and automation rates vary enormously by company, country and task. The presets are starting assumptions to show how the maths works, not benchmarks. Your own numbers are the only ones that matter.
How do I find the real share of tasks an agent can handle?
Take a few hundred recent tasks, run a prototype agent on them, and have a person grade the results. The share it gets right without help is your automatable percentage. Anything less rigorous is a guess.
Does this include the cost of building evals and guardrails?
Only if you put it in. Add it to the build cost, and the ongoing part (monitoring, eval runs, prompt fixes) to monthly maintenance. Teams that skip this usually pay for it later in incidents.
06

Compare by role

07

More from the workbench

02 · AI & LLMsLLM API Cost CalculatorYour monthly bill across Claude, GPT, Gemini and open models, side by side.03 · Money & fundraisingRunway & Burn CalculatorMonths of cash left, the date it hits zero, and whether you are default alive.07 · AI & LLMsToken CounterToken counts per model, cost per call and how much of the context window you are using.

Fine print

Tools: on the house.
Engineer: by the hour.

The math was free. A reliable agent takes engineering, which is what I do.

I'm Himanshu, an AI and full-stack engineer. I build MVPs, AI agents and the growth plumbing around them for early-stage startups.

Hire me →My projects