★ SDR · agent or hire?
AI Agent vs Hiring an SDR: Cost Calculator
Sales development is research plus writing plus follow-up, at volume. Agents are good at the research and first drafts. They are bad at sounding human at scale, and a burned domain or a spammy reputation costs more than the SDR you replaced.
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Presets are starting assumptions for a small team, not industry statistics. Change every number to match your reality.
The agent saves
$5,107 / month
Pays back the build cost in 2.9 months.
People only
$12.5K/mo
With agent
$7.4K/mo
Hours freed
135 h/mo
12 months, side by side
Cumulative cost. The agent line starts at the build cost.
| Month | People only | Agent |
|---|---|---|
| 0 | $0 | $15K |
| 1 | $12.5K | $22.4K |
| 2 | $25K | $29.8K |
| 3 | $37.5K | $37.2K |
| 4 | $50K | $44.6K |
| 5 | $62.5K | $52K |
| 6 | $75K | $59.4K |
| 7 | $87.5K | $66.8K |
| 8 | $100K | $74.1K |
| 9 | $112.5K | $81.5K |
| 10 | $125K | $88.9K |
| 11 | $137.5K | $96.3K |
| 12 | $150K | $103.7K |
Where the agent cost goes
- Work people still do$6,250
- Reviewing the agent$625
- Model usage$18
- Maintenance$500
Model cost per task: $0.0240 on Claude Sonnet 5.5, for 750 agent tasks a month.
An agent that pays back in 2.9 months.
I build production AI agents with human review, guardrails and logging.
What the agent can and can't take
Good jobs for the agent
- Research a prospect from public sources and your CRM
- Draft a personalised first email for review
- Classify replies and book follow-ups
- Keep CRM fields up to date
Keep with a person
- Read the room on a live call
- Build a relationship over months
- Decide which accounts are strategically worth chasing
What review looks like: Approve every outbound message before it is sent until reply rates and complaint rates are stable, then move to sampling.
Good fit, bad fit
| Good fit if… | Not a good fit if… |
|---|---|
| A clear ideal customer profile and good data | Enterprise deals driven by relationships |
| Research is the slow part of the job today | No reliable data source for prospects |
| You already have a human closing the deals | You need volume to fix a product problem |
Risks to plan for
- Hurting your sending reputation with high-volume, low-quality email
- Personalisation that is wrong and embarrassing
- Breaking anti-spam rules in the countries you sell into
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.
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.
Worked example
The sdr preset with the example salary of $60,000 plus 25% overhead, 1,500 prospects researched and contacted a month at 12 minutes each, the agent handling 50% and a person reviewing 40% of those:
| Line | Per month |
|---|---|
| People only | $12,500 |
| Remaining human work | $6,250 |
| Reviewing the agent | $625 |
| Model usage (Claude Sonnet 5.5) | $18 |
| Maintenance | $500 |
| With the agent | $7,393 |
| Saving | $5,107 |
With a $15,000 example build cost, payback is 2.9 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.
Questions about this role
Will prospects know the email was written by AI?
Is an AI SDR cheaper than a human SDR?
What metrics show an AI SDR is working?
Is an AI agent cheaper than hiring someone?
Where do the model prices come from?
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More from the workbench
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.