AI Talent Cost Comparison: US vs India
Almost every published comparison of AI engineering cost between the United States and India compares base salaries, and almost every one is misleading in the same direction. Base salary is a minority of total employment cost in the United States and a larger share of it in India, so a salary comparison systematically overstates the difference.
That does not mean the gap is not real. It is substantial. But the number most companies use in planning is wrong in ways that matter, and the errors run in both directions: some inflate the saving and some ignore costs that erode it.
This guide sets out how to build a comparison that holds up, which costs belong on each side, and the factors that determine the outcome more than the rate difference does.
Key points
- Base salary is a minority of total employment cost in the United States
- Equity is a large component of US AI compensation and is routinely omitted
- The Indian side must include entity, compliance and fixed costs that do not scale down
- Time to hire is a real cost and usually favours India for senior applied roles
- Effective seniority and supervision load matter more than the rate difference
Why salary comparisons mislead
In the United States, base salary is typically well under the full cost of employing someone. Payroll taxes, health insurance, retirement contributions, other benefits and equity all sit on top, and for senior AI roles equity in particular can be a very large share of total compensation.
In India the loading is smaller as a proportion. Statutory contributions, gratuity and insurance add meaningfully to base but usually less than the equivalent US loading, and equity is a smaller share of compensation for most roles outside the funded startup segment.
So a base salary comparison understates the US side more than the Indian side, which makes the true gap in fully loaded cost larger than the salary comparison suggests. This is the one error that runs in favour of the offshore case, and it is worth stating because most of the others run the other way.
What belongs on each side
A like for like comparison needs the same categories on both sides, plus the categories that only exist on one. The table below sets out what a complete model includes.
| Cost component | United States | India | Usually included? |
|---|---|---|---|
| Base salary | Yes | Yes | Always |
| Payroll taxes and statutory | Substantial | Moderate | Sometimes |
| Health and benefits | Substantial | Lower | Sometimes |
| Equity | Often very large for senior AI | Smaller share | Rarely |
| Recruitment cost | High in a saturated market | Moderate | Sometimes |
| Entity and compliance | Existing | New fixed cost | On the India side only |
| Workspace and infrastructure | Often existing | New | On the India side only |
| Onshore management overhead | Minimal | Real and ongoing | Almost never |
| Ramp to productivity | Present | Present, often longer | Rarely |
| Cost of an unfilled role | Very high in saturated markets | Lower | Almost never |
The last row is the largest omitted number in most comparisons. A senior role that stays open for three quarters has a cost, and it is usually larger than the entire salary difference.
The cost of not hiring
This deserves separate treatment because it is the number that most often changes the conclusion and it appears in almost no model.
A senior applied AI role in a saturated market can stay open for two or three quarters. During that time the roadmap does not move, the work is either not done or is done by people who should be doing something else, and the opportunity cost accumulates. That cost is real, it is frequently larger than the annual salary of the role, and it is entirely invisible in a comparison that only looks at what each person costs once hired.
Including it changes the framing. The question stops being whether an Indian engineer costs less than an American one, and becomes whether the capability can be acquired at all within a timeframe that keeps the plan viable.
What erodes the saving
Three things reliably reduce the advantage, and all three are within your control.
Entity, compliance, audit and minimum infrastructure do not scale down. At twenty people they are a substantial share of total cost; at two hundred they disappear into the average.
A team that escalates constantly consumes expensive senior onshore time. This is real capacity, it never appears on an invoice, and it can exceed the salary difference entirely.
Hiring cheaper, less experienced people to maximise the apparent saving produces a team that cannot decide anything, which converts the saving into supervision cost and delay.
Ramp is often slightly longer offshore because context transfer takes longer across a time zone gap. Modelling immediate productivity will produce a first year variance that looks like failure.
What determines the real cost difference between the two options
Illustrative weighting of what drives the outcome rather than measured research. The headline compensation gap is real but it is not usually what decides the comparison.
Building the comparison
Five steps produce a model that will survive its own second year. The discipline is in completeness rather than precision.
Use fully loaded figures on both sides
Include statutory costs, benefits and equity. Comparing US base salary against fully loaded Indian cost is the most common error and it runs against the offshore case.
Model the fixed base separately
Entity, compliance, audit and infrastructure as a distinct line rather than blended into a per head average. Below fifty people this dominates.
Include a ramp curve on both sides
New hires are not productive immediately anywhere. Model three to six months to steady state, slightly longer offshore where context transfer crosses a time zone gap.
Estimate supervision load
Ask how many onshore hours a week the arrangement will consume, and sanity check it against the decision authority the team will have. This is the largest uncounted cost.
Price the unfilled role
Estimate what a role open for two or three quarters costs in delayed roadmap. This is often the number that decides the comparison.
Where the comparison breaks down
For a small number of very senior specialists whose specific experience cannot be sourced elsewhere, cost comparison is the wrong frame entirely. If two or three people in the world have solved your exact problem, you hire them wherever they are and pay what it takes.
The comparison is useful for building applied capability at depth: a team of eight to fifteen that will own systems and operate them for years. That is where the arithmetic matters and where the differences compound.
It is worth being explicit about this because conflating the two produces bad decisions in both directions: paying frontier rates for applied work, or trying to build applied depth by hiring one expensive specialist.
- Compare fully loaded against fully loaded, including equity
- Model the Indian fixed cost base separately from per head cost
- Include ramp on both sides, slightly longer offshore
- Estimate supervision hours and treat them as a real cost
- Price the cost of the role staying unfilled
- Track cost per delivered outcome once running, not only cost per head
The gap is real and it is not the point,Fully loaded, the difference is substantial and larger than salary comparisons suggest. But for most companies the decisive factor is whether the capability can be acquired at all, and that is an access question rather than a cost one.
Frequently asked questions
What is the actual cost difference?
Rather than adopting a published figure, model your own fully loaded costs on both sides, because the answer varies enormously by role, seniority and location within each market. Published comparisons usually compare US base salary against fully loaded Indian cost, which understates the true gap.
Why is equity so important in the comparison?
Because for senior AI roles in the United States it can be a very large share of total compensation, and it is almost always omitted from comparisons. Leaving it out understates the US cost substantially and produces a comparison that is wrong in a direction people do not expect.
Does the saving disappear for a small team?
It shrinks, because entity, compliance and infrastructure costs cannot be spread across a large headcount. It rarely disappears. What matters more is that these costs are modelled explicitly rather than blended into a per head average that hides them.
How long is the ramp difference?
Usually modest, perhaps a few weeks, and it depends far more on whether a senior lead is already in place than on geography. Where context must transfer across a large time zone gap with no local decision authority, the difference can be considerably larger.
What is the biggest hidden cost on the India side?
Onshore management overhead. A team without genuine decision authority escalates constantly, consuming expensive senior time that never appears on any invoice. This is controllable through scope design and is the single largest determinant of whether the saving is real.
Should we hire more junior people to maximise the saving?
No. This is the most common way an offshore AI team fails. Applied AI work is bounded by judgement, and a team without senior depth cannot make the decisions the work requires, which converts the saving into supervision cost, rework and delay.
Is it cheaper to use a vendor than to build an entity?
Over one year usually yes, because there is no fixed base and no ramp to fund. Over three years an owned team is normally cheaper, because there is no vendor margin and context does not have to be relearned after rotation.
What should we measure once the team is running?
Cost per delivered outcome, and onshore hours consumed per unit of output. The first tells you whether the economics are working; the second captures supervision load, effective seniority and rework in a single number that no rate comparison will show you.
Sources & further reading
- NASSCOM — https://nasscom.in/
- Stanford HAI — https://hai.stanford.edu/
- Deloitte — https://www.deloitte.com/
- McKinsey & Company — https://www.mckinsey.com/
Compare on capability, not on salary
Hexominds models the fully loaded cost of an applied AI team in India, including the items a salary comparison leaves out.