Five engagement models, ranked by 2026 commonality
In rough order of how often we see each work for SMB and mid-market AI buyers:
- Agency project (most common for first AI build)
- Fractional engineer or CTO (most common for ongoing oversight)
- Hybrid: agency builds, in-house maintains (most common at scale)
- Full-time hire (most common when AI is the product)
- Freelance contractor (least common, highest variance)
1. Agency project
You hire a studio to build a defined scope, fixed price or T&M, with a deliverable date.
Cost in 2026: $15K-80K boutique, $80K-300K mid-tier, $300K+ enterprise.
Time-to-start: 1-2 weeks scoping, 2-4 weeks to project kickoff.
Time-to-output: 4-12 weeks for a single agent or automation system.
Best for: First AI build, well-scoped projects, teams that don't want to manage hiring.
Risk: Wrong-fit agency. Mitigate with named-client case studies and a fixed-price option even if you go T&M.
2. Fractional CTO or engineer
A senior person works with you part-time over months. Strategic plus tactical. Common entry path for AI-curious companies that don't need full-time delivery yet.
Cost in 2026: $150-400/hour, 20-60 hours/month, $30K-150K per year typical.
Time-to-start: 1-2 weeks.
Time-to-output: Strategic output day one. Build output depends on hours.
Best for: Seed-stage products, mid-market teams exploring AI strategy, owner-operators who want senior thinking on call.
Risk: Capacity. A fractional engineer with five clients can't respond fast when you need them. Pin down hours upfront and what response time looks like.
3. Hybrid: agency builds, in-house maintains
Agency ships v1 with documentation and training. A single mid-level engineer (or sometimes a senior ops person) maintains it after handover. Most mature teams end up here.
Cost in 2026: $40K-120K build + $80K-130K maintainer year one. Roughly $120K-250K all-in.
Time-to-start: Agency on standard timelines. Maintainer hire happens during build (60-90 days).
Time-to-output: 4-12 weeks for v1. Maintainer takes over month 3 or 4.
Best for: Teams that want speed of agency plus ownership of in-house. Most SMBs that grow into mid-market.
Risk: Bad handover. Mitigate by picking an agency that documents properly and uses a hireable stack.
4. Full-time hire
Direct employee, salary plus equity, owns AI work end-to-end.
Cost in 2026: US senior $260K-380K base. Loaded year-one $400K-550K. EU senior €120K-200K. India senior $80K-160K.
Time-to-start: 60-120 days from posting to start date.
Time-to-output: 60-90 days ramp before first production output. 4-6 months until cruising.
Best for: Companies where AI is the product (not a feature). Continuous build pipelines. Long-term strategic ownership.
Risk: Wrong-fit hire is brutal. Recruiter fees plus ramp time plus opportunity cost can mean a single mis-hire costs $200K+ in real dollars.
5. Freelance contractor
Independent senior, hourly or daily, project-by-project basis.
Cost in 2026: $1,200-2,500 per day for genuinely senior people. Lower-tier marketplaces ($300-800/day) carry serious quality variance.
Time-to-start: 1-3 weeks if you have a network. Months if you don't.
Time-to-output: Day one if scope is clear. Slower if discovery is needed.
Best for: Specific gaps, short engagements, augmenting an existing team.
Risk: Variance. Vet hard. Three reference calls, code samples from production work, and a paid trial week before scoping anything important.
Salary benchmarks (April 2026)
Base salaries from Levels.fyi, Glassdoor, and our hiring conversations across 200+ AI companies in 2025-2026:
| Role | US median | EU median | India median |
|---|---|---|---|
| ML Engineer (mid) | $180K-240K | €80K-120K | $45K-90K |
| ML Engineer (senior) | $260K-380K | €120K-200K | $80K-160K |
| AI Research Engineer | $320K-550K | €140K-260K | $100K-200K |
| AI Product Engineer | $200K-300K | €90K-150K | $60K-120K |
| Fractional CTO (hourly) | $200-400/hr | €150-300/hr | $80-150/hr |
How to evaluate AI developers
The 2026 interview is not the 2022 interview. AI engineers who can't use Claude or GPT productively in their daily work are pretending. The interview should test how well they use AI, not whether they can solve problems without it.
Our standard rubric:
- Production code review. Send a real codebase and ask the candidate to identify three issues. Real production code, not a leetcode problem.
- AI-assisted exercise. Set up a 90-minute build session with internet, AI tools, your stack. Watch how they prompt, iterate, and ship. Pretending not to use AI is a fail.
- Architecture discussion. Ask them to design a system for a real ambiguous problem. Listen for trade-offs, not best-of-everything answers.
- Reference calls. Three. With people who managed them, not friends.
- Paid trial. 1-2 weeks of real work before extending an offer. Cheaper than a wrong-fit hire.
Working with DK Studio
We engage three ways. Fixed-price project for first builds and well-scoped work. Monthly retainer for ongoing automation work or maintenance after a build. Revenue share for select creator and product partnerships where we're betting on the same outcome you are.
We're not the right fit for: enterprise procurement that requires SOC 2 today (we'll get there, not yet), ongoing in-house engineering augmentation (hire someone), or pure consulting (we ship, we don't advise).
We are the right fit for: SMB and agency-tier teams that want senior people on every project, fast iteration, and a hireable stack you can take over after handover.