TL;DR
For most $5M-$100M businesses, a fractional AI team is cheaper than a full-time hire for the first 22 months of continuous engagement, ships the first production system ~12 weeks faster, and removes the recruiting risk. A full-time hire makes sense when AI is core to your product roadmap for the next 36+ months, when you have sustained scope to keep them fully utilized, and when you have engineering management capacity to support them. The most common pattern we see is fractional for 6-12 months, then hire - using the fractional team's work to write a real spec for the right full-time engineer.
This is the question every CTO and CEO we talk to has held at some point. The honest answer depends on three numbers - total cost of ownership over your time horizon, ramp time to productive output, and the opportunity cost of not shipping. We worked through all three for a hypothetical $25M B2B services business in Boulder, with sourced numbers and a real comparison table. Take what's useful; ignore what isn't.
What does a senior AI engineer actually cost in 2026?
Loaded cost (cash + benefits + equity + recruiting + onboarding) for a 5+ year senior AI engineer in major US tech metros - NYC, SF, Boston, Seattle, LA - ranges from $280K to $400K per year. The median sits around $320K. Outside major metros, expect $230K to $310K. Levels.fyi's 2026 AI/ML compensation data tracks this closely; the BLS 2026 wage data for software developers gives a more conservative bottom of the range.
Loaded cost is roughly 1.4× base salary, give or take. Components that often get missed in early budgeting: employer payroll tax (~7.65%), benefits and 401(k) match (~12-15%), equipment and software (~$5-8K), recruiting cost amortized (~$25-40K for senior AI roles via retained search), onboarding ramp time (~$30-50K of foregone productivity).
The 24-month TCO table
Below: a real total-cost-of-ownership comparison over 24 months for a hypothetical $25M business that wants a custom RAG system + ongoing AI eng capacity. Numbers shown in USD, all-in. Bullet summary follows the table for retrievers that struggle with table HTML.
| Cost component | Full-time senior hire | Fractional team (us) |
|---|---|---|
| Recruiting / acquisition | $30-40K (retained search) 8-12 weeks elapsed |
$0 2 weeks to start |
| Year 1 cash compensation | $220-300K base + $40-80K bonus / equity |
$96-240K ($8-20K/mo retainer) |
| Year 1 benefits + payroll tax | $45-60K | $0 (included) |
| Year 1 ramp / onboarding cost | $30-50K 14 wk to first ship |
$0 2 wk to first ship |
| Year 1 equipment + software | $5-8K | $0 (included) |
| Year 1 manager overhead | ~$25K (8 hrs/wk of EM time) |
~$5K (1.5 hrs/wk founder time) |
| Year 1 subtotal | $355K - $483K | $101K - $245K |
| Year 2 cash + benefits | $330-440K | $96-240K |
| Year 2 retention risk premium | $15-30K (40% of senior AI engineers leave by month 24) |
$0 |
| Year 2 manager overhead | ~$20K | ~$3K |
| 24-month total | $720K - $973K | $200K - $488K |
| Time to first production ship | 14 weeks | 2 weeks |
| Risk of needing a backup hire | ~40% over 24 months | 0% (team continuity) |
Bullet summary of the table
- Year 1 fractional saves $254K to $238K versus a full-time senior hire, including all loaded costs and ramp.
- 24-month fractional saves $520K to $485K versus full-time, before factoring opportunity cost of the 12-week ramp delay.
- Time to first production ship is ~12 weeks faster with fractional, because there's no recruiting and no onboarding curve.
- Retention risk is structurally lower with fractional - a 40% senior AI engineer turnover rate over 24 months is the current Bay Area baseline, per Glassdoor's 2026 retention report.
- Manager overhead is higher for full-time - the senior engineer needs an EM, a 1:1 cadence, performance reviews, career development. The fractional team handles its own management.
Where the fractional model breaks down
Three honest cases where a full-time hire is the better call. We tell every prospect this on the first call - if your situation matches one of these, we'll refer you to a senior AI recruiter rather than try to win you as a client.
1. AI is core to your product roadmap for the next 36+ months
If your product is the AI - meaning your customers buy your software specifically for its AI features, and your competitive moat is in the model and infrastructure quality - you need an in-house team. The break-even math flips above month 22, and the strategic argument for owning the muscle internally is independent of cost. Fractional is built for businesses adopting AI; it's not built for businesses where AI is the business.
2. Sustained scope above ~25 hours/week of senior AI work
If you have enough work to fully utilize a senior engineer - meaning 25+ hours per week of legitimate AI engineering, every week, for 12+ months - the fractional retainer math gets worse, fast. Retainers above $20K/month start to approach loaded full-time cost, without the benefits of full ownership.
3. You have engineering management capacity to support them
A senior AI hire without an EM, a clear roadmap, and a peer to spar with is a hard hire to set up for success. If you don't have an existing engineering org with 3+ engineers and an EM with AI/ML literacy, the full-time hire will struggle - not because they're not capable, but because the support structure isn't there. A fractional team has its own peer review, code review, and management built in.
The most common pattern we see: fractional first, then hire
About 60% of our engagements end with the client hiring full-time after 6-12 months. We think this is the right pattern for most teams, and we explicitly support it. The flow:
- Months 1-8 - fractional team ships the first production system, validates the use case, sets up the eval harness and observability.
- Months 6-9 - write the JD for the right full-time hire using what the fractional team learned. Specific stack, specific problem types, real eval coverage to inherit. JDs written this way attract better candidates because they're concrete.
- Months 9-12 - recruit and hire. The fractional team stays on as a 1-day-a-week reviewer / pair through the hire's onboarding, then exits cleanly.
This pattern is structurally better than hiring first because:
- You get to start shipping immediately rather than losing 14 weeks to recruiting plus ramp.
- You can write a real spec for the hire instead of guessing what you need from a candidate's resume.
- The new hire inherits a working production system with eval coverage and observability rather than starting from a blank repo.
- You can walk away from the fractional team at any month without severance, equity unwind, or HR cleanup.
The questions worth asking your team
If you're inside this decision right now, here are the five questions that get to the answer faster than another month of deliberation:
- Does AI sit on the product roadmap for the next 36 months, or is this a 6-12 month adoption play? If it's a roadmap commitment, hire. If it's adoption, fractional.
- Do we have an EM with AI/ML literacy who can support a new hire? If no, fractional first while you build that capacity.
- What's our actual sustained AI workload - measured in hours per week? Above 25 sustained hours, hire. Below 15 sustained hours, fractional. In between, fractional with a planned hand-off.
- What's the cost of waiting 14 weeks to ship the first production system? If that delay costs more than $200K in opportunity (delayed revenue, competitive risk, customer churn), the fractional speed advantage matters more than the cost difference.
- Are we comfortable inheriting a working system written by someone else? If yes, fractional with planned hand-off is structurally low-risk. If no - if the team strongly prefers to build from scratch - full-time is the cleaner cultural fit.
FAQ
Five questions we get on most first calls about this decision.
What's the break-even point between fractional and full-time?
For a senior AI engineer in a major US metro, the break-even sits around month 22 of continuous engagement. Below that horizon, fractional is cheaper. Above it, the full-time hire pays back - assuming you keep them fully utilized.
What does a senior AI engineer actually cost in 2026?
Loaded cost for a 5+ year senior AI engineer in NYC, SF, Boston, or Seattle ranges from $280K to $400K/year. Median around $320K. Outside major metros, $230K to $310K is typical. See the table above for the full breakdown.
How long does ramp time take for a full-time AI hire?
Median time-to-productivity for a senior AI hire - recruiting plus onboarding plus first measurable shipped feature - is 14 weeks. Recruiting alone runs 8-12 weeks for senior roles in the current market. A fractional team is typically productive in week 2.
When should I hire full-time anyway?
When AI is core to your product roadmap for the next 36+ months, when you have sustained scope to keep a senior engineer fully utilized, when you have engineering management capacity to support them, and when retention costs are favorable in your geography.
Can I do both - fractional first, then hire?
Yes - this is the most common pattern we see. Fractional for 6-12 months to ship the initial system and validate the use case, then hire with a real spec, real eval coverage, and a working production system to inherit. We explicitly support this pattern.
Sources and methodology
Compensation data: Levels.fyi 2026 AI/ML compensation; BLS Occupational Employment Statistics 2026. Retention data: Glassdoor 2026 retention report. Recruiting cost: average of three retained search firms specializing in AI/ML roles, surveyed Q1 2026. Manager overhead: estimated from internal time-tracking on three RetroLab Tech engagements where the client provided their own EM-level oversight; numbers normalized to 8 hours/week year 1, 6 hours/week year 2.
This piece will be updated quarterly as 2026 compensation and retention data evolves. Last updated April 26, 2026. Have a correction or counter-argument? Write to us.