Hiring an in-house AI engineer costs roughly $150,000–220,000 a year fully loaded in the US, or ₹25–60 lakh in India, and takes three to six months to fill and ramp. An agency costs $3,000–25,000 per project with no ramp-up and no hiring risk. Agencies win on the first one to three AI features, where the value is patterns you don't have yet. In-house wins once AI is a permanent part of the roadmap — typically past two ongoing workstreams — because context compounds and salary stops being the dominant cost.
This is written by an agency, so read the last two sections first if you're pressed for time. They're the ones that argue against hiring us.
What an in-house AI engineer actually costs
The salary is the number people quote and about two-thirds of the real one.
| Line item | US / Western Europe | India |
|---|---|---|
| Base salary | $140,000 – $190,000 | ₹18 – 40 lakh |
| Employer taxes, benefits, equipment | +25 – 35% | +10 – 15% |
| Recruitment (agency or internal) | 15 – 25% of first-year salary | 8 – 15% |
| Fully loaded, year one | $190,000 – $260,000 | ₹25 – 60 lakh |
| Ongoing years | $175,000 – $230,000 | ₹22 – 50 lakh |
Then the parts that don't appear on a budget line. Three to six months to hire, because engineers who have actually shipped LLM features to production are genuinely scarce and know it. One to three months to ramp on your codebase. Roughly four hours a week of somebody senior's time managing them. And the risk that the first hire doesn't work out, which costs the whole search again plus the months.
What an agency actually costs
Project pricing for AI integration work at a small studio in 2026, ours included:
| Engagement | Cost | Elapsed time |
|---|---|---|
| Discovery / feasibility | $600 – $1,800 | 3–5 days |
| One feature into an existing product | $3,000 – $8,000 | 3–6 weeks |
| RAG system over one corpus | $4,000 – $10,000 | 4–8 weeks |
| Full AI-first product or MVP | $8,000 – $25,000 | 8–16 weeks |
| Ongoing retainer | $900 – $6,000 / month | Continuous |
No recruitment, no ramp, no notice period, and it stops when you stop. The costs that don't appear here are real too: your team's time explaining the domain, the handover, and the fact that when we leave, the deepest understanding of that system leaves with us unless the documentation was taken seriously.
The crossover
Compare over eighteen months rather than a quarter, because that's the window where hiring costs stop being front-loaded.
| Scenario | In-house | Agency |
|---|---|---|
| One AI feature, then done | ~$290,000 and you're still paying | ~$6,000 |
| Three features over 18 months | ~$290,000 | ~$18,000 + retainer ≈ $63,000 |
| Continuous AI roadmap, 2+ workstreams | ~$290,000 for one engineer | $150,000+ and you're renting context |
The pattern is consistent across every version of this arithmetic we've run: agencies win decisively at low volume and lose at high volume, and the crossover sits somewhere around a permanent two-workstream commitment. Below it you're buying patterns. Above it you're renting an employee at a premium, which is a bad trade for both sides.
The third option most teams should take
Framing it as a binary is the actual mistake. The sequence that works:
- 01Validate with an agency. One feature, six weeks, a real number attached. You find out whether AI moves anything in your product before committing a headcount to the belief that it does.
- 02Insist the handover is real. Documentation, an eval suite your team can run, and a walkthrough with your engineers. This is the deliverable that decides whether step three is cheap or expensive.
- 03Hire once the roadmap is proven. Now you can interview properly, because you have a working system to ask candidates about and someone internal who understands it.
- 04Keep a thin retainer. For model migrations, eval upkeep and the occasional second opinion, while your hire owns the day-to-day.
This is a worse outcome for our revenue than a permanent retainer, and it's what we recommend to most teams who ask.
When you should not hire an agency
Plainly, so you can check yourself against it:
- You already have engineers who've shipped production LLM systems. You're paying a premium for patterns you have. Give them the roadmap space instead — that's cheaper and produces a better system.
- AI is your product, not a feature of it. If the model pipeline *is* the company, that capability cannot live outside it. Build the team.
- Your data can't leave and can't be described. Some regulated environments make external help slower than doing it internally, even badly.
- You need more than ~80 engineering hours a month, indefinitely. That's a full-time job. Retaining it costs more than employing it, and we'll say so on the call.
- The real problem isn't AI. A distressing share of AI enquiries are data problems, process problems or a search box that was never fixed. An agency that takes the brief without checking is selling you something.
When an agency genuinely is the better call
- Nobody internal has done this before. The first production LLM system teaches expensive lessons. Buying them once is cheaper than learning them on your own roadmap.
- You need an answer this quarter. Hiring takes three to six months before anyone writes code. If the window is now, that settles it.
- You're not sure it's worth doing. Six weeks and $6,000 to find out beats a $260,000 annual commitment to the hypothesis.
- The work is spiky. One intense build, then maintenance. That shape fits a project plus a retainer and fits a full-time hire badly.
The honest summary
If you'll build one to three AI features over the next eighteen months, an agency is cheaper by an order of magnitude and faster by a quarter. If AI is becoming permanent infrastructure in your product, hire — and use an agency to de-risk the first build and to train the person you hire. Anyone who tells you the answer is always their own model of working hasn't done the arithmetic, or has and would rather you didn't.
If you want the version of this conversation applied to your actual situation, that's what our scoping calls are for — including the ones that end with us telling you to hire someone.